statistics coursework help

Teach yourself statistics

Statistics and Probability

This website provides training and tools to help you solve statistics problems quickly, easily, and accurately - without having to ask anyone for help.

Online Tutorials

Learn at your own pace. Free online tutorials cover statistics, probability, regression, analysis of variance, survey sampling, and matrix algebra - all explained in plain English.

• Advanced Placement (AP) Statistics . Full coverage of the AP Statistics curriculum.
• Probability . Fundamentals of probability. Clear explanations with pages of solved problems.
• Linear Regression . Regression analysis with one or more independent variables.
• ANOVA . Analysis of variance made easy. How to collect, analyze, and interpret data.
• Survey Sampling . How to conduct a statistical survey and analyze survey data.
• Matrix Algebra . Easy-to-understand introduction to matrix algebra.

Practice and review questions reinforce key points. Online calculators take the drudgery out of computation. Perfect for self-study.

AP Statistics

Here is your blueprint for test success on the AP Statistics exam.

• AP Tutorial : Study our free, AP statistics tutorial to improve your skills in all test areas.
• Practice exam : Test your understanding of key topics, through sample problems with detailed solutions.

Be prepared. Get the score that you want on the AP Statistics test.

Random Number Generator

Produce a list of random numbers, based on your specifications.

• Control list size (generate up to 10,000 random numbers).
• Specify the range of values that appear in your list.
• Permit or prevent duplicate entries.

Free and easy to use.

Sample Size Calculator

Create powerful, cost-effective survey sampling plans.

• Find the optimum design (most precision, least cost).
• See how sample size affects cost and precision.
• Compare different survey sampling methods.
• Assess statistical power and Type II errors.

Tailor your sampling plan to your research needs.

Stat Toolbox

Check out our statistical tables and online calculators - fast, accurate, and user-friendly.

Discrete probability distributions

• Hypergeometric
• Multinomial
• Negative binomial
• Poisson distribution

Continuous probability distributions

• f-Distribution
• Normal distribution
• t-Distribution

Special-purpose calculators

• Bayes Rule Calculator
• Combination-Permutation
• Event Counter
• Factorial Calculator
• Bartlett's Test Calculator
• Statistics Calculator
• Probability Calculator

Each calculator features clear instructions, answers to frequently-asked questions, and a one or more problems with solutions to illustrate calculator use.

Introduction to Statistics

(15 reviews)

David Lane, Rice University

Copyright Year: 2003

Publisher: David Lane

Language: English

Formats Available

Conditions of use.

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

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 rather than too few.

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

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.

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... read more

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

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 Dr. Lane and colleagues. It is obvious that several iterations have only made it better.

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

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 attached in the same package, even though they are available online.

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

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 of intro stats topics (and some more), plus the case studies and the glossary.

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... read more

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.... read more

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,... read more

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... read more

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... read more

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.

Table of Contents

• 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.

Contribute to this Page

If you're seeing this message, it means we're having trouble loading external resources on our website.

If you're behind a web filter, please make sure that the domains *.kastatic.org and *.kasandbox.org are unblocked.

To log in and use all the features of Khan Academy, please enable JavaScript in your browser.

High school statistics

Unit 1: displaying a single quantitative variable, unit 2: analyzing a single quantitative variable, unit 3: two-way tables, unit 4: scatterplots, unit 5: study design, unit 6: probability, unit 7: probability distributions & expected value.

Grow with Sophia this spring. Save 20% on your first month with code SPRING2024.

Offer is valid through May 24. The discount provides 20% off the first month. After the first month, you will be charged our normal $99 per month membership subscription fee, unless canceled. 

• Introduction to Statistics
• All courses
• Math courses

Introduction to Statistics reviews

Ground yourself in the basics of statistics and complete a college-level course in the process. Feel confident knowing Sophia’s smart, intuitive platform and learning coaches are here to support you every step of the way.

62642 students successfully completed

61 partners accept credit transfer.* See Partners

3.0 semester credits

Recommended for credit to the ACE® and DEAC college and university networks.

Download Syllabus

Fill in the form to recieve a syllabus for your course..

By providing your information, you consent to receive occasional special promotional offers and education opportunities by email from Sophia Learning or one of its affiliates.

