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Statistics Assignment Examples

February 2, 2024

Introduction: Embarking on the intricate journey of statistics assignments can be both challenging and enlightening. To navigate this terrain successfully, gaining a deep understanding of statistical concepts is crucial. In this comprehensive exploration, we’ll delve into the world of statistics assignment examples, unraveling the complexities through real-world scenarios. Whether you’re a student seeking clarity for your assignments or an enthusiast eager to grasp the practical applications of statistics, join us on this illuminating journey with Statistics Homework Tutors. Together, we’ll dissect key statistical concepts using practical examples, providing you with the tools to master this field and transform challenges into triumphs.

The Essence of Descriptive Statistics: Shedding Light on Performance

Example 1: exam scores.

Imagine you’re a student in a class of 30, eagerly awaiting your math exam scores. Descriptive statistics come to the rescue, turning those numbers into meaningful insights:

Average (Mean): Calculating the sum of all scores and dividing by the number of students provides a snapshot of the overall performance.
Middle Value (Median): Arranging scores in ascending order and identifying the middle score offers a representation of central tendency.
Most Frequent Score (Mode): Spotting the score that appears most frequently gives a sense of the prevailing performance level.

This statistical approach transforms a seemingly random set of scores into a clear understanding of the class’s academic standing.

Harnessing the Power of Inferential Statistics: Predicting Outcomes

Example 2: opinion polls.

In the world of politics, predicting election outcomes is a task tailor-made for inferential statistics. Imagine conducting an opinion poll with 1,000 participants to gauge voter preferences:

Hypothesis Testing: Evaluating whether a particular candidate’s popularity significantly differs from others provides insights into voter sentiment.
Confidence Intervals: Estimating the range within which the true proportion of voters supporting a candidate lies adds a layer of certainty to predictions.

Inferential statistics empowers us to draw meaningful conclusions about a larger population based on a representative sample.

Business Optimization through Regression Analysis

Example 3: sales and advertising.

Enter the dynamic world of business, where regression analysis plays a pivotal role in optimizing advertising strategies. Consider a scenario where a company invests in advertising campaigns over several months:

Modeling Relationships: Establishing a mathematical relationship between advertising expenses and sales unveils the dynamics at play.
Predicting Outcomes: Anticipating the expected increase in sales for a given increase in advertising spending guides decision-making.

Regression analysis transforms data into actionable insights, aiding businesses in making informed choices to maximize their impact.

Quantifying Uncertainty with Probability Distributions

Example 4: coin toss.

Dive into the world of probability distributions with a simple coin toss. The unpredictability of this event is quantified through:

Likelihood of Outcomes: A fair coin offers a 50% chance of landing on heads or tails, illustrating the fundamental concept of probability.
Expected Value: The average outcome over many tosses converges to 0.5, showcasing the stability of probability predictions.

Understanding probability distributions is fundamental to making sense of uncertainties and predicting outcomes in various scenarios.

Real-World Impact: Statistics in Healthcare

Example 5: medical diagnosis.

In the critical realm of healthcare, statistics takes center stage in medical diagnosis. Consider a diagnostic test for a rare disease:

Sensitivity and Specificity: Evaluating the accuracy of the test in identifying true positives and true negatives helps in assessing its reliability.
False Positive Rate: Examining the likelihood of a healthy person receiving an incorrect diagnosis adds a layer of caution.

Statistics in healthcare guides medical professionals in interpreting test results, making accurate diagnoses, and ultimately improving patient outcomes.

The Role of Statistics Homework Tutors: Tailored Support for Success

With these examples in mind, it’s crucial to acknowledge the role of Statistics Homework Tutors in enhancing your journey through the statistical landscape. As a student-centric platform, Statistics Homework Tutors provides:

Tailored Support for Students:

Recognizing the unique challenges faced by students, the platform’s approach is tailored to simplify complexities, ensuring a profound understanding of statistical principles.

Expert Tutors in Statistics:

Access to a team of expert tutors with specialized knowledge in statistics guarantees students receive guidance from professionals who excel not only in subject matter expertise but also in effective communication.

Application-Oriented Learning:

Moving beyond theoretical concepts, Statistics Homework Tutors emphasizes practical examples and applications, making statistics more relevant to real-world scenarios.

Timely Assistance:

Acknowledging the time constraints students often encounter, Statistics Homework Tutors provides timely assistance, ensuring that deadlines are met without compromising the quality of students’ work.

The Path to Mastery: Statistics Homework Tutors’ Approach

1. illuminating concepts:.

Statistics Homework Tutors illuminates complex statistical concepts through real-world examples, making them more accessible and applicable.

2. Enhancing Understanding:

By providing tailored support, the platform enhances students’ understanding of statistics, ensuring they grasp both the theory and practical applications.

3. Real-Time Assistance:

Offering real-time assistance, Statistics Homework Tutors ensures that students can navigate challenges and overcome hurdles with expert guidance.

4. Promoting Application:

Encouraging application-oriented learning, the platform equips students with the skills to apply statistical methods to various scenarios.

5. Secure and Confidential:

Prioritizing the security and confidentiality of student information, Statistics Homework Tutors ensures a safe and trustworthy learning environment.

Conclusion: Your Journey to Statistical Mastery

As we conclude this comprehensive exploration of statistics and its real-world applications, it’s evident that mastering this field is not just about crunching numbers but understanding the stories they tell. Statistics Homework Tutors serves as a guiding light on your journey to statistical mastery, offering tailored support, expert guidance, and real-world examples that transform challenges into opportunities for growth.

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Introduction to Statistics

(15 reviews)

David Lane, Rice University

Publisher: David Lane

Language: English

Formats Available

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Learn more about reviews.

Reviewed by Terri Torres, professor, Oregon Institute of Technology on 8/17/23

This author covers all the topics that would be covered in an introductory statistics course plus some. I could imagine using it for two courses at my university, which is on the quarter system. I would rather have the problem of too many topics... read more

Comprehensiveness rating: 5 see less

Content Accuracy rating: 5

Yes, Lane is both thorough and accurate.

Relevance/Longevity rating: 5

What is covered is what is usually covered in an introductory statistics book. The only topic I may, given sufficient time, cover is bootstrapping.

Clarity rating: 5

The book is clear and well-written. For the trickier topics, simulations are included to help with understanding.

Consistency rating: 5

All is organized in a way that is consistent with the previous topic.

Modularity rating: 5

The text is organized in a way that easily enables navigation.

Organization/Structure/Flow rating: 5

The text is organized like most statistics texts.

Interface rating: 5

Easy navigation.

Grammatical Errors rating: 5

I didn't see any grammatical errors.

Cultural Relevance rating: 5

Nothing is included that is culturally insensitive.

The videos that accompany this text are short and easy to watch and understand. Videos should be short enough to teach, but not so long that they are tiresome. This text includes almost everything: videos, simulations, case studies---all nicely organized in one spot. In addition, Lane has promised to send an instructor's manual and slide deck.

Reviewed by Professor Sandberg, Professor, Framingham State University on 6/29/21

This text covers all the usual topics in an Introduction to Statistics for college students. In addition, it has some additional topics that are useful. read more

This text covers all the usual topics in an Introduction to Statistics for college students. In addition, it has some additional topics that are useful.

I did not find any errors.

Some of the examples are dated. And the frequent use of male/female examples need updating in terms of current gender splits.

I found it was easy to read and understand and I expect that students would also find the writing clear and the explanations accessible.

Even with different authors of chapter, the writing is consistent.

The text is well organized into sections making it easy to assign individual topics and sections.

The topics are presented in the usual order. Regression comes later in the text but there is a difference of opinions about whether to present it early with descriptive statistics for bivariate data or later with inferential statistics.

I had no problem navigating the text online.

The writing is grammatical correct.

I saw no issues that would be offensive.

I did like this text. It seems like it would be a good choice for most introductory statistics courses. I liked that the Monty Hall problem was included in the probability section. The author offers to provide an instructor's manual, PowerPoint slides and additional questions. These additional resources are very helpful and not always available with online OER texts.

Reviewed by Emilio Vazquez, Associate Professor, Trine University on 4/23/21

This appears to be an excellent textbook for an Introductory Course in Statistics. It covers subjects in enough depth to fulfill the needs of a beginner in Statistics work yet is not so complex as to be overwhelming. read more

I found no errors in their discussions. Did not work out all of the questions and answers but my sampling did not reveal any errors.

Some of the examples may need updating depending on the times but the examples are still relevant at this time.

This is a Statistics text so a little dry. I found that the derivation of some of the formulas was not explained. However the background is there to allow the instructor to derive these in class if desired.

The text is consistent throughout using the same verbiage in various sections.

The text dose lend itself to reasonable reading assignments. For example the chapter (Chapter 3) on Summarizing Distributions covers Central Tendency and its associated components in an easy 20 pages with Measures of Variability making up most of the rest of the chapter and covering approximately another 20 pages. Exercises are available at the end of each chapter making it easy for the instructor to assign reading and exercises to be discussed in class.

The textbook flows easily from Descriptive to Inferential Statistics with chapters on Sampling and Estimation preceding chapters on hypothesis testing

I had no problems with navigation

All textbooks have a few errors but certainly nothing glaring or making text difficult

I saw no issues and I am part of a cultural minority in the US

Overall I found this to be a excellent in-depth overview of Statistical Theory, Concepts and Analysis. The length of the textbook appears to be more than adequate for a one-semester course in Introduction to Statistics. As I no longer teach a full statistics course but simply a few lectures as part of our Research Curriculum, I am recommending this book to my students as a good reference. Especially as it is available on-line and in Open Access.

Reviewed by Audrey Hickert, Assistant Professor, Southern Illinois University Carbondale on 3/29/21

All of the major topics of an introductory level statistics course for social science are covered. Background areas include levels of measurement and research design basics. Descriptive statistics include all major measures of central tendency and dispersion/variation. Building blocks for inferential statistics include sampling distributions, the standard normal curve (z scores), and hypothesis testing sections. Inferential statistics include how to calculate confidence intervals, as well as conduct tests of one-sample tests of the population mean (Z- and t-tests), two-sample tests of the difference in population means (Z- and t-tests), chi square test of independence, correlation, and regression. Doesn’t include full probability distribution tables (e.g., t or Z), but those can be easily found online in many places.

I did not find any errors or issues of inaccuracy. When a particular method or practice is debated in the field, the authors acknowledge it (and provide citations in some circumstances).

Relevance/Longevity rating: 4

Basic statistics are standard, so the core information will remain relevant in perpetuity. Some of the examples are dated (e.g., salaries from 1999), but not problematic.

