# Introductory Business Statistics

Lex Holmes, University of Oklahoma

Barbara Illowsky, De Anza College

Susan Dean, De Anza College

Copyright Year: 2017

ISBN 13: 9781947172470

Publisher: OpenStax

Language: English

## Formats Available

## Conditions of Use

Attribution

CC BY

## Reviews

This textbook covers the major topics in the introductory of statistics. There are 13 chapters, and the first 3 chapters focus on the introduction of data, descriptive statistics and probabilities. From Chapter 4 to Chapter 7, those chapters... read more

This textbook covers the major topics in the introductory of statistics. There are 13 chapters, and the first 3 chapters focus on the introduction of data, descriptive statistics and probabilities. From Chapter 4 to Chapter 7, those chapters introduce the basic concepts in both discrete random variables and continuous random variables. This textbook also covers the confidence intervals, hypotheses tests, ANOVA and simple linear regression. In my opinions, those chapters are explained and organized consistently and easy to follow. However, as an introductory statistics textbook for students majoring in Business, I think this textbook probably doesn’t provide more relevant examples in Business. For example, in CH4, there are only a few examples related to business. As for a one quarter business statistics class, 13 chapters are still a little bit lengthy. For example, the textbook introduces normal distribution in Chapter 6, and Central Limit Theorem in Chapter 7, it might be appropriate to put Central Limit Theorem as a section in Chapter 6.

The contents of this textbook are accurate, error-free and unbiased.

When I began to read this textbook, I expected to see some application of Excel or even R, as using these software or programming language would be greatly helpful for our undergraduate students. However, I didn’t see those applications in this textbook. Although including the application of Excel might make the textbook lengthy, I think it might be useful for instructors to use show those applications to students and strengthen their understanding about how to use Excel or R to do basic business analysis.

This textbook is well organized consistently and easy to follow. It also provides numerous graphs and figures to visualize the statistical analysis.

The contents of this textbook are internally consistent in terms of terminology and framework. Each chapter begins with an interesting statistical topic in reality, and then follows the “Terminology” section to introduce key concepts. It also provides examples for each section to strengthen students’ learning about new contents. At the end of each chapter, it summarizes the key terms, chapter review, formula review, which would be convenient for students to grasp the major contents.

There are 13 chapters in this textbook, and after reading each chapter, I think the text can be readily divided into smaller reading sections. For example, if I just want to introduce Chi-square distribution, then I can assign Chapter 11 for students to learn without asking them to read Chapter 7, which focuses on Central Limit Theorem.

Overall, the contents are well organized in a logical fashion. However, I want to point it out the Venn Diagrams in Chapter 3 taking much more spaces than it supposes to be. For example, from page 164 to page 167, the Venn Diagrams take nearly a half page, which are not well organized in the flow. On page 292, the format of key terms are not well aligned and seem to be a little bit messy to follow.

Download the textbook online is easy and the hyperlinks in each section work well.

I cannot recall any grammatical errors in the textbook.

I think there is no culturally offensive content.

As an instructor to community college students, I think the content of this textbook is easy to go through for an introduction class of Business Statistics. However, I think a textbook designated for teaching statistics for students majoring in Business, this textbook doesn't provide enough business analysis examples. And I would also recommend the authors can add some applications of Excel or R to make statistical analysis more applicable for both students and instructors.

This textbook covers all the relevant chapters for a one-semester Business Statistics undergraduate class. Some of the concepts could more details (e.g., hypergeometric distribution, uniform distribution, separating simple and multiple linear... read more

This textbook covers all the relevant chapters for a one-semester Business Statistics undergraduate class. Some of the concepts could more details (e.g., hypergeometric distribution, uniform distribution, separating simple and multiple linear regression) while some other concepts could be added relevant to business students (e.g., expected returns, variance, standard deviation, log-normal distribution, two-factor ANOVA). Some chapters do not include enough examples (e.g., Chapter 4) and some other chapters do not include examples relevant to business students (e.g., Chapter 3).

The contents seem to be accurate, unbiased, and without any gross errors.