Learning outcomes

Partners who accept this course.

This course transfers into a degree program at these featured institutions

Assignments & grading

This is a pass/fail course. You must complete 20 Challenges (formative assessments) and 6 Milestones (summative assessments) with an overall score of 70% or better.

• No credit card required!
• Sophia membership starts with a risk-free trial

* All fields are required.

Inside the Sophia courseroom

Knock out your general education requirements on your terms. Sophia courses are available anytime, anywhere, and most can be accessed from any device.

A revolutionary way to satisfy general education requirements for your degree. On demand. ACE ®-recommended. Low cost.

Sophia's Learning Coaches are here for you by phone, email or online chat from the start of your course to ordering your final transcript.

How many courses can I take with the free trial?

Access the course content through the first Challenge of any of Sophia’s 50+ courses.

How do I confirm transfer credit with my school?

Check Sophia’s list of partner schools to see if your school is on the list. If not, contact your registrar to learn about your school’s transfer credit policy and if Sophia coursework can be submitted for transfer.

Can I take courses for credit if I’m not enrolled at a school?

Sophia course completions do not expire and will be available to submit for transfer when you’re ready. At that time, check with your school’s registrar for their credit transfer policies.

What happens to the coursework I’ve already completed once I become a Sophia member?

If you’ve completed the first Challenge during your free trial, you can pick up right where you left off after you become a Sophia member.

What happens to my coursework if I cancel my Sophia membership?

Don’t worry. Your completed courses won’t disappear if you cancel your membership. Those courses will be there for you when you’re ready to submit for transfer.

Will my HR benefits cover the cost of Sophia?

If you have an education benefit through your employer, it may cover your subscription to Sophia. Check with your benefits administrator to find out if you qualify.

• Higher education partnerships
• Corporate partnerships
• Social impact partnerships
• Privacy policy
• Cookie policy
• Terms of use

Statistics Courses

• Social Sciences

Data Science: Inference and Modeling

Learn inference and modeling: two of the most widely used statistical tools in data analysis.

Data Science: Probability

Learn probability theory — essential for a data scientist — using a case study on the financial crisis of 2007–2008.

Case Studies in Functional Genomics

Perform RNA-Seq, ChIP-Seq, and DNA methylation data analyses, using open source software, including R and Bioconductor.

Introduction to Bioconductor

The structure, annotation, normalization, and interpretation of genome scale assays.

Advanced Bioconductor

Learn advanced approaches to genomic visualization, reproducible analysis, data architecture, and exploration of cloud-scale consortium-generated genomic data.

High-Dimensional Data Analysis

A focus on several techniques that are widely used in the analysis of high-dimensional data.

Statistical Inference and Modeling for High-throughput Experiments

A focus on the techniques commonly used to perform statistical inference on high throughput data.

Introduction to Linear Models and Matrix Algebra

Learn to use R programming to apply linear models to analyze data in life sciences.

Principles, Statistical and Computational Tools for Reproducible Data Science

Learn skills and tools that support data science and reproducible research, to ensure you can trust your own research results, reproduce them yourself, and communicate them to others.

Fat Chance: Probability from the Ground Up

Increase your quantitative reasoning skills through a deeper understanding of probability and statistics.

Introduction

Chapter objectives.

By the end of this chapter, the student should be able to:

• Recognize and differentiate between key terms.
• Apply various types of sampling methods to data collection.
• Create and interpret frequency tables.

You are probably asking yourself the question, "When and where will I use statistics?" If you read any newspaper, watch television, or use the Internet, you will see statistical information. There are statistics about crime, sports, education, politics, and real estate. Typically, when you read a newspaper article or watch a television news program, you are given sample information. With this information, you may make a decision about the correctness of a statement, claim, or "fact." Statistical methods can help you make the "best educated guess."

Since you will undoubtedly be given statistical information at some point in your life, you need to know some techniques for analyzing the information thoughtfully. Think about buying a house or managing a budget. Think about your chosen profession. The fields of economics, business, psychology, education, biology, law, computer science, police science, and early childhood development require at least one course in statistics.

Included in this chapter are the basic ideas and words of probability and statistics. You will soon understand that statistics and probability work together. You will also learn how data are gathered and what "good" data can be distinguished from "bad."