Clarity rating: 4

All of the key terms, formulas, and logic for statistical tests are clearly explained. The book sometimes uses different notation than other entry-level books. For example, the variance formula uses "M" for mean, rather than x-bar.

The explanations are consistent and build from and relate to corresponding sections that are listed in each unit.

Modularity is a strength of this text in both the PDF and interactive online format. Students can easily navigate to the necessary sections and each starts with a “Prerequisites” list of other sections in the book for those who need the additional background material. Instructors could easily compile concise sub-sections of the book for readings.

The presentation of topics differs somewhat from the standard introductory social science statistics textbooks I have used before. However, the modularity allows the instructor and student to work through the discrete sections in the desired order.

Interface rating: 4

For the most part the display of all images/charts is good and navigation is straightforward. One concern is that the organization of the Table of Contents does not exactly match the organizational outline at the start of each chapter in the PDF version. For example, sometimes there are more detailed sub-headings at the start of chapter and occasionally slightly different section headings/titles. There are also inconsistencies in section listings at start of chapters vs. start of sub-sections.

The text is easy to read and free from any obvious grammatical errors.

Although some of the examples are outdated, I did not review any that were offensive. One example of an outdated reference is using descriptive data on “Men per 100 Women” in U.S. cities as “useful if we are looking for an opposite-sex partner”.

This is a good introduction level statistics text book if you have a course with students who may be intimated by longer texts with more detailed information. Just the core basics are provided here and it is easy to select the sections you need. It is a good text if you plan to supplement with an array of your own materials (lectures, practice, etc.) that are specifically tailored to your discipline (e.g., criminal justice and criminology). Be advised that some formulas use different notation than other standard texts, so you will need to point that out to students if they differ from your lectures or assessment materials.

Reviewed by Shahar Boneh, Professor, Metropolitan State University of Denver on 3/26/21, updated 4/22/21

The textbook is indeed quite comprehensive. It can accommodate any style of introductory statistics course. read more

The textbook is indeed quite comprehensive. It can accommodate any style of introductory statistics course.

The text seems to be statistically accurate.

It is a little too extensive, which requires instructors to cover it selectively, and has a potential to confuse the students.

It is written clearly.

Consistency rating: 4

The terminology is fairly consistent. There is room for some improvement.

By the nature of the subject, the topics have to be presented in a sequential and coherent order. However, the book breaks things down quite effectively.

Organization/Structure/Flow rating: 3

Some of the topics are interleaved and not presented in the order I would like to cover them.

Good interface.

The grammar is ok.

The book seems to be culturally neutral, and not offensive in any way.

I really liked the simulations that go with the book. Parts of the book are a little too advanced for students who are learning statistics for the first time.

Reviewed by Julie Gray, Adjunct Assistant Professor, University of Texas at Arlington on 2/26/21

The textbook is for beginner-level students. The concept development is appropriate--there is always room to grow to high higher level, but for an introduction, the basics are what is needed. This is a well-thought-through OER textbook project by... read more

I found all the material accurate.

Essentially, statistical concepts at the introductory level are accepted as universal. This suggests that the relevance of this textbook will continue for a long time.

The book is well written for introducing beginners to statistical concepts. The figures, tables, and animated examples reinforce the clarity of the written text.

Yes, the information is consistent; when it is introduced in early chapters it ties in well in later chapters that build on and add more understanding for the topic.

Modularity rating: 4

The book is well-written with attention to modularity where possible. Due to the nature of statistics, that is not always possible. The content is presented in the order that I usually teach these concepts.

The organization of the book is good, I particularly like the sample lecture slide presentations and the problem set with solutions for use in quizzes and exams. These are available by writing to the author. It is wonderful to have access to these helpful resources for instructors to use in preparation.

I did not find any interface issues.

The book is well written. In my reading I did not notice grammatical errors.

For this subject and in the examples given, I did not notice any cultural issues.

For the field of social work where qualitative data is as common as quantitative, the importance of giving students the rationale or the motivation to learn the quantitative side is understated. To use this text as an introductory statistics OER textbook in a social work curriculum, the instructor will want to bring in field-relevant examples to engage and motivate students. The field needs data-driven decision making and evidence-based practices to become more ubiquitous than not. Preparing future social workers by teaching introductory statistics is essential to meet that goal.

Reviewed by Mamata Marme, Assistant Professor, Augustana College on 6/25/19

This textbook offers a fairly comprehensive summary of what should be discussed in an introductory course in Statistics. The statistical literacy exercises are particularly interesting. It would be helpful to have the statistical tables... read more

Comprehensiveness rating: 4 see less

The terminology and notation used in the textbook is pretty standard. The content is accurate.

The statistical literacy example are up to date but will need to be updated fairly regularly to keep the textbook fresh. The applications within the chapter are accessible and can be used fairly easily over a couple of editions.

The textbook does not necessarily explain the derivation of some of the formulae and this will need to be augmented by the instructor in class discussion. What is beneficial is that there are multiple ways that a topic is discussed using graphs, calculations and explanations of the results. Statistics textbooks have to cover a wide variety of topics with a fair amount of depth. To do this concisely is difficult. There is a fine line between being concise and clear, which this textbook does well, and being somewhat dry. It may be up to the instructor to bring case studies into the readings we are going through the topics rather than wait until the end of the chapter.

The textbook uses standard notation and terminology. The heading section of each chapter is closely tied to topics that are covered. The end of chapter problems and the statistical literacy applications are closely tied to the material covered.

The authors have done a good job treating each chapter as if they stand alone. The lack of connection to a past reference may create a sense of disconnect between the topics discussed

The text's "modularity" does make the flow of the material a little disconnected. If would be better if there was accountability of what a student should already have learnt in a different section. The earlier material is easy to find but not consistently referred to in the text.

I had no problem with the interface. The online version is more visually interesting than the pdf version.

I did not see any grammatical errors.

Cultural Relevance rating: 4

I am not sure how to evaluate this. The examples are mostly based on the American experience and the data alluded to mostly domestic. However, I am not sure if that creates a problem in understanding the methodology.

Overall, this textbook will cover most of the topics in a survey of statistics course.

Reviewed by Alexandra Verkhovtseva, Professor, Anoka-Ramsey Community College on 6/3/19

This is a comprehensive enough text, considering that it is not easy to create a comprehensive statistics textbook. It is suitable for an introductory statistics course for non-math majors. It contains twenty-one chapters, covering the wide range... read more

The content is pretty accurate, I did not find any biases or errors.

The book contains fairly recent data presented in the form of exercises, examples and applications. The topics are up-to-date, and appropriate technology is used for examples, applications, and case studies.

The language is simple and clear, which is a good thing, since students are usually scared of this class, and instructors are looking for something to put them at ease. I would, however, try to make it a little more interesting, exciting, or may be even funny.

Consistency is good, the book has a great structure. I like how each chapter has prerequisites and learner outcomes, this gives students a good idea of what to expect. Material in this book is covered in good detail.

The text can be easily divided into sub-sections, some of which can be omitted if needed. The chapter on regression is covered towards the end (chapter 14), but part of it can be covered sooner in the course.

The book contains well organized chapters that makes reading through easy and understandable. The order of chapters and sections is clear and logical.

The online version has many functions and is easy to navigate. This book also comes with a PDF version. There is no distortion of images or charts. The text is clean and clear, the examples provided contain appropriate format of data presentation.

No grammatical errors found.

The text uses simple and clear language, which is helpful for non-native speakers. I would include more culturally-relevant examples and case studies. Overall, good text.

In all, this book is a good learning experience. It contains tools and techniques that free and easy to use and also easy to modify for both, students and instructors. I very much appreciate this opportunity to use this textbook at no cost for our students.

Reviewed by Dabrina Dutcher, Assistant Professor, Bucknell University on 3/4/19

This is a reasonably thorough first-semester statistics book for most classes. It would have worked well for the general statistics courses I have taught in the past but is not as suitable for specialized introductory statistics courses for engineers or business applications. That is OK, they have separate texts for that! The only sections that feel somewhat light in terms of content are the confidence intervals and ANOVA sections. Given that these topics are often sort of crammed in at the end of many introductory classes, that might not be problematic for many instructors. It should also be pointed out that while there are a couple of chapters on probability, this book spends presents most formulas as "black boxes" rather than worry about the derivation or origin of the formulas. The probability sections do not include any significant combinatorics work, which is sometimes included at this level.

I did not find any errors in the formulas presented but I did not work many end-of-chapter problems to gauge the accuracy of their answers.

There isn't much changing in the introductory stats world, so I have no concerns about the book becoming outdated rapidly. The examples and problems still feel relevant and reasonably modern. My only concern is that the statistical tool most often referenced in the book are TI-83/84 type calculators. As students increasingly buy TI-89s or Inspires, these sections of the book may lose relevance faster than other parts.

Solid. The book gives a list of key terms and their definitions at the end of each chapter which is a nice feature. It also has a formula review at the end of each chapter. I can imagine that these are heavily used by students when studying! Formulas are easy to find and read and are well defined. There are a few areas that I might have found frustrating as a student. For example, the explanation for the difference in formulas for a population vs sample standard deviation is quite weak. Again, this is a book that focuses on sort of a "black-box" approach but you may have to supplement such sections for some students.

I did not detect any problems with inconsistent symbol use or switches in terminology.

Modularity rating: 3

This low rating should not be taken as an indicator of an issue with this book but would be true of virtually any statistics book. Different books still use different variable symbols even for basic calculated statistics. So trying to use a chapter of this book without some sort of symbol/variable cheat-sheet would likely be frustrating to the students.

However, I think it would be possible to skip some chapters or use the chapters in a different order without any loss of functionality.

This book uses a very standard order for the material. The chapter on regressions comes later than it does in some texts but it doesn't really matter since that chapter never seems to fit smoothly anywhere.

There are numerous end of chapter problems, some with answers, available in this book. I'm vacillating on whether these problems would be more useful if they were distributed after each relevant section or are better clumped at the end of the whole chapter. That might be a matter of individual preference.

I did not detect any problems.

I found no errors. However, there were several sections where the punctuation seemed non-ideal. This did not affect the over-all useability of the book though

I'm not sure how well this book would work internationally as many of the examples contain domestic (American) references. However, I did not see anything offensive or biased in the book.

Reviewed by Ilgin Sager, Assistant Professor, University of Missouri - St. Louis on 1/14/19

As the title implies, this is a brief introduction textbook. It covers the fundamental of the introductory statistics, however not a comprehensive text on the subject. A teacher can use this book as the sole text of an introductory statistics. The prose format of definitions and theorems make theoretical concepts accessible to non-math major students. The textbook covers all chapters required in this level course.