The statistical concepts are not going to change anytime soon, so the materials would be relevant probably for a long time. However, the presentation of examples and most importantly, the lack of business examples and the lack of data in Excel (or other formats) are going to be a bog issue for future instructors and students. As an Instructor, I love to demonstrate examples using MS Excel in class, and the lack of Excel data is a big concern for me. And, there is no guidance for using data analysis software (MS Excel, R, and others). It would be difficult to compete with the publisher textbooks who provide these supports.

Overall, the text is clear, easy to understand, and concise. Some chapters have lots of graphs and examples. However, some concepts are very short and without many examples which makes it harder to grasp the concept. Also, elaborating some concepts would provide a better understanding to some concepts, such as, separating sections for simple and multiple linear regression model.

The book is consistent in terms of concepts, materials, annotations, and chapter structure.

The chapters are independent of each other, and a chapter can easily be added or skipped based on individual needs.

The topics are well organized, and the flow is smooth. As a minor suggestion, I would love to see reorganization of few concepts, such as hypergeometric distribution after geometric distribution in Chapter 4, and a short explanation of normal distribution in Chapter 5 and why it deserves to be a separate chapter (Chapter 6). Also, separating sections for simple and multiple linear regression model in Chapter 13 would make the structure more interesting.

I did not find any interface issue. Both online and PDF versions work well without any distortions.

I did not find any grammatical errors.

I did not find anything insensitive or offensive. The texts and problems seem inclusive and unbiased.

Overall, this is a book with the minimum number of chapters needed for an introductory business statistics course. Some chapters and concepts could have been more elaborate with business relevant examples. One concern is the data availability for students to work on different concepts. Providing the data in Excel format would make the textbook much more attractive. A reliable and automated homework/quiz platform would be nice too, but given that this is a free textbook, it is worth a try.

All relevant chapters covered in most undergraduate introductory statistics classes are included and explained in a consistent and clear way that keep students engaged. Considering that the book is intended to be used by students majoring in... read more

All relevant chapters covered in most undergraduate introductory statistics classes are included and explained in a consistent and clear way that keep students engaged. Considering that the book is intended to be used by students majoring in business, the application of statistical methods and tools in the business setting could have been more pronounced. There are a few chapters (for example, on probability) that barely mention any type of statistical problem set in the realm of management, finance, marketing, HR, etc. The book provides an effective index at the end, but not the glossary. Though not an issue, the students should be instructed to find the term in the index and search for the definition in the corresponding chapter (each chapter ends with key terms and a review which is quite helpful).

The content of the book seems free of any gross errors and biases.

The standard statistical concepts that the book covers will not change any time soon. Though the lack of business-specific (or pop culture) examples might be a missed opportunity, providing the typical cards, balls and student GPA examples makes the book less likely to be dated in the next decade.

The text is clear, easy to follow and understand. The authors provide numerous examples to make the concepts comprehensible. They also use visual tools, such as tables and figures, well to keep the students’ attention and enhance the understanding of the statistical problems at hand.

The book is consistent in terms of language, tone, annotation and chapter structure (introduce, give basic examples, build, add more complex problems, finish with reviews and practice problems).

The flow of the chapters is logical and can be easily divided into smaller sections. There are a number of subsections in a chapter that can be included or skipped based on the individual course learning goals. A number of chapters (especially sub-chapters) overlap with the authors’ general statistics book that is also part of the Open Stax library and includes additional chapters (can be combined in an extended course syllabus).

Overall, the topics are organized well in a logical fashion. There were a few instances in the book where individual instructors would choose to cover a specific sub-chapter earlier or later in the course, mostly to follow the research process (from a question, hypotheses, design, data collection, analyses, interpretation…). However, since chapters are easily divisible, a different flow of topics can be easily arranged based on course needs and learning goals.

The authors have provided a number of problems and concepts with visual representation. Using visual tools in introductory courses is very welcome and enhances student understanding. Though some images and charts vary in size and detail (x-y axes), I haven’t found any that are distorted to the point of confusion. Use of colors and notation works reasonably well on different platforms.

The text and questions are clearly and correctly worded.

The text and problems in the chapters seem inclusive, not culturally offensive or insensitive. A number of examples mention different races, ethnicity, political affiliations, but in a neutral tone, without bias.

Students will appreciate each chapter ending with key terms, a chapter review, a formula review and a long set of practice problems. As instructors, we frequently have students ask for additional problems to work on in order to prepare for tests and/or to understand the concept variations better. This book provides plenty of problems for them and wraps up each chapter with more homework problems. Solutions are provided at the end.