As an Amazon Associate we earn from qualifying purchases.

This book may not be used in the training of large language models or otherwise be ingested into large language models or generative AI offerings without OpenStax's permission.

Want to cite, share, or modify this book? This book uses the Creative Commons Attribution License and you must attribute OpenStax.

Access for free at https://openstax.org/books/introductory-statistics/pages/1-introduction

• Authors: Barbara Illowsky, Susan Dean
• Publisher/website: OpenStax
• Book title: Introductory Statistics
• Publication date: Sep 19, 2013
• Location: Houston, Texas
• Book URL: https://openstax.org/books/introductory-statistics/pages/1-introduction
• Section URL: https://openstax.org/books/introductory-statistics/pages/1-introduction

© Jun 23, 2022 OpenStax. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution License . The OpenStax name, OpenStax logo, OpenStax book covers, OpenStax CNX name, and OpenStax CNX logo are not subject to the Creative Commons license and may not be reproduced without the prior and express written consent of Rice University.

How to write an introduction to statistics coursework

Statistics Coursework is written for something that you have to research and write on, or an statistical experiment you tried. It is a part of dissertation program in several universities. Just remember, this is essential to your GPA, so put in your 100%.

Most people writing a course work forget that the introduction is the face of their writing. Some teachers judge a paper by the introduction, so it is crucial to make a great first impression.

A good introduction is around 2-3 pages long; this lays down the provisions that determine the entire Statistics coursework . It has a sound structure, which includes the study, object, subject, purpose, hypothesis, task and other elements. The opening can start with information from other authors whose work helped you decide your topic of research. This highlights the work you put in to the reader, making them concentrate more on your work while saving them precious time, which would have been used up to research the topic.

The primary advice here is to devise a working plan. Have backend research always available and accessible. Many amazing papers fail simply because of a lack of proper planning or execution. There are papers which have simple ideas but are executed well; then we have brilliant ideas with poor execution. Concentrate on a plan and the perfect execution, things will fall into place. Having a plan in place will help layout the information in a cohesive manner that is also easy to understand.

We tell people not to judge books by their covers. However, that is not the case with research papers and coursework . Your introduction is what sets you apart from everyone else in your grade.

First , make your beginning attractive. It’s simple, begin with a short explanation on the importance of the theme, and communicate with the readers on why the topic needed to be researched on and how it is relatable to them, this gets yours readers curiosity earning you brownie points.

Follow this with a synopsis of the research question, and what made you choose it. Use layman words artistically, and proofread everything you write as a disinterested reader.

Second , Outline the goals of the project, keep it short and crisp.

Third , make a thesis statement, list out probable results and what you hope to achieve with the information, and possible uses for your data.

Finally , conclude it with a thought or an open-ended question for the reader to think about while going through the coursework.

That is how you write a great introduction to your statistics coursework.

• Coursework assignment help
• Statistics Coursework Help
• Statistics Coursework Homework
• statistics coursework services
• Statistics Homework Help

• A global market analysis (1)
• Academic (22)
• Algorithms (1)
• Big Data Analytics (4)
• Bio Statistics (3)
• Clinical Prediction Model (1)
• Corporate (9)
• Corporate statistics service (1)
• Data Analyses (23)
• Data collection (11)
• Genomics & Bioinformatics (1)
• Guidelines (2)
• Machine Learning – Blogs (1)
• Meta-analysis service (2)
• Network Analysis (1)
• Predictive analyses (2)
• Qualitative (1)
• Quantitaive (2)
• Quantitative Data analysis service (1)
• Research (59)
• Shipping & Logistics (1)
• Statistical analysis service (7)
• Statistical models (1)
• Statistical Report Writing (1)
• Statistical Software (10)
• Statistics (64)
• Survey & Interview from Statswork (1)
• Uncategorized (1)

Recent Posts

• Top 10 Machine Learning Algorithms Expected to Shape the Future of AI
• Data-Driven Governance: Revolutionizing State Youth Policies through Web Scraping
• The Future is Now: The Potential of Predictive Analytics Models and Algorithms
• 2024 Vision: Exploring the Impact and Evolution of Advanced Analytics Tools
• Application of machine learning in marketing

Statswork is a pioneer statistical consulting company providing full assistance to researchers and scholars. Statswork offers expert consulting assistance and enhancing researchers by our distinct statistical process and communication throughout the research process with us.