It is accurate; the subject matter in the examples to be up to date, is timeless and wouldn't need to be revised in future editions; there is no error except a few typographical errors. There are no logic errors or incorrect explanations.

This text will remain up to date for a long time since it has timeless examples and exercises, it wouldn't be outdated. The information is presented clearly with a simple way and the exercises are beneficial to follow the information.

The material is presented in a clear, concise manner. The text is easy readable for the first time statistics student.

The structure of the text is very consistent. Topics are presented with examples, followed by exercises. Problem sets are appropriate for the level of learner.

When the earlier matters need to be referenced, it is easy to find; no trouble reading the book and finding results, it has a consistent scheme. This book is set very well in sections.

The text presents the information in a logical order.

The learner can easily follow up the material; there is no interface problem.

There is no logic errors and incorrect explanations, a few typographical errors is just to be ignored.

Not applicable for this textbook.

Reviewed by Suhwon Lee, Associate Teaching Professor, University of Missouri on 6/19/18

This book is pretty comprehensive for being a brief introductory book. This book covers all necessary content areas for an introduction to Statistics course for non-math majors. The text book provides an effective index, plenty of exercises, review questions, and practice tests. It provides references and case studies. The glossary and index section is very helpful for students and can be used as a great resource.

Content appears to be accurate throughout. Being an introductory book, the book is unbiased and straight to the point. The terminology is standard.

The content in textbook is up to date. It will be very easy to update it or make changes at any point in time because of the well-structured contents in the textbook.

The author does a great job of explaining nearly every new term or concept. The book is easy to follow, clear and concise. The graphics are good to follow. The language in the book is easily understandable. I found most instructions in the book to be very detailed and clear for students to follow.

Overall consistency is good. It is consistent in terms of terminology and framework. The writing is straightforward and standardized throughout the text and it makes reading easier.

The authors do a great job of partitioning the text and labeling sections with appropriate headings. The table of contents is well organized and easily divisible into reading sections and it can be assigned at different points within the course.

Organization/Structure/Flow rating: 4

Overall, the topics are arranged in an order that follows natural progression in a statistics course with some exception. They are addressed logically and given adequate coverage.

The text is free of any issues. There are no navigation problems nor any display issues.

The text contains no grammatical errors.

The text is not culturally insensitive or offensive in any way most of time. Some examples might need to consider citing the sources or use differently to reflect current inclusive teaching strategies.

Overall, it's well-written and good recourse to be an introduction to statistical methods. Some materials may not need to be covered in an one-semester course. Various examples and quizzes can be a great recourse for instructor.

Reviewed by Jenna Kowalski, Mathematics Instructor, Anoka-Ramsey Community College on 3/27/18

The text includes the introductory statistics topics covered in a college-level semester course. An effective index and glossary are included, with functional hyperlinks. read more

The text includes the introductory statistics topics covered in a college-level semester course. An effective index and glossary are included, with functional hyperlinks.

Content Accuracy rating: 3

The content of this text is accurate and error-free, based on a random sampling of various pages throughout the text. Several examples included information without formal citation, leading the reader to potential bias and discrimination. These examples should be corrected to reflect current values of inclusive teaching.

The text contains relevant information that is current and will not become outdated in the near future. The statistical formulas and calculations have been used for centuries. The examples are direct applications of the formulas and accurately assess the conceptual knowledge of the reader.

The text is very clear and direct with the language used. The jargon does require a basic mathematical and/or statistical foundation to interpret, but this foundational requirement should be met with course prerequisites and placement testing. Graphs, tables, and visual displays are clearly labeled.

The terminology and framework of the text is consistent. The hyperlinks are working effectively, and the glossary is valuable. Each chapter contains modules that begin with prerequisite information and upcoming learning objectives for mastery.

The modules are clearly defined and can be used in conjunction with other modules, or individually to exemplify a choice topic. With the prerequisite information stated, the reader understands what prior mathematical understanding is required to successfully use the module.

The topics are presented well, but I recommend placing Sampling Distributions, Advanced Graphs, and Research Design ahead of Probability in the text. I think this rearranged version of the index would better align with current Introductory Statistics texts. The structure is very organized with the prerequisite information stated and upcoming learner outcomes highlighted. Each module is well-defined.

Adding an option of returning to the previous page would be of great value to the reader. While progressing through the text systematically, this is not an issue, but when the reader chooses to skip modules and read select pages then returning to the previous state of information is not easily accessible.

No grammatical errors were found while reviewing select pages of this text at random.

Cultural Relevance rating: 3

Several examples contained data that were not formally cited. These examples need to be corrected to reflect current inclusive teaching strategies. For example, one question stated that “while men are XX times more likely to commit murder than women, …” This data should be cited, otherwise the information can be interpreted as biased and offensive.

An included solutions manual for the exercises would be valuable to educators who choose to use this text.

Reviewed by Zaki Kuruppalil, Associate Professor, Ohio University on 2/1/18

This is a comprehensive book on statistical methods, its settings and most importantly the interpretation of the results. With the advent of computers and software’s, complex statistical analysis can be done very easily. But the challenge is the knowledge of how to set the case, setting parameters (for example confidence intervals) and knowing its implication on the interpretation of the results. If not done properly this could lead to deceptive inferences, inadvertently or purposely. This book does a great job in explaining the above using many examples and real world case studies. If you are looking for a book to learn and apply statistical methods, this is a great one. I think the author could consider revising the title of the book to reflect the above, as it is more than just an introduction to statistics, may be include the word such as practical guide.

The contents of the book seems accurate. Some plots and calculations were randomly selected and checked for accuracy.

The book topics are up to date and in my opinion, will not be obsolete in the near future. I think the smartest thing the author has done is, not tied the book with any particular software such as minitab or spss . No matter what the software is, standard deviation is calculated the same way as it is always. The only noticeable exception in this case was using the Java Applet for calculating Z values in page 261 and in page 416 an excerpt of SPSS analysis is provided for ANOVA calculations.

The contents and examples cited are clear and explained in simple language. Data analysis and presentation of the results including mathematical calculations, graphical explanation using charts, tables, figures etc are presented with clarity.

Terminology is consistant. Framework for each chapter seems consistent with each chapter beginning with a set of defined topics, and each of the topic divided into modules with each module having a set of learning objectives and prerequisite chapters.

The text book is divided into chapters with each chapter further divided into modules. Each of the modules have detailed learning objectives and prerequisite required. So you can extract a portion of the book and use it as a standalone to teach certain topics or as a learning guide to apply a relevant topic.

Presentation of the topics are well thought and are presented in a logical fashion as if it would be introduced to someone who is learning the contents. However, there are some issues with table of contents and page numbers, for example chapter 17 starts in page 597 not 598. Also some tables and figures does not have a number, for instance the graph shown in page 114 does not have a number. Also it would have been better if the chapter number was included in table and figure identification, for example Figure 4-5 . Also in some cases, for instance page 109, the figures and titles are in two different pages.

No major issues. Only suggestion would be, since each chapter has several modules, any means such as a header to trace back where you are currently, would certainly help.

Grammatical Errors rating: 4

Easy to read and phrased correctly in most cases. Minor grammatical errors such as missing prepositions etc. In some cases the author seems to have the habbit of using a period after the decimal. For instance page 464, 467 etc. For X = 1, Y' = (0.425)(1) + 0.785 = 1.21. For X = 2, Y' = (0.425)(2) + 0.785 = 1.64.

However it contains some statements (even though given as examples) that could be perceived as subjective, which the author could consider citing the sources. For example from page 11: Statistics include numerical facts and figures. For instance: • The largest earthquake measured 9.2 on the Richter scale. • Men are at least 10 times more likely than women to commit murder. • One in every 8 South Africans is HIV positive. • By the year 2020, there will be 15 people aged 65 and over for every new baby born.

Solutions for the exercises would be a great teaching resource to have

Reviewed by Randy Vander Wal, Professor, The Pennsylvania State University on 2/1/18

As a text for an introductory course, standard topics are covered. It was nice to see some topics such as power, sampling, research design and distribution free methods covered, as these are often omitted in abbreviated texts. Each module introduces the topic, has appropriate graphics, illustration or worked example(s) as appropriate and concluding with many exercises. An instructor’s manual is available by contacting the author. A comprehensive glossary provides definitions for all the major terms and concepts. The case studies give examples of practical applications of statistical analyses. Many of the case studies contain the actual raw data. To note is that the on-line e-book provides several calculators for the essential distributions and tests. These are provided in lieu of printed tables which are not included in the pdf. (Such tables are readily available on the web.)

The content is accurate and error free. Notation is standard and terminology is used accurately, as are the videos and verbal explanations therein. Online links work properly as do all the calculators. The text appears neutral and unbiased in subject and content.

The text achieves contemporary relevance by ending each section with a Statistical Literacy example, drawn from contemporary headlines and issues. Of course, the core topics are time proven. There is no obvious material that may become “dated”.

The text is very readable. While the pdf text may appear “sparse” by absence varied colored and inset boxes, pictures etc., the essential illustrations and descriptions are provided. Meanwhile for this same content the on-line version appears streamlined, uncluttered, enhancing the value of the active links. Moreover, the videos provide nice short segments of “active” instruction that are clear and concise. Despite being a mathematical text, the text is not overly burdened by formulas and numbers but rather has “readable feel”.

This terminology and symbol use are consistent throughout the text and with common use in the field. The pdf text and online version are also consistent by content, but with the online e-book offering much greater functionality.

The chapters and topics may be used in a selective manner. Certain chapters have no pre-requisite chapter and in all cases, those required are listed at the beginning of each module. It would be straightforward to select portions of the text and reorganize as needed. The online version is highly modular offering students both ease of navigation and selection of topics.

Chapter topics are arranged appropriately. In an introductory statistics course, there is a logical flow given the buildup to the normal distribution, concept of sampling distributions, confidence intervals, hypothesis testing, regression and additional parametric and non-parametric tests. The normal distribution is central to an introductory course. Necessary precursor topics are covered in this text, while its use in significance and hypothesis testing follow, and thereafter more advanced topics, including multi-factor ANOVA.

Each chapter is structured with several modules, each beginning with pre-requisite chapter(s), learning objectives and concluding with Statistical Literacy sections providing a self-check question addressing the core concept, along with answer, followed by an extensive problem set. The clear and concise learning objectives will be of benefit to students and the course instructor. No solutions or answer key is provided to students. An instructor’s manual is available by request.