The textbook covers all of the main topics for a typical one semester Business Statistics course: descriptive statistics, probability, discreet and continuous distributions, central limit theory and confidence intervals, hypothesis testing for 1,... read more

The textbook covers all of the main topics for a typical one semester Business Statistics course: descriptive statistics, probability, discreet and continuous distributions, central limit theory and confidence intervals, hypothesis testing for 1, 2, or many samples, Chi-Squared distributions, and simple and multiple linear regression. Some of the chapters could use more details if the reader wants a more comprehensive coverage of the topics. It would be up to the instructor using this textbook to supplement textbook with details that they deem important. One example is in the descriptive statistics chapter where there could be an explicit discussion of the difference between frequency distributions and graphs for nominal versus ratio data. Another example is in the ANOVA chapter which does not cover Two Way ANOVAs or block designs.

The accuracy is very good in this textbook. However, one area of concern, which is often hotly debated, is found in the Linear Regression and Correlation chapter where there is a discussion of how independent variables will have a significant effect on the dependent variable. Saying that one variable has a significant effect on another variable should only be done in the context of an experimental design. Correlational analysis can only suggest a cause and effect relationship or allow us to make predictions.

The fundamental topics in this textbook are very stable. There should be little difficulty with the longevity of the textbook.

The text does a good job of concisely describing the topics. There a many unique descriptions of concepts that made the book enjoyable to read.

There were no problems with consistency.

The chapters do well standing independently of each other. They are also well organized internally with practice problems and homework problems at the end of each chapter.

The textbook does not deviate from the organization found in most business statistic text books. One minor difference from the typical structure is the combination of frequency distributions and graphs with the topics of central location and variable in a single chapter.

I read some of the textbook using the Kindle but on a cell phone. While this made the book easily accessible, I would recommend using a tablet or browser.

There were very few errors noticed while reading the textbook. There were recent changes made in the text book at the time this review was written based on the History section found on the browser version. One correction that could be made is in section 9.1: “that is set my the analyst” should be “that is set by the analyst”.

There were no noticeable problems in the examples or homework problems.

In most business statistic textbooks, there is usually the problem of there being too many chapters and sections to cover in a single semester and it is the job of the instructor to select which topics to cover and which to ignore. As I was reviewing this book I was struck more by what was missing than want was in the book. But that might be as much my own expectations of what should be in a business statistic textbook. This textbook covers the minimum number of topics and depending on what is taught in a particular course might require supplemental coverage. For example Time-Series Forecasting is not included, but this might not be a problem in many courses. I was also concerned that there is no mention of the terms ‘false positive’ and ‘false negative’ when introducing Type I and Type II errors, but this is something I can cover in class lectures.

In reading and reviewing this resource it is very complete, but very specific to business statistics. All statistical components and aspects are taken into consideration in a factual way, but not always tied back to the wider research process. The... read more

In reading and reviewing this resource it is very complete, but very specific to business statistics. All statistical components and aspects are taken into consideration in a factual way, but not always tied back to the wider research process. The examples and ability to try out the concepts throughout the chapter keep the readers mind engaged and active.

This resource is what I would expect from a business statistics book. It covers the material in a factual, clear manner.

There are no cultural or popular references in this resource that will date it in a few years. It uses tried and true example such as playing cards or sports to demonstrate the topics. It does occasionally refer to "this course" but does not specify the course, and is easily adaptable to any relevant course.

The resource is clearly written with relevant examples and context. It is easy to understand and follow within each chapter and from chapter to chapter.

The text is consistent in language and structure.

The chapters of this resource stand alone well, and can be easily remixed or used individually to cover a specific concept. Much of the book also overlaps with the Open Stax more general statistics book.

The organization seems to be more along the statistical needs of the students, than the research process itself. However, with the ease of modularity each chapter can stand alone, and easily be remixed to the instructor's liking.

Using both an online copy and a PDF copy this text works well with no distortions or interface issues. In the PDF edition it can be onerous to scroll through the end of chapter problems if one is not completing them.

The resource meets with American English grammatical standards and expectations. It is easily read at an introductory or early college level.