Functional Area

– Research Planning – Tool Development – Data Mining – Data Collection – Statistics Coursework – Research Methodology – Meta Analysis – Data Analysis

• – Corporate
• – Statistical Software
• – Statistics

Corporate Office

#10, Kutty Street, Nungambakkam, Chennai, Tamil Nadu – 600034, India No : +91 4433182000, UK No : +44-1223926607 , US No : +1-9725029262 Email: [email protected]

Website: www.statswork.com

© 2024 Statswork. All Rights Reserved

NO Trøndelag

Recently viewed courses

Recently viewed.

Find Your Dream School

This site uses various technologies, as described in our Privacy Policy, for personalization, measuring website use/performance, and targeted advertising, which may include storing and sharing information about your site visit with third parties. By continuing to use this website you consent to our Privacy Policy and Terms of Use .

   COVID-19 Update: To help students through this crisis, The Princeton Review will continue our "Enroll with Confidence" refund policies. For full details, please click here.

• Homework Help
• Find a Tutor
• How It Works
• Pre-Med GPA Booster
• Need a test prep tutor? Call us: 888-231-7737

24/7 Statistics Help

Tell us your statistics question, and we’ll connect you with an online tutor in seconds. Get expert statistics help anytime, anywhere.

TRY IT FOR FREE!

How Our Statistics Tutors Can Help

Whatever you're working on, your online tutor will walk you step-by-step through the problem and the solution. Check homework answers, solve a question you're stuck on, or let us help you study for your next statistics test.  Watch how it works.  

Raise Your Statistics Grade

96% of students say they improved their grades with Princeton Review tutors.* Our online classroom is equipped with all the tools you need for homework success. Upload a problem set, practice drawing distribution curves on the interactive whiteboard, and chat with your tutor until your statistics question is answered.

Your Statistics Tutor is Waiting

Get 24/7 statistics help—no appointment needed. Our statistics tutors are online now.

*Based on 2016 survey of students of Princeton Review/Tutor.com

Free MCAT Practice Test

I already know my score.

MCAT Self-Paced 14-Day Free Trial

Enrollment Advisor

1-800-2REVIEW (800-273-8439) ext. 1

1-877-LEARN-30

Mon-Fri 9AM-10PM ET

Sat-Sun 9AM-8PM ET

Student Support

1-800-2REVIEW (800-273-8439) ext. 2

Mon-Fri 9AM-9PM ET

Sat-Sun 8:30AM-5PM ET

Partnerships

• Teach or Tutor for Us

College Readiness

International

Advertising

Affiliate/Other

• Enrollment Terms & Conditions
• Accessibility
• Cigna Medical Transparency in Coverage

Register Book

Local Offices: Mon-Fri 9AM-6PM

• SAT Subject Tests

Academic Subjects

• Social Studies

Find the Right College

• College Rankings
• College Advice
• Applying to College
• Financial Aid

School & District Partnerships

• Professional Development
• Advice Articles
• Private Tutoring
• Mobile Apps
• Local Offices
• International Offices
• Work for Us
• Affiliate Program
• Partner with Us
• Advertise with Us
• International Partnerships
• Our Guarantees
• Accessibility – Canada

Privacy Policy | CA Privacy Notice | Do Not Sell or Share My Personal Information | Your Opt-Out Rights | Terms of Use | Site Map

©2024 TPR Education IP Holdings, LLC. All Rights Reserved. The Princeton Review is not affiliated with Princeton University

TPR Education, LLC (doing business as “The Princeton Review”) is controlled by Primavera Holdings Limited, a firm owned by Chinese nationals with a principal place of business in Hong Kong, China.

• Rankings, Awards, and Stats
• Department Awards
• Diversity and Inclusion
• Undergraduate
• Information for New Statistics Freshmen
• Statistics Major Advising and Registration FAQs
• Statistics Major
• Statistics Minor
• Academic and Research Opportunities
• Clubs and Organizations
• Ph.D. Programs
• Master’s Programs
• Online Programs
• Application Process
• Student Life
• Research Areas
• Administration
• Graduate Students
• Faculty Resources
• Staff Resources
• Student Resources
• IT Resources

Required Courses

The Master of Statistics degree requires a minimum of 30 semester hours (ten courses). This includes seven required courses. Most students take one course per semester while others take a full-time load of three courses per semester.  