The on-line interface works well. In fact, I was pleasantly surprised by its options and functionality. The pdf appears somewhat sparse by comparison to publisher texts, lacking pictures, colored boxes, etc. But the on-line version has many active links providing definitions and graphic illustrations for key terms and topics. This can really facilitate learning as making such “refreshers” integral to the new material. Most sections also have short videos that are professionally done, with narration and smooth graphics. In this way, the text is interactive and flexible, offering varied tools for students. To note is that the interactive e-book works for both IOS and OS X.

The text in pdf form appeared to free of grammatical errors, as did the on-line version, text, graphics and videos.

This text contains no culturally insensitive or offensive content. The focus of the text is on concepts and explanation.

The text would be a great resource for students. The full content would be ambitious for a 1-semester course, such use would be unlikely. The text is clearly geared towards students with no statistics background nor calculus. The text could be used in two styles of course. For 1st year students early chapters on graphs and distributions would be the starting point, omitting later chapters on Chi-square, transformations, distribution-free and size effect chapters. Alternatively, for upper level students the introductory chapters could be bypassed with the latter chapters then covered to completion.

This text adopts a descriptive style of presentation with topics well and fully explained, much like the “Dummy series”. For this, it may seem a bit “wordy”, but this can well serve students and notably it complements powerpoint slides that are generally sparse on written content. This text could be used as the primary text, for regular lectures, or as reference for a “flipped” class. The e-book videos are an enabling tool if this approach is adopted.

Reviewed by David jabon, Associate Professor, DePaul University on 8/15/17

This text covers all the standard topics in a semester long introductory course in statistics. It is particularly well indexed and very easy to navigate. There is comprehensive hyperlinked glossary. read more

This text covers all the standard topics in a semester long introductory course in statistics. It is particularly well indexed and very easy to navigate. There is comprehensive hyperlinked glossary.

The material is completely accurate. There are no errors. The terminology is standard with one exception: the book calls what most people call the interquartile range, the H-spread in a number of places. Ideally, the term "interquartile range" would be used in place of every reference to "H-spread." "Interquartile range" is simply a better, more descriptive term of the concept that it describes. It is also more commonly used nowadays.

This book came out a number of years ago, but the material is still up to date. Some more recent case studies have been added.

The writing is very clear. There are also videos for almost every section. The section on boxplots uses a lot of technical terms that I don't find are very helpful for my students (hinge, H-spread, upper adjacent value).

The text is internally consistent with one exception that I noted (the use of the synonymous words "H-spread" and "interquartile range").

The text book is brokenly into very short sections, almost to a fault. Each section is at most two pages long. However at the end of each of these sections there are a few multiple choice questions to test yourself. These questions are a very appealing feature of the text.

The organization, in particular the ordering of the topics, is rather standard with a few exceptions. Boxplots are introduced in Chapter II before the discussion of measures of center and dispersion. Most books introduce them as part of discussion of summaries of data using measure of center and dispersion. Some statistics instructors may not like the way the text lumps all of the sampling distributions in a single chapter (sampling distribution of mean, sampling distribution for the difference of means, sampling distribution of a proportion, sampling distribution of r). I have tried this approach, and I now like this approach. But it is a very challenging chapter for students.

The book's interface has no features that distracted me. Overall the text is very clean and spare, with no additional distracting visual elements.

The book contains no grammatical errors.

The book's cultural relevance comes out in the case studies. As of this writing there are 33 such case studies, and they cover a wide range of issues from health to racial, ethnic, and gender disparity.

Each chapter as a nice set of exercises with selected answers. The thirty three case studies are excellent and can be supplement with some other online case studies. An instructor's manual and PowerPoint slides can be obtained by emailing the author. There are direct links to online simulations within the text. This text is very high quality textbook in every way.

1. Introduction
2. Graphing Distributions
3. Summarizing Distributions
4. Describing Bivariate Data
5. Probability
6. Research Design
7. Normal Distributions
8. Advanced Graphs
9. Sampling Distributions
10. Estimation
11. Logic of Hypothesis Testing
12. Testing Means
14. Regression
15. Analysis of Variance
16. Transformations
17. Chi Square
18. Distribution-Free Tests
19. Effect Size
20. Case Studies
21. Glossary

Ancillary Material

Ancillary materials are available by contacting the author or publisher .

About the Book

Introduction to Statistics is a resource for learning and teaching introductory statistics. This work is in the public domain. Therefore, it can be copied and reproduced without limitation. However, we would appreciate a citation where possible. Please cite as: Online Statistics Education: A Multimedia Course of Study (http://onlinestatbook.com/). Project Leader: David M. Lane, Rice University. Instructor's manual, PowerPoint Slides, and additional questions are available.

About the Contributors

David Lane is an Associate Professor in the Departments of Psychology, Statistics, and Management at the Rice University. Lane is the principal developer of this resource although many others have made substantial contributions. This site was developed at Rice University, University of Houston-Clear Lake, and Tufts University.

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Student Study Guide With IBM® SPSS® Workbook for Research Methods, Statistics, and Applications

Kathrynn A. Adams - Guilford College
Eva K. McGuire - Guilford College
Description

See what’s new to this edition by selecting the Features tab on this page. Should you need additional information or have questions regarding the HEOA information provided for this title, including what is new to this edition, please email [email protected] . Please include your name, contact information, and the name of the title for which you would like more information. For information on the HEOA, please go to http://ed.gov/policy/highered/leg/hea08/index.html .

For assistance with your order: Please email us at [email protected] or connect with your SAGE representative.

SAGE 2455 Teller Road Thousand Oaks, CA 91320 www.sagepub.com

Although in my course I do not use Spss but Stata, I find this Study guide extremely useful in that it provides many different types of exercises and activities tied to each textbook chapter. I will use the Student Guide for proposing weekly activities.

KEY FEATURES:

Learning Objectives are listed in both the textbook and the study guide to help students reorient to the textbook chapter and serve as a reminder to consider what they have learned and what areas require additional review.
Brief Chapter Summaries orient students to the material in the main text.
Practice quizzes help students strengthen their recall of the material and recognize where they may need to go back and reread the text.
Answers to quizzes are provided after the quizzes for immediate feedback.
Short exercises lend themselves to in-class or in-lab activities and discussion, especially when students are required to complete the fill-in-the blank sections prior to class or lab.
The Your Research section has students apply key concepts and skills from the chapter to a research topic of their choice to encourage them to think about and develop their own area of research and promote deeper understanding and integration of the material.
Step-by-step directions for IBM® SPSS® Data Analysis and Interpretation are now included all at the back of the book students gain competence using the program as well as interpreting and writing up results.

We have used the previous version of the study guide in our research methods and analysis course and students reported the exercises to be invaluable. We found students were much more prepared for class and asked better questions as a result of assigning the exercises as homework. Consequently, for this edition, we moved the exercise answer keys to the Instructor Resource site so that professors can decide how and when to share the answers with students. In order to allow students to check their knowledge of key concepts, we added multiple choice quizzes at the beginning of each study guide chapter followed immediately by the answers.

For the third edition, we moved directions for SPSS ® to a separate IBM ® SPSS ® Workbook section. We believe this will make finding the directions for specific analyses easier, and students can easily refer back to the workbook throughout their academic careers and beyond. The workbook organization matches the guidelines on selecting statistical analyses and includes similar flowcharts found in the final, “Putting it all Together” chapter of the textbook. In the workbook, we provide guidelines and examples for writing up results in APA style. Practice exercises are provided to help students gain competence using the program as well as interpreting and writing up results. We have used a version of this IBM ® SPSS ® workbook in our research methods and analysis course for over 15 years, and students tell us that they cannot imagine getting through the course without it. We also created an alternative workbook for jamovi that is available on the instructor resource site.

Sample Materials & Chapters

CHAPTER 1 Thinking Like a Researcher

CHAPTER 2 Build a Solid Foundation for Your Study Based on Past Research

For instructors

Select a purchasing option, related products.

12 Top Applications of Statistics in Daily Life and Industry

In today’s data-driven world, the applications of statistics are vast and varied, touching nearly every aspect of our daily life and industry.

From healthcare and education to business and government, the power of statistical analysis helps in decision-making, predicting future trends, and understanding complex phenomena.

This article delves into the multifaceted applications of statistics, shedding light on how this mathematical discipline shapes our understanding of the world.

Whether you’re a student, professional, or simply a curious mind, join us as we explore the significant impact of statistics across different sectors, illustrating its indispensable role in our daily lives and the advancement of society.

Applications of Statistics - an infographic

Download the above infographic in PDF

1. Healthcare and Medicine

Applications of Statistics in Healthcare and Medicine - an infographic

In the realm of healthcare and medicine, the applications of statistics are both profound and pivotal.

From the development of new drugs to the management of patient care, statistical methods underpin many of the advances in this field.

Let’s dive into how statistics plays a vital role in healthcare and medicine, making complex decisions clearer and treatments more effective.

Clinical Trials

One of the most critical applications of statistics in healthcare is in the design and analysis of clinical trials.

These trials are the backbone of medical research, providing the evidence needed to determine whether new treatments are safe and effective.

Statistical methods are used to design the trial, including determining the sample size needed to detect a treatment effect, if one exists.

They are also used to analyze the results, helping researchers understand whether any differences observed are due to the treatment or occurred by chance.

This rigorous application of statistics ensures that medical practices are based on solid evidence.

Epidemiology

Epidemiology, the study of how diseases spread within populations, relies heavily on statistics.

Statisticians use models to track the progression of diseases, identify risk factors, and evaluate the effectiveness of public health interventions.

During pandemics, such as the COVID-19 crisis, epidemiological statistics become crucial in decision-making processes, guiding public health policies and measures to control the spread.

In genetics, statistics is key to unraveling the complex relationship between genes and traits, including susceptibility to diseases.

Techniques such as genome-wide association studies (GWAS) rely on statistical analysis to identify genetic variants associated with specific conditions.

This research is vital for understanding diseases at a molecular level and developing targeted therapies.

Public Health

Public health officials depend on statistical data to make informed decisions about healthcare policies, resource allocation, and preventive measures.

By analyzing health data, statisticians can identify trends, such as increases in certain diseases, and evaluate the impact of public health interventions.

This can include everything from vaccination programs to education campaigns on healthy living.

The use of statistics in healthcare and medicine is a testament to its value across disciplines.

By providing a framework for making evidence-based decisions, statistics helps improve patient outcomes, advance medical research, and enhance public health initiatives.

For anyone interested in understanding the applications of statistics in real-world scenarios, healthcare and medicine offer compelling examples of its critical role in advancing human health.

2. Business and Economics

Applications of Statistics in Business and Economics - an infographic

The role of statistics in business and economics cannot be overstated.

It serves as the backbone for decision-making, strategy development, and forecasting, enabling businesses to navigate the complexities of the market with data-driven insights.

Let’s explore how statistics fuels the engines of business and economics, making it indispensable in the corporate world.

Market Research

In the competitive arena of business, understanding the market and consumer behavior is crucial.

Statistics come into play through market research, where data collection and analysis provide insights into consumer preferences, buying habits, and trends .

This information helps businesses tailor their products, services, and marketing strategies to meet the needs of their target audience, ensuring they stay ahead of the competition.

Statistical analysis of market research data can reveal segments of the population that are more likely to purchase certain products, enabling companies to focus their efforts more effectively.

Quality Control

Maintaining high-quality products and services is essential for any business’s success.

Statistics plays a pivotal role in quality control processes, utilizing methods such as statistical process control (SPC) to monitor and control the quality of manufacturing and production processes.

By analyzing data from these processes, businesses can detect any deviations from the standard quality and take corrective actions promptly.

This not only ensures the consistency and reliability of the products but also reduces waste and improves efficiency.

Risk Management

In the world of finance and investment, risk management is a key concern.

Statistical models are used to assess and quantify the financial risks associated with investment decisions.

By analyzing historical data, statisticians can predict the likelihood of various outcomes, helping businesses and investors make informed decisions about where to allocate their resources.

This application of statistics is crucial for minimizing potential losses and maximizing returns in the volatile world of finance.

Forecasting

Predicting future market trends, economic conditions, and consumer behavior is another area where statistics shine.

Through forecasting models, businesses can anticipate changes in the market, adjust their strategies accordingly, and seize opportunities for growth.

This forward-looking approach, grounded in statistical analysis, is essential for staying competitive in a rapidly changing economic landscape.

Statistics in business and economics is about more than just numbers; it’s a powerful tool for understanding the world and making informed decisions.

From optimizing product lines to navigating financial risks, the applications of statistics are integral to the success and sustainability of businesses and economies worldwide.

3. Engineering

Engineering is a field where precision, efficiency, and innovation are paramount.

Statistics plays a critical role in all aspects of engineering, from the design phase through to testing and quality assurance.

Let’s explore the integral role that statistics plays in engineering, showcasing its importance in problem-solving and decision-making processes.

Product Design and Development

In the initial stages of product design and development, engineers face the challenge of creating products that meet specific criteria for performance, reliability, and cost-effectiveness.

Statistical methods, such as Design of Experiments (DOE) , are employed to systematically investigate the relationship between design parameters and the performance of a product.

By analyzing these relationships, engineers can identify the optimal design settings that meet the desired specifications with minimal experimentation, saving time and resources.

Reliability Engineering

Statistics is fundamental to reliability engineering, where the focus is on ensuring that products perform without failure over a specified period under stated conditions.

Statistical models help in estimating a product’s lifespan, predicting failure rates, and identifying factors that contribute to potential failures.

This information is crucial for engineers to design more reliable products, plan maintenance schedules, and assess the need for warranties or recalls.

The application of statistics in reliability engineering enhances consumer trust and satisfaction by ensuring product durability and safety.

Process Optimization

Efficiency in manufacturing and production processes is vital for the economic success of engineering projects.

Statistical Process Control (SPC) and other statistical techniques are used to monitor, control, and optimize manufacturing processes.

By analyzing process data, engineers can identify sources of variability that affect product quality and take corrective actions to eliminate waste and reduce production costs.

This continuous improvement of processes ensures that products meet quality standards consistently, enhancing productivity and profitability.

The application of statistics in engineering is a testament to its versatility and power as a tool for innovation and improvement.

By providing a framework for making data-driven decisions, statistics helps engineers solve complex problems, design better products, and optimize processes.

Whether you’re an engineer, a student of engineering, or simply someone fascinated by how things are made, the role of statistics in engineering offers a compelling glimpse into the precision and ingenuity behind the products and technologies we rely on every day.

4. Government and Public Policy

Statistics play a pivotal role in shaping government policies and decision-making processes.

Through the collection, analysis, and interpretation of data, governments can better understand the needs of their populations, allocate resources efficiently, and evaluate the impact of their policies.

Census Data Analysis

The census is a fundamental statistical exercise conducted by governments worldwide.

By collecting detailed information on the population’s demographic, social, and economic characteristics, the census provides a comprehensive snapshot of the nation at a specific point in time.

This data is crucial for policy formulation, planning, and resource allocation.

For instance, census data helps in determining the distribution of government funds, planning for educational needs, healthcare facilities, and infrastructure development.

It also plays a role in political representation, as census results are often used to draw electoral district boundaries.

Environmental Policy

Statistics are indispensable in the development and evaluation of environmental policies.

By analyzing data on air and water quality, waste management, and the impact of human activities on natural resources, governments can make informed decisions about conservation efforts and regulatory measures.

Statistical models are used to predict the effects of climate change, assess the risk of natural disasters, and evaluate the effectiveness of environmental policies.

This data-driven approach enables governments to protect natural resources, mitigate environmental risks, and promote sustainable development.

Crime Statistics

Understanding crime patterns and trends is essential for effective law enforcement and public safety policies.

Through the analysis of crime statistics, governments can identify areas with high crime rates, types of crimes that are increasing, and demographic factors associated with criminal behavior.

This information is used to allocate law enforcement resources, develop crime prevention strategies, and evaluate the effectiveness of public safety initiatives.

By basing these decisions on statistical evidence, governments can more effectively protect their citizens and reduce crime rates.

The applications of statistics in government and public policy highlight the importance of data in making informed decisions that affect the lives of millions.

Applications of Statistics in Finance - an infographic

In the dynamic world of finance, the application of statistics is crucial for making informed decisions, managing risks, and predicting market trends.

From individual investors to multinational corporations, the ability to analyze and interpret financial data through statistical methods underpins success in this sector.

Investment Analysis

Investment analysis relies heavily on statistics to evaluate the performance of stocks, bonds, and other financial instruments.

By analyzing historical data, statisticians can identify patterns and trends that help predict future performance.

Key statistical measures such as mean returns, variance, and standard deviation provide insights into the expected return and risk associated with different investments.

This analysis helps investors make informed decisions, balancing potential rewards against the risk of loss.

Credit Scoring

Credit scoring is another critical application of statistics in finance, affecting both individuals and businesses.

Financial institutions use statistical models to assess the creditworthiness of applicants based on their credit history, income, employment status, and other factors.

These models predict the likelihood of default, enabling lenders to make data-driven decisions on loan approvals and interest rates.

This not only helps manage the lender’s risk but also ensures that loans are offered to those most likely to repay them, maintaining financial stability.

Market Risk Management

Market risk management is essential for protecting assets against the volatility of financial markets.

Statistical models are used to quantify the risk associated with different investment portfolios, considering factors such as market trends, interest rates, and currency exchange rates.

Techniques such as Value at Risk (VaR) provide a quantitative measure of the potential loss in value of a portfolio over a specified time period, under normal market conditions.

This information is vital for financial institutions and investors to mitigate risk and protect their investments.

The application of statistics in finance is a testament to its critical role in navigating the complexities of the financial world.

By providing the tools to analyze data, predict trends, and manage risk, statistics helps ensure that financial decisions are based on solid evidence rather than intuition.

6. Education

The field of education benefits greatly from the application of statistics, providing educators, policymakers, and students with insights that help improve teaching methodologies, learning outcomes, and policy decisions.

Educational Research

Educational research relies heavily on statistical methods to explore a wide range of topics, from learning styles and teaching methods to the impact of technology in the classroom.

By analyzing data collected through surveys, tests, and observational studies, researchers can identify trends, correlations, and causal relationships that inform educational theory and practice.

This research helps in developing effective teaching strategies, designing curricula that cater to diverse learning needs, and understanding the factors that influence student achievement and engagement.

Policy Evaluation

Statistics are crucial for evaluating the impact of educational policies and programs.

Policymakers use statistical analysis to assess the effectiveness of initiatives such as literacy campaigns, STEM education programs, and school funding models.

By examining data on student performance, graduation rates, and other key indicators, they can determine whether policies are achieving their intended outcomes or if adjustments are needed.

This evidence-based approach ensures that educational resources are allocated efficiently and that policies contribute positively to student learning and achievement.

Standardized Testing

Standardized testing is another area where statistics play a vital role. These tests are designed to assess student achievement and compare educational outcomes across different populations and regions.

Statistical methods are used to ensure the reliability and validity of test scores, enabling educators to make fair comparisons and identify areas for improvement.

Additionally, the analysis of standardized test data helps in identifying achievement gaps, informing targeted interventions to support underperforming student groups.

The application of statistics in education highlights its value in fostering an environment of continuous improvement and innovation.

By providing a framework for analyzing educational data, statistics supports the development of policies and practices that enhance teaching effectiveness, improve student outcomes, and ensure equitable access to quality education.

7. Science and Technology

The influence of statistics in science and technology is immense, offering a foundation for discovery, innovation, and validation across various fields.

From decoding the mysteries of the universe in astronomy to advancing the frontiers of artificial intelligence, statistical methods are integral to analyzing data, testing hypotheses, and drawing conclusions.

In the vast expanse of astronomy, statistics is a key tool for making sense of the data collected from telescopes, satellites, and space missions.

Astronomers use statistical techniques to analyze the light from stars and galaxies, determining their composition, distance, and motion.

This analysis helps in understanding the structure of the universe, the life cycle of stars, and the distribution of galaxies.

Statistical methods also play a crucial role in the search for exoplanets and the study of cosmic phenomena, enabling scientists to uncover the secrets of the cosmos from vast datasets.

Climate Science

Climate science relies heavily on statistics to model and predict changes in the Earth’s climate system.

By analyzing historical climate data, scientists use statistical models to understand patterns of temperature, precipitation, and extreme weather events.

These models are essential for predicting the impacts of climate change, informing policy decisions on mitigation and adaptation strategies.

Statistics also help in understanding the variability and uncertainty associated with climate models, providing a clearer picture of the potential risks and challenges posed by global warming.

Machine Learning and Artificial Intelligence

The fields of machine learning and artificial intelligence (AI) are fundamentally based on statistical analysis and inference.

Statistical methods are used to develop algorithms that can learn from and make predictions on data.

This includes everything from natural language processing and image recognition to predictive analytics and autonomous systems.

By applying statistical models to large datasets, AI technologies can uncover patterns and insights that would be impossible for humans to discern, driving innovation in healthcare, finance, manufacturing, and beyond.

The applications of statistics in science and technology underscore its critical role in expanding our knowledge and capabilities.

8. Social Sciences

Applications of Statistics in Social Sciences - an infographic

The social sciences encompass a broad range of disciplines that study human society and social relationships.