We offer our required courses most semesters, allowing the courses to be done in sequence.  For instance, the required ST 501/502/503 courses can be taken in sequence starting in the summer or fall.

• ST 501 – Fundamentals of Statistical Inference I (Summer, Fall)
• ST 502 – Fundamentals of Statistical Inference II (Fall, Spring)
• ST 503 – Fundamentals of Linear Models and Regression (Spring, Summer)

The required ST 517/518 sequence can be taken starting in the summer or fall as well.

• ST 517 – Applied Statistical Methods I (Summer, Fall)
• ST 518 – Applied Statistical Methods II (Fall, Spring)

One programming intensive course from the following list is required:

• ST 554 – Big Data (Python) (Spring, Summer)
• ST 555 – Statistical Programming I (SAS) (Summer, Fall, Spring)
• ST 558 – Data Science for Statisticians (R) (Summer, Fall)

Lastly, our required ‘capstone’ course is available each spring and summer.

• ST 542 – Statistical Practice (Spring, Summer)

For those entering the program with a strong statistics background, the ST 517 requirement can be waived.  For those entering the program with a strong programming background, the requirement of ST 554, ST 555, or ST 558 can be waived. In both cases, the course would be replaced with a three-credit elective.  

Substitutions may be made for courses taken elsewhere, but transfer credit is not given.

Elective Courses

To fill out the remaining nine required credits (three courses), elective courses are taken. We currently do not offer the concentrations available for the in-person master’s degree as we do not have as many elective options available in the online program.

The offerings of electives generally rotates each semester depending on interest and demand.  When scheduling, members of the program are polled and feedback on elective offerings is considered.  

Electives and when they are commonly offered are given below.

• ST 519 – Teaching and Learning of Statistical Thinking (Spring)
• ST 534 – Applied Time Series (Fall)
• ST 537 – Applied Multivariate and Longitudinal Data Analysis (Fall)
• ST 540 – Applied Bayesian Analysis (Spring)
• ST 544 – Applied Categorical Data Analysis (Spring)
• ST 556 – Statistical Programming II (Spring, Summer)
• ST 563 – Introduction to Statistical Learning (Spring, Summer)

Students can also take more than one programming intensive course from the required courses list. Local students can take in-person courses if approved by the program director. Non-ST electives can also be taken with approval from the program director.

Example Plan of Work

Your coursework could really begin in a number of ways.  Below you’ll find a few example course sequences depending on when you are admitted and your intended course load.

Fall admit with summer courses (one-course per semester)

• Year 1: Fall 501, Spring 502, Summer 503
• Year 2: Fall 517, Spring 518, Summer 555 or 558 or 554
• Year 3: Fall elective, Spring 542, Summer elective
• Year 4: Fall elective

Fall admit without summer courses (one-course per semester)

• Year 1: Fall 517, Spring 518
• Year 2: Fall 501, Spring 502
• Year 3: Fall 555 or 558, Spring 503
• Year 4: Fall elective, Spring 542
• Year 5: Fall elective, Spring elective

Fall admit with summer courses (three-courses per semester)

• Fall 501, 517, 555 or 558
• Spring 502, 518, 503
• Fall elective (x 3)

Fall admit without summer courses (three-courses per semester)

Year 1: 

• Spring 502, 503, 518

Spring admit with summer courses (one-course per semester)

• Year 1: Spring 555 or 554, Summer 501
• Year 2: Fall 502, Spring 503, Summer 517
• Year 3: Fall 518, Spring elective, Summer 542
• Year 4: Fall elective, Spring elective

Spring admit without summer courses (one-course per semester)

• Year 1: Spring 555 or 554
• Year 2: Fall 517, Spring 518
• Year 3: Fall 501, Spring 502
• Year 4: Fall elective, Spring 503
• Year 5: Fall elective, Spring 542
• Year 6: Fall elective

Spring admit with summer courses (three-courses per semester)