In fields such as sociology, psychology, and political science, statistics play a crucial role in understanding complex social phenomena.

By applying statistical methods to social science research, scholars can uncover patterns, test theories, and contribute valuable insights into human behavior and societal structures.

Sociology utilizes statistics to analyze societal trends and the behavior of groups within society.

Through surveys, census data, and observational studies, sociologists gather data on aspects such as family dynamics, social inequality, education, and crime.

Statistical analysis of this data helps in identifying social patterns, understanding the effects of social policies, and exploring the relationship between different social factors.

For example, regression analyses can reveal the impact of educational attainment on income levels, while longitudinal studies track changes in social attitudes over time.

This evidence-based approach enables sociologists to contribute to policy debates and societal development with concrete data.

In psychology, statistics are fundamental to both experimental and clinical research.

Psychological studies often involve measuring behaviors, cognitive processes, and emotional responses, with statistical methods used to analyze the resulting data.

This analysis can help determine whether observed effects are significant and not just due to chance.

For instance, statistical tests can validate hypotheses about the effectiveness of therapeutic interventions or the impact of environmental factors on mental health.

By applying statistical analysis, psychologists can refine their theories and improve mental health treatments, enhancing well-being and understanding of the human mind.

Political Science

Statistics in political science are essential for analyzing voter behavior, public opinion, and policy impact.

Polling data, election results, and legislative studies provide a wealth of information for statistical analysis.

Techniques such as logistic regression can predict voting patterns, while time-series analysis examines the effects of political events on public opinion over time.

This quantitative approach allows political scientists to test theories of political behavior, evaluate the effectiveness of governance, and contribute to the development of democratic practices.

The applications of statistics in the social sciences demonstrate its indispensable role in dissecting the complexities of human society.

The world of sports is increasingly data-driven, with statistics playing a crucial role in analyzing performance, strategizing, and enhancing the overall experience for athletes, teams, and fans alike.

Performance Analysis

In sports, every point, every play, and every movement can be quantified, providing a wealth of data for analysis.

Statistics are used to evaluate the performance of athletes, identifying strengths, weaknesses, and areas for improvement.

Metrics such as scoring averages, completion rates, and defensive statistics offer insights into player effectiveness and contribution to the team.

Coaches and analysts use this data to make informed decisions on training regimens, game strategies, and player selection, ultimately aiming to enhance individual and team performance.

Injury Prediction and Prevention

Statistics also play a crucial role in understanding and preventing injuries in sports.

By analyzing injury data and player statistics, medical teams can identify patterns and risk factors associated with certain injuries.

This information leads to the development of targeted training and conditioning programs designed to minimize the risk of injury.

Additionally, wearables and tracking technologies generate real-time data on player biomechanics, further aiding in injury prevention efforts by monitoring fatigue levels and stress on particular body parts.

The application of statistics in sports extends beyond just enhancing performance and preventing injuries.

It has revolutionized fan engagement, with sports analytics providing fans with deeper insights into the game, player statistics, and predictive outcomes.

This has enriched the viewing experience, making sports more accessible and enjoyable for fans around the world.

10. Technology and Cybersecurity

Applications of Statistics in IT and Cybersecurity - an infographic

In the rapidly evolving domains of information technology (IT) and cybersecurity, statistics serve as a crucial tool for analyzing data, enhancing system performance, and fortifying security measures.

With the digital universe expanding at an unprecedented rate, the role of statistics in managing, understanding, and securing this vast amount of data cannot be overstated.

Network Traffic Analysis

The analysis of network traffic is fundamental for maintaining and optimizing the performance of IT infrastructures.

Statistical methods are employed to monitor data flow across networks, identifying patterns that indicate normal usage versus potential threats or bottlenecks.

By analyzing these patterns, IT professionals can ensure network resources are allocated efficiently, enhance system performance, and detect anomalies that could signify cybersecurity threats.

This proactive approach to network management relies on the statistical analysis of vast datasets, highlighting the importance of statistics in sustaining the backbone of our digital world.

Software Testing

In software development, ensuring the reliability and functionality of applications is paramount.

Statistics play a key role in software testing, where data-driven approaches are used to determine the extent of testing required and to identify potential bugs or vulnerabilities.

Through techniques such as A/B testing and error analysis, developers can statistically assess the performance of software under various conditions, ensuring that applications meet quality standards before release.

This not only enhances user experience but also minimizes the risk of software failures that could lead to data loss or security breaches.

User Behavior Analytics

Understanding user behavior is critical for both optimizing system performance and enhancing cybersecurity.

Statistical analysis of user activity data helps IT professionals identify patterns and trends that can inform system improvements, personalize user experiences, and detect irregular activities indicative of security threats.

In cybersecurity, this analysis is crucial for identifying potential insider threats, phishing attempts, and other sophisticated attacks that might not be detected by traditional security measures.

By leveraging statistics, organizations can create more secure and user-friendly IT environments.

The application of statistics in information technology and cybersecurity underscores its value in navigating the complexities of the digital landscape.

11. Environmental Science

Environmental science is a critical field where statistics plays a vital role in understanding and addressing the challenges facing our planet.

From monitoring pollution levels to assessing the impact of climate change, statistical analysis provides the tools needed to make informed decisions about environmental protection and conservation.

Wildlife Conservation

In wildlife conservation, statistics are used to estimate population sizes, monitor species diversity, and assess the effectiveness of conservation strategies.

Through methods such as mark-recapture studies and population viability analyses, conservationists can determine the status of endangered species and the threats they face.

This information is crucial for developing targeted conservation programs, managing habitats, and ensuring the survival of species at risk.

Statistical models also help in predicting the impacts of human activities and environmental changes on wildlife, guiding efforts to mitigate these effects.

Pollution Monitoring and Control

Monitoring and controlling pollution is another area where statistics are indispensable.

By analyzing data from air and water quality tests, environmental scientists can identify sources of pollution, track trends over time, and assess the effectiveness of regulations aimed at reducing pollution.

Statistical analysis helps in understanding the distribution and concentration of pollutants, evaluating health risks, and developing strategies to protect public health and the environment.

This evidence-based approach ensures that policies and interventions are targeted and effective, minimizing the impact of pollution on ecosystems and human populations.

Climate Modeling

Climate modeling relies heavily on statistical methods to predict future climate patterns and assess the potential impacts of climate change.

Through the analysis of historical climate data and the use of complex statistical models, scientists can make predictions about temperature changes, sea-level rise, and the frequency of extreme weather events.

These models are essential for understanding the long-term effects of climate change, informing policy decisions, and planning for mitigation and adaptation strategies.

Statistics also play a role in evaluating the uncertainty associated with these predictions, providing a range of possible outcomes based on different scenarios of greenhouse gas emissions.

The application of statistics in environmental science highlights its critical role in addressing some of the most pressing issues of our time. B

12. Transportation and Logistics

Data Analysis in Transportation and Logistics

The transportation and logistics sector is the backbone of global commerce, relying heavily on efficiency, reliability, and optimization to move goods and people across the world.

Statistics plays a critical role in achieving these goals, providing the data-driven insights needed to streamline operations, enhance safety, and reduce costs.

Traffic Flow Analysis

Understanding and managing traffic flow is crucial for minimizing congestion, reducing travel times, and enhancing road safety.

Statistical analysis of traffic data helps urban planners and transportation engineers identify patterns and bottlenecks in road networks.

By analyzing vehicle counts, speeds, and types, alongside other variables like time of day and weather conditions, they can make informed decisions on road design, traffic signal timings, and congestion management strategies.

These insights lead to improved traffic flow, reduced emissions, and a better overall travel experience for commuters.

Supply Chain Management

In the realm of supply chain management, statistics are used to optimize the movement of goods from suppliers to consumers.

Analyzing data on inventory levels, demand forecasts, lead times, and transportation costs enables companies to make informed decisions on procurement, production scheduling, and distribution strategies.

Statistical models help in identifying the most efficient routes and modes of transportation, minimizing inventory levels while ensuring timely delivery of products.

This not only reduces costs but also improves customer satisfaction by ensuring that products are available when and where they’re needed.

Route Optimization

Route optimization is another area where statistics significantly impact transportation and logistics.

By applying statistical algorithms to data on distances, travel times, vehicle capacities, and delivery windows, companies can determine the most efficient routes for their fleets.

This not only reduces fuel consumption and wear and tear on vehicles but also maximizes the number of deliveries that can be made in a given period.

In an era where e-commerce is booming, efficient route optimization is key to meeting the growing demand for quick and reliable deliveries.

The applications of statistics in transportation and logistics showcase its critical role in enhancing operational efficiency, improving safety, and reducing environmental impacts.

By leveraging data-driven insights, companies and governments can make informed decisions that optimize the movement of goods and people, ensuring the smooth functioning of global commerce and transportation networks.

In conclusion, the applications of statistics are not only widespread but also crucial in driving progress and innovation in various fields.

As we have seen, statistics play a pivotal role in healthcare, education, business, government, and many other areas, providing the tools needed to analyze data, make informed decisions, and predict future trends.

The power of statistical analysis lies in its ability to turn raw data into meaningful insights, thereby enhancing our understanding of the world around us.

As we continue to generate and collect data at an unprecedented rate, the importance of statistics is only set to increase, highlighting the need for statistical literacy and skilled professionals in this field.

Embracing the applications of statistics is essential for solving complex problems and creating a better future for all.

Dive deeper into data science with our posts:

20 Key Types of Robots
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Generative AI: Applications, Examples, Meaning

About The Author

Silvia Valcheva

Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc.

Apply Now: IRSA Postdoctoral Fellowship

The School of Statistics and the Institute for Research in Statistics and Its Applications (IRSA) at the University of Minnesota invites applications for the IRSA Faragher Distinguished Postdoctoral Fellowship . This endowed fellowship seeks to bring exceptional scientists of outstanding potential to the university. This two year fellowship, with no teaching or service obligations, is to begin on August 28, 2023. The Fellow will hold the rank of postdoctoral research scientist. A competitive annual salary will be supplemented with generous funding for relocation, conference travel, and research support.

Applications in all areas of statistics or related data sciences will be considered: the primary selection criterion will be the candidates’ exceptional promise to produce high quality and visible research. Fellows will be expected to pursue a vigorous research agenda and to participate actively in the intellectual life of the school.

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See full job description for internal candidates .

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The University of Minnesota shall provide equal access to and opportunity in its programs, facilities, and employment without regard to race, color, creed, religion, national origin, gender, age, marital status, disability, public assistance status, veteran status, sexual orientation, gender identity, or gender expression.

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Zombie colleges? These universities are living another life online, and no one can say why

Stratford University says it will prepare students to “Be the Boss.” But applicants hoping a Stratford education will ensure that future are headed for disappointment: The Virginia school closed two years ago this fall.

Instead, Stratford is one of at least nine shuttered colleges whose names have been resurrected on the web. None of these zombie universities are accredited or cleared to receive federal financial aid – hurdles that signal legitimacy. And their motives are cloaked in a mystery no federal oversight agency seems to have tried to solve.

Kari Kammel, who heads the Center for Anti-Counterfeiting and Product Protection at Michigan State University, was perplexed by the counterfeit sites. At USA TODAY’s request, she reviewed them and said she suspected identity theft could be at play for some.

“They figured out a way to post something, get students to apply, take application information, take credit card information, take financial aid information,” Kammel said.

Know more about zombie colleges? Tell us about it. We won't use your information without first discussing it with you.

Some of the imposter websites are tied to colleges that shut down long ago, like Morrison University in Nevada, which closed its doors in 2014 , or Jones International, one of the first schools to offer college courses online, which followed in 2015. Others focused on institutions whose demise came recently, including the private Catholic Marymount California University, which closed nearly two years ago.

Brian W.G. Marcotte, the real Marymount California’s final president, was troubled to learn of the new website from USA TODAY. He also was confused: Why would anyone spend so much time and energy creating a replica of the school? With other former staffers, Marcotte sent a cease-and-desist letter sent to the site’s domain registrar, Namecheap Inc., and posted a warning on the actual Marymount website.

“We take it pretty personally that somebody is misrepresenting what we worked so hard for 54 years to create,” Marcotte said. “And to have somebody twisting the reality of where we are right now is very troublesome to us.”

The warning was posted on April 29, and by May 6, the imposter Marymount California website had been taken down. It was one of several changes that came after USA TODAY began asking questions.

But whoever is behind the websites will have an ever-growing pool of schools to emulate as more universities close. And regulators have had mixed success in addressing counterfeit school websites.

King’s College in Charlotte, which closed in 2018, attracted the attention of the North Carolina attorney general in December 2022, according to a local television station. More than a year later, that office is, “working with tech companies to limit the reach of these fraudsters to help prevent others from falling victim to the scam,” according to Olivia Weidie, deputy press secretary, who would not divulge precisely what the website was accused of doing.

That imposter website remains online.

The Federal Trade Commission declined to say whether the websites even fall under its purview. In 2018, the agency fined operators of what appeared to be military recruiting websites, including one at army.com. But those sites were actually mining marketing leads for colleges. The fine, later suspended, was about $12 million, and the operators were required to surrender the domain names for the imposter sites.

The Education Department confirmed that the nine schools identified by USA TODAY were closed and that it didn’t approve the accreditor some of the universities claim. But the agency said it does not oversee unrecognized accrediting agencies or schools that are ineligible for federal aid.

Though a web of connections links many of the closed universities, a nagging question remains: Where is the spider?

Administrators listed on the schools’ websites have almost no digital footprint. Phone numbers lead to prerecorded messages that in turn lead nowhere. Google Street View shows listed addresses often are nondescript office buildings instead of campuses.

The Stratford address, now in Kentucky, is a law office. When asked if it was home to a university, a receptionist there said no.

Online, Stratford provides information about its business, computer science, law and other degrees but posts no list of professors who teach advertised courses or schedule of when classes start. Are they offered online or in person? Does the school admit people on a rolling basis or do new students have to wait until the fall semester to start?

With no indication of whom to ask, I decided to apply to Stratford. I sent in an essay and the $75 application fee. Then, like aspiring university students across the country, all I could do was wait.

Nonexistent students, barely there administrators and a made-up bakery

Former Stratford University students provided the tip about the school’s resurrection because they were worried. The Education Department typically cancels federal financial aid debt for students if their university closes unexpectedly, as happened with Stratford. Some of the students haven’t gotten their debt forgiven yet, they said, and they feared it was because the college appeared to remain viable.

A quick look suggested something was amiss.

When the original Stratford was still open, its homepage featured a smiling dark-haired woman in a pink cardigan alongside the motto “Changing Lives… One Student at a Time.” The new Stratford website features the same woman and motto. The two websites also share a blue-and-white color scheme.

From there, the replica version quickly veers into some unusual new territory. The school’s past president was Richard R. Shurtz, who didn’t return calls or requests for comment. The leader of the replicated website is described as a headmaster, a title seldom used in America. His name? Jonathan Doe.

Tracing titles, backgrounds and images leads down some winding paths.

A reverse image search showed Jonathan Doe’s headshot is in high demand across the internet. He is identified as a speaker at a Norwegian church, the author of a book on currency trading, a dental office patient.

That's because his image is for sale on Adobe’s marketplace, described as a “studio portrait of a confident businessman.”

A review of the school’s “Board of Trusties (sic)” does reference a university president, Silas Hawthorne, and a chief academic officer, Theron Beaumont. Neither could be located in real life despite their unusual names. A blog post says Thaddeus Ellington, the dean of students, used to teach at California Marymount University, but there’s no record of Ellington serving at that college – or any other.

Student profiles also appear to have been fabricated. Some undergrads are described as having played sports for the university, a difficult feat since the original Stratford hadn’t fielded teams for years. Rachel Stevens’ biography mentions her work at her family’s business, the Sweet Moments Bakery in Fairfax, Virginia. There’s no indication such a bakery ever existed.

Stevens’ student profile also is posted on a website called Hozpitality.com – with a “z.” That site is described as a “Social Media and Networking platform focussed (sic) on the Hospitality Industry,” with offices in Canada, Dubai and India.

Another post on that site gives Stratford University a backstory, acknowledging in broken English that it closed two years ago but is now on the upswing.

“Despite early successes, SU encountered challenges leading to a temporary closure in Fall 22,” the post reads in part. “After 46 years of providing unwavering global service and high-quality education. Declining enrollment and financial restructuring needs prompted a difficult decision.”

Hozpitality’s website features posts about eight other closed universities that suggest they remain open. As with Stratford, some address their closure, saying they reopened thanks to an influx of dollars. For three – Colorado Heights, Jones International and Urbana University – the posts say an “EEI investment” made their reopening possible. They don’t specify what EEI is or why it’s interested in funding closed American colleges.

As I continued my reporting, the application pages for several colleges vanished. The replica website for Nyack College also came down after I reached out to Christian & Missionary Alliance, the group that had been affiliated with the former small Christian school. Hannah Castro, a spokeswoman for the group, said that the organization wasn’t aware of the website but that its legal counsel was “handling the situation.”

The state of Colorado was listed on websites as having approved four of the colleges. Megan McDermott, spokeswoman for the state Department of Higher Education, told me that was not the case. Her agency referred the cases to the Colorado Attorney General’s Office.

Anyone with pocket change can look like a university online

Uncovering who is behind these websites proves to be a tall order. There are no restrictions on who can register sites through .college, .university, .education or .org web addresses − the domain endings used by the counterfeit universities.

They’re also cheap to register. A .education domain name, for example, costs only about $20 for a year via Google. That means anyone can throw up a website that seems like a place offering higher education.

The domain names do set them apart, because most American universities use .edu for their websites. That ending is generally reserved for institutions approved by an accreditor – quasi-governmental bodies certified by the federal government to ensure colleges meet a minimum academic threshold.

None of the zombie universities have been approved by recognized accreditors. A few do claim to be accredited by organizations like Gulf HEC or the United States Learning Commission – none recognized by the federal government.

Though .edu domains require institutions to report who is behind them in public registries, other domain endings do not. What was available for the zombie schools was only the country where the web addresses are or were registered – the U.S., Australia, Iceland and Panama – and how much they cost.

Law enforcement agencies and internet regulators can get access to private domain information. In some cases, they can even force bad actors to take down websites.

That is how Somsak Jinaphan and his company, IDigitalAsset LLC, got caught. They had been tied to several cases of cybersquatting, which involves creating a web address nearly identical to an established business or brand, then offering turn it over or take it down – for a price.

In the mid-2010s, Jinaphan’s company registered a knockoff version of the private liberal arts school Davidson College, under the web address Davidson.College. The college uses Davidson.edu, and it wanted that fake address taken down.

Davidson College’s board of trustees filed a complaint under the Internet Corporation for Assigned Names and Numbers’ domain dispute policy. That body oversees web addresses worldwide. In the complaint, the trustees accused Jinaphan of using the counterfeit site to host ads and had put the knockoff url up for sale.

An independent panelist agreed with the school and required Jinaphan to turn the address over to the college.

Back in 2017, Jinaphan also was listed as the owner of the domain stratford.university, according to the domain registry lookup, WHOXY. Jinaphan didn’t respond to USA TODAY’s request for comment, and the website’s current owner has opted to withhold their name.

Martin Calihan, who was the last president of King’s College in Charlotte, faced a situation similar to Davidson’s. He told an NBC affiliate in 2022 that he had spent more than six months trying to get the fake website taken down. The University of North Carolina system in 2023 even sent a cease-and-desist letter. Calihan told the trade publication Inside Higher Ed that he also had set up a website warning of the scam.

Despite those efforts, the copycat version of the King’s College website remains live. That’s likely, Kammel said, because the actual university is closed. Only the owner of a trademark can sue to enforce it, she said, and it’s impossible for a closed business to exercise that right.

Still, Kammel had thought the media attention back then would have caused the website to go dark. Given that it’s still up, she wondered where the money was going for it and the other sites.

I had the same question. I hoped following my application fee to Stratford might begin to offer some clues.

What are these universities? How are they connected?

It had been more than a decade since I applied to any college, but luckily the Stratford application was straightforward. In addition to the essay, it required standard materials such as academic transcripts and proof of English proficiency for international students.

It also asked for a copy of a driver’s license or passport – and the corresponding numbers.

I wanted to see how deep this rabbit hole went, but I wanted to hold on to my identity, so I skipped the field for driver’s license number and uploaded a redacted version of my ID.

For the application fee, I shielded my credit by setting up a temporary card that still allowed me to track transactions. The location to send the fee to was identified only as “Wire Now.” Once I did, I was charged a foreign transaction fee – another sign pointing to offshore operators.

A few minutes later, a confirmation arrived in my inbox.

“Taking the first step is often the most difficult. We congratulate you on that!” the message read. “With the submission of your application, it will take between 3-6 weeks before we return with a decision as we screen through each applicant carefully.”

There was no way to reply to the email, no one named in it and no contact information other than a generic email already present on the university’s website.

I chose Stratford because I had written about its closure, but the process would probably have been the same for any of the other resurrected universities. Every one of their applications asks nearly identical questions: What are your goals in life? Why do you want to attend this university? And what do you hope to achieve with a university education?