• Spring 555, 554
• Summer 501, 517
• Fall 502, 518, elective
• Spring 503, 542, elective

Spring admit without summer courses (three-courses per semester)

• Fall 501, 517, elective

Year 3: 

• Fall elective

Summer admit (one-course per semester)

• Year 1: Summer 501
• Year 3: Fall 518, Spring 555 or 554, Summer 542
• Year 4: Fall elective, Spring elective, Summer elective

Summer admit (three-courses per semester)

Year 2: 

• Fall 502, 518, 555 or 558
• Spring 503, elective (x 2)
• Summer 542, elective

• CBSSports.com
• Fanatics Sportsbook
• CBS Sports Home
• Champions League
• Motor Sports
• High School

Football Pick'em

College Pick'em

Fantasy baseball, fantasy football, fantasy basketball, fantasy hockey, franchise games, 24/7 sports news network.

• CBS Sports Golazo Network
• PGA Championship
• UEFA Champions League
• UEFA Europa League
• Italian Serie A
• Watch CBS Sports Network
• TV Shows & Listings

The Early Edge

A Daily SportsLine Betting Podcast

With the First Pick

NFL Draft recap

• Podcasts Home
• The First Cut Golf
• Beyond the Arc
• We Need to Talk Now
• Eye On College Basketball
• NFL Pick Six
• Cover 3 College Football
• Fantasy Football Today
• My Teams Organize / See All Teams Help Account Settings Log Out

2024 PGA Championship picks, predictions: Five trends to help determine this year's winner at Valhalla

Trends pointed to the winner of this year's green jacket; will they have the same success finding who will hoist the wanamaker trophy.

A total of 156 golfers will tee it up this week at the 2024 PGA Championship, and while all would love to believe they have a chance to raise the Wanamaker Trophy at Valhalla Golf Club, history suggests otherwise. There are 21 PGA professionals, a number of past champions who are there for a bit of a joy ride and those that play on the PGA Tour, DP World Tour or LIV Golf who are simply out of form.

That's not to say it is impossible for someone to catch lightning in a bottle ... just look at Phil Mickelson three years ago. He defied conventional wisdom arriving on Kiawah Island without a top-20 finish that calendar year only to leave the South Carolina coast with the Wanamaker Trophy and his sixth major triumph in hand. Oh, and all this happened at the ripe age of 50.

There have been other close calls from unlikely contenders -- like Mito Pereira at Southern Hills -- but with a list of winners that features Brooks Koepka three times, Justin Thomas twice and Collin Morikawa in his championship debut, it's hard to look past the quality of player who is likely to come away with the second major championship of the year.

With all that in mind, let's take a look back at the 10 most recent winners of the PGA Championship and identify what trends exist as we attempt to whittle down the field of 156 players to pinpoint this year's champion.

1. Official World Golf Rankings

The rankings have lost some accuracy and completeness amid the LIV Golf drama, but they are still sound for tours that receive points. We are going to have to get a little flexible here as nine of last 10 winners found themselves ranked inside the top 50 of the OWGR at the time of triumph (Mickelson was ranked 115th). Seven of the last 10 were ranked inside the top 12, a list that does not include Koepka last season. Let's start with that top-50 cutoff point for those players on tours that do receive points.

Eliminated: Non-LIV golfers outside the top 50 of the OWGR, notably Jake Knapp, Adam Scott, Taylor Moore, Alex Noren, Tiger Woods and Michael Block

2. Course changes, stakes do not

The PGA Championship has moved in more ways than one over the last 10 years. Not only does the championship rotate golf courses, it has also moved up from August to May on the calendar; that switch happened in 2019. Regardless, quality has proven to be an important barometer at this championship in particular. Nine of the last 10 champions had a top-20 finish to their name with the lone exception being Morikawa, who won in his debut appearance (2020). 