They all also request similar identifying information, charge a $75 application fee and warn applicants that their personal information will be stored.

Similarities don’t end with the application forms, either. Jones International and Stratford, for example, use identical language to describe their law programs.

“The Law Center at Stratford University is ranked as one of the top two law schools in the country in offering energy-specific courses, especially those in oil and gas,” reads the Stratford description. Jones International simply subs in “Jones International.”

Perhaps the strongest tie is that someone set up limited liability companies in California under the names of the closed institutions. Some of the companies list EEI Education as their manager, although it’s unclear whether that is the same EEI referenced in the Hozpitality postings. Those companies also share common addresses and business officers, including CEO Teoh Tong Wah and Roy Virgen Jr.

Wah doesn’t have much of a web presence that I could locate. His listed address in Singapore is a massive shopping complex. Virgen would be a different story.

Who raised these universities from the dead?

Virgen is in the business of advising and launching new colleges through his company, American Education Inc. He is listed as the CEO of at least two universities, which include American Management University and Southern Valley University.

Online, Virgen appears to lead a full life. In addition to his role as CEO of several colleges – usually a full-time job – he is identified as an adjunct lecturer at several schools, including two University of California campuses. He also had several acting and producing credits for movies, including “Amityville Shark House” and “Bermuda Island.”

I reached out to the Gmail associated with his IMDB account but did not expect a response. Within hours, he called to explain.

The movies, he said, were just for fun. He said his company often helps set up new universities, especially in California. He added that he had let colleges use his name as the leader of their institutions but stopped after some institutions did so without permission.

Asked about the resurrected colleges, Virgen offered contradictory snippets of information. He said in a phone call that a Singapore group had approached him about relaunching the Morrison brand. The group told him it had acquired the university’s name and wanted to bring it back as an accredited college. But in a later email, he said “Morrison University” had sought consultation from his group in 2021, nearly seven years after the school had closed.

On the phone call, he said he worked to set up an LLC under the Morrison University name. The plan was to restart the college in California and offer courses to students from certain professional organizations. That, he said, would allow the school to sidestep the normal bureaucracy associated with opening a new college.

Ultimately, Virgen said the state of California denied the application, which he attributed to news coverage of Morrison’s closure. He noted that his group would sometimes get “strange phone calls” from prospective students with questions about the university’s address, which appears to be an office building in Pasadena, California.

Virgen said he offered to set up a new university under a different name, but the Singapore group was not interested. So he cut ties, he said, and “that’s where I left it at.”

In an initial conversation, Virgen didn’t mention the other LLCs that have his name or his company’s mailing address. Asked via email why many of the closed schools had his company’s street address as their mailing address, he didn’t directly respond.

“We will take more time in vetting future clients as well as letting them know not to use our address on their websites so that our information is not copied by others,” he wrote. “Also, we are looking for ways to get illegitimate sites to stop using our programmatic information or implying that I or my business are consulting with them.”

Virgen was also listed as the CEO of Florida Grace College, a website that was taken down after I asked Virgen about it. He later said in the email that it was a school that hadn’t “got off the ground and so we let the website lapse.”

I asked for the name of his client in Singapore, but he didn’t provide it. He also did not respond when I asked whether he had helped set up California LLCs named after Jones International, Kings College, Southern Vermont College or Urbana University.

Though it was clear these universities were trying to present a distorted reality, Virgen got me no closer to knowing why. It had been two weeks and I still hadn’t heard back from Stratford, either, so it was time for me to try another school.

How many fake colleges are there online?

The Ministry of Higher Education Commission describes itself as a college accreditor, and Jones International, Southern Vermont and Urbana University claim it approved their operations. It’s not recognized by the federal government, but a review of the ministry’s membership rolls online revealed a whole new world of misrepresentations and fake colleges.

The ministry claims to have approved real universities such as Carnegie Mellon and the University of Phoenix – which actually are accredited by federally recognized agencies.

It also claims dozens of clearly fictitious schools, like “University of Wayne State Pennsylvania.” That school’s website appears to be an amalgamation of Wayne State University, a public school in Detroit, and Pennsylvania State University. “New York Central University” evokes New York University, with a logo reminiscent of the iconic NYU torch.

Not listed on the site are the three who claimed it: Jones International, Southern Vermont or Urbana University. Stratford is listed on the ministry’s website, but the ministry isn’t listed on Stratford’s site.

The set of universities that appear on the ministry website were more direct about the information they want from applicants. Questions about what I would do with a university education were replaced by questions about how well-connected I am. One asked whether I was a business owner, a “high profile corporate,” or an option that reads: “I belong to Royal Family.”

I opted for the student option at the City University of Michigan but couldn’t find an actual application form. I closed the window thinking that was the end of it only to receive a call later that same day from Gary Rice, who said he was from the City University of Michigan. I made it clear that I am a reporter, but that did not deter him.

Rice asked what I had studied as an undergrad – creative writing. He then suggested I would be a perfect match for a master’s degree in … creative writing.

Like the first batch of schools, City University of Michigan wanted a record of my past studies and my driver’s license. It also wanted a copy of my passport and didn’t offer alternatives when I explained that I am one of the roughly 50% of Americans who don’t have one.

Rice, who said he was a professor in charge of talking to potential students, said that once I submitted that information I could start my studies right away − no application fee required.

After we hung up, I looked up the college’s address. Google Street View turned up an empty parking structure,

There was another suitor in the wings, though. True to their word, the folks behind Stratford got back to me in about three weeks in an email that landed at 10 p.m. East Coast time. It shared “exciting news.” I had been accepted. It would take me only a year to earn my undergraduate degree at a monthly cost of $2,530.

Nothing on this journey could be simple, though. I had applied to Stratford but the email announcing my acceptance came from Vanguard College, with a web address that corresponded with a Singapore web address.

Vanguard’s website offers little about itself. It does include links to pay for degrees through other colleges, specifically the Swiss School of Management and the International American University, which is accredited in the U.S.

Serena Magnanti, the Swiss School of Management’s vice president, told me it had partnered with Vanguard College “solely for recruitment purposes.”

“Regarding the links on Vanguard College’s website, while we were not previously aware of the details, we are actively investigating the process and purpose behind them,” she wrote. “Please rest assured that we are fully committed to looking into this matter and ensuring transparency.”

The International American University didn’t respond to questions, but a staff member did cc me when they forwarded my email to the university’s president, Ryan Doan.

“Hi RD, FYI,” the message read.

The Vanguard website also features student testimonials via a YouTube video in which they seem to be referencing working with a college coaching company, not an actual college. The acceptance email implored me to reach out if I needed help. So I responded: “I thought I had applied to Stratford University, but I see you are emailing me from an address tied to Vanguard College? Can you explain?”

No response, but days later, I received another email from a Stratford University address from someone new, who urged me to check my email for my acceptance letter.

“I did receive a message from someone saying I was accepted to the university, but it came from Vanguard College,” I wrote on April 29. “Can you explain the connection there? I thought I had applied to Stratford?”

Again, crickets. But by April 30, I had a different kind of answer: The “apply now” button had disappeared from Stratford’s website and the web address for the application page returned a 404 error.

Much like the actual Stratford, the imposter seems to be closed for business − at least for now.

Chris Quintana is a reporter on the USA TODAY investigations team with a background in higher education and student loans. Contact him at [email protected] , @CquintanaDC on Instagram and X, or by Signal at 202-308-9021.

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These days, the availability of electronics is certainly a major factor in the modern development of statistics. 3. Importance of Statistics. Statistics is a crucial process behind how we make discoveries in science, make decisions based on data, and make predictions. Statistics allows us to understand a subject much more deeply.
List of fields of application of statistics
Forensic statistics is the application of probability models and statistical techniques to scientific evidence, such as DNA evidence, and the law. In contrast to "everyday" statistics, to not engender bias or unduly draw conclusions, forensic statisticians report likelihoods as likelihood ratios (LR). Geostatistics is a branch of geography that ...
2022 Congressional District Assignment Methodology
2022 Congressional District Assignment Methodology. The census of agriculture, conducted every five years, is the leading source of facts and statistics about the Nation's farms and ranches and the only source of uniform, comprehensive agriculture data for every State and county, or county equivalent, in the U.S.
Apply Now: IRSA Postdoctoral Fellowship
Applications in all areas of statistics or related data sciences will be considered: the primary selection criterion will be the candidates' exceptional promise to produce high quality and visible research. Fellows will be expected to pursue a vigorous research agenda and to participate actively in the intellectual life of the school.
Junior officers, apply to study abroad with no payback assignment
Applications are due by May 20, 2024, via Direct Access and must be command endorsed. The selection panel will select three to five finalists, then ask for more materials to submit a final application package to the Olmsted Scholar Program Manager, Mr. Jessie Phillips, by July 22, 2024.
PDF Volunteer Enrollment Application
110 Volunteer Application Checklist Application Volunteer Record Check 2 Completed Volunteer Personal Reference Questionnaires Return the completed documents to your Regional Coordinator prior to date of training. You may keep copies if you desire. If you have any questions, do not hesitate to contact the Talent Management office at (954) 847-8139.
Closed colleges are rising from the dead online, taking applications
For the application fee, I shielded my credit by setting up a temporary card that still allowed me to track transactions. The location to send the fee to was identified only as "Wire Now ...
Assignments
Statistics for Applications . Menu. More Info Syllabus Lecture Slides Lecture Videos Assignments Assignments. pdf. 132 kB 18.650 (F16) Problem Set 1 ... assignment Problem Sets. Download Course. Over 2,500 courses & materials Freely sharing knowledge with learners and educators around the world.
BJS FY24 State Justice Statistics Program for Statistical Analysis
BJS seeks to fund applications from state Statistical Analysis Centers under the fiscal year 2024 State Justice Statistics program. The program supports the collection, analysis, and dissemination of statistical information on crime and criminal justice at the state and local levels.
Statistics for Applications
Course Description. This course is a broad treatment of statistics, concentrating on specific statistical techniques used in science and industry. Topics include: hypothesis testing and estimation, confidence intervals, chi-square tests, nonparametric statistics, analysis of variance, regression, correlation, decision theory, and Bayesian ….
How many people do we grant protection to?
There were 78,768 asylum applications (relating to 97,390 people) in the UK in the year ending June 2023. This is 19% more applications than in the year ending June 2022 (66,384 applications ...
Assignments
Statistics for Applications . Menu. More Info Syllabus Lecture Slides Lecture Videos Assignments Assignments. There are 11 problem sets. No solutions are available. ... assignment Problem Sets. Download Course. Over 2,500 courses & materials Freely sharing knowledge with learners and educators around the world.