Eliminated:  Dean Burmester, Adrian Meronk, Joaquin Niemann, Andy Ogletree, David Puig, Wyndham Clark, Sahith Theegala, Ludvig Åberg, Matthieu Pavon, Byeong Hun An, Tom Kim, Nick Taylor, Denny McCarthy, Akshay Bhatia, Nicolai Hojgaard, Stephan Jaeger, J.T. Poston, Adam Schenk, Austin Eckroat

3. What have you done for me lately?

Major champions  rarely  pop up unannounced, and even Mickelson had flashed some form in 2021. The six-time major champion won a couple times on the Champions Tour that fall and actually held the first-round lead at the Wells Fargo Championship two weeks prior to his victory. Rory McIlroy, Jason Day and Thomas (2017) had already claimed multiple victories, while Koepka (2018, 2023) and Morikawa entered the winner's circle within the prior handful of starts. Nine of the last 10 winners had at least one top-five finish under their belt during the calendar year.

Eliminated: Martin Kaymer, Phil Mickelson, Viktor Hovland, Lucas Glover, Rickie Fowler, Si Woo Kim, Emiliano Grillo, Harris English, Kurt Kitayama, Corey Conners

4. Major-championship pedigree

Six of the last 10 winners already had a major victory on their résumé. The last winner at Valhalla, McIlroy, already had three at the time of his victory. Last year's champion, Koepka, already had four. Once again, Morikawa bucks this trend -- it was only his second major championship -- but nine of the last 10 winners had at least one top-10 finish in a major championship.

Eliminated:  Sam Burns, Lucas Herbert, Talor Gooch

5. Let's get technical

If finishing positions don't do it for you, strokes-gained data is likely your vice. The last 10 champions all averaged positive strokes gained per round over the last three months with Mickelson and Walker bringing up the rear at +0.43 and +0.04, respectively. When looking at this, it makes sense they flew in under the radar. The same cannot be said for Day (+2.03), Koepka in 2018 (+2.09), Thomas in 2022 (+2.36) and McIlroy (+2.86). All together, this group of champions averaged +1.64 strokes gained per round -- the benchmark we will be utilizing much to the dismay of many recent major champions and many potential contenders.

Eliminated : Brooks Koepka, Jon Rahm, Tyrrell Hatton, Dustin Johnson, Patrick Reed, Cameron Smith, Patrick Cantlay, Max Homa, Brian Harman, Tommy Fleetwood, Collin Morikawa, Cameron Young, Russell Henley, Jason Day, Keegan Bradley, Sepp Straka, Jordan Spieth, Chris Kirk, Justin Thomas, Sungjae Im, Will Zalatoris, Min Woo Lee

That leaves us with ... five golfers

Scottie Scheffler, Rory McIlroy, Bryson DeChambeau, Xander Schauffele, Hideki Matsuyama

Scheffler is far and away the best player in the world with four wins in his last five starts and all the statistics to back it up, but can he overcome a month layoff en route to his second major championship this season? McIlroy, fresh off back-to-back wins, eyes his first major title in a decade at the golf course where he last raised the Wanamaker Trophy. DeChambeau has returned to a major-caliber player grabbing the first-round lead not only at the Masters this year but also the PGA Championship last year. Will he string it together for all 72 holes? 

Meanwhile, Schauffele continues to be a statistical darling and top-10 machine, but at some point, a player of his skill level has to go out there and grab a big-time golf tournament away from big-time competitors. Matsuyama may be the biggest question mark of the bunch following his withdrawal from the Wells Fargo Championship, but if he is healthy, he is capable of adding another major trophy to his mantle.

Our Latest Golf Stories

2024 PGA Championship odds, picks, best bets, field

Cbs sports staff • 5 min read.

2024 PGA Championship expert picks, odds, bets, field

2024 PGA Championship One & Done expert picks, sleepers

Cbs sports staff • 4 min read.

2024 PGA Championship expert picks, predictions, odds

Kyle porter • 13 min read, 2024 pga championship fantasy golf picks, bets, cbs sports staff • 3 min read, 2024 pga championship prop picks, bets, parlay, odds, share video.

Trends to help identify winner at PGA Championship

PGA Championship expert picks, predictions

Ranking the 2024 PGA field, 1-24

Who to cheer this week at Valhalla

Five sleepers who could win it all

Round 1 tee times, pairings at PGA Championship

Tiger on PGA Tour, PIF deal: 'It changes day to day'

Scheffler to be without usual caddie for Round 3 at PGA

PGA Championship TV schedule, coverage guide

PGA odds: Scottie, Brooks, Rory among favorites