This resource is culturally neutral. There is minimal mention of qualitative data, and no bias or cultural references given in any of the examples or data described.

The number of student problems in each chapter is astounding. Students who complete (sometimes more than 100) problems on a given topic will understand it quite well.

Very good for an introductory book. Actually better than the text I've used in the past, covering several key areas such as types of distributions. The authors chose specific enough statistics that students do not need more than the free... read more

Very good for an introductory book. Actually better than the text I've used in the past, covering several key areas such as types of distributions. The authors chose specific enough statistics that students do not need more than the free statistics add-on in Excel to use pretty much everything explored in the text. The text is appropriate in a 1st of 2 statistics courses. It does not cover non-linear regression as would be used to assess likelihood of outcome, it does not cover descriptive clustering, and it does not cover predictive segmentation. It also does not cover time-series analysis. As a result, it does not cover the techniques commonly employed in business. But it does provide the background necessary prior to learning and use of more advanced topics.

The content appears to be accurate, error-free and unbiased.

This book does not need to change for at least several hundred years. May be good forever, literally.

Really clear, easy to understand. Nice diagrams and examples, many questions and exercises built in. Built to use Excel. World-class for a stats book.

Very consistent and stays within its limits. Doesn't stray from introductory statistics using the Excel stats package.

Very well divided and logically clear.

Flows in the order I would choose. Not need or benefit to cover in anything other than chapter order.

Better than I expected for a PDF. Links work well, sections are logical.

Very clear for a stats book, Questions seemed carefully worded to avoid misinterpretation. Of course, students are very clever when it comes to finding ways to misinterpret, so we'll see once I use it in class.

Unless someone is professionally offended, and looking for ways to claim to be offended in order to further their career or notoriety, it is unlikely in my opinion they will find a fair, reasonable, and legitimate cause to be offended as a result of this text.

Way, way better than I honestly expected.

How one assesses the comprehensiveness of this text depends on one’s purpose. It is purportedly designed for a one-semester course. For that (at least relative to business students at UNO), it is too long -- and too long on the mathematics. For a... read more

How one assesses the comprehensiveness of this text depends on one’s purpose. It is purportedly designed for a one-semester course. For that (at least relative to business students at UNO), it is too long -- and too long on the mathematics. For a two-semester course, at least for our purposes, it is too short. For example, there is no discussion of 2-factor Anova, RBD, etc. However, there is much to admire about the way that the authors present the ideas.

I spent about four hours reading various parts of the text and found no sign of bias or any gross errors. One can quibble over some of the definitions, e.g., that a discrete random variable must have only integral values. I saw a typo or two -- e.g., an SStotal that should have been an SSbetween. As with any book, there are probably others. But let me emphasize that I am not a professional statistician.

Introductory statistics is a little bit like Latin, a ‘dead language’. The basics aren’t going to change. However, the “statistics education community” -- if there is such a thing -- seems to be in a tizzy these days over how to incorporate ‘big data’, etc. into such introductory courses. Some now use the term ‘data science.’ This text is definitely an old-fashioned and rather ‘mathy’ approach (not a bad thing in my eyes). Surprisingly, calculus techniques make an appearance toward the end of the book (the average business student will have little or no idea of that). But, aside from some instructions for using Excel for regression analysis (why not do this for Anova as well?), there is little guidance for technology. In the chapter on the F-distribution, where did those p-values come from? TI-83? Excel? StatTrek? Or did I miss something? Most of the current business stats texts give directions for using Excel, TI-83/4, Minitab, R or all of these. How is this text going to compete with those? I reckon that individual instructor/department could make amendments, but how many would be willing to?

Overall, I like the breezy writing style. But it is a bit bipolar, occasionally almost patronizing and then rather technical. Some terms are used which the student audience has virtually no chance of understanding. Better to omit those or provide explanation. On the other hand, since few students these days actually read books, particularly math books, anyway, why not let the authors express themselves in a way that they find logical and intellectually appealing? My students depend on me to explain the material, or, if I fail them, YouTube.

The text seemed terminologically consistent to me. I do recall a spot in the Anova section where the use of n (nT?) and nj may cause confusion.

Introductory statistics is not a very modular sort of subject -- it is more a continuous development. For example, the concept of p-value is introduced in chapter 9 of virtually every stats text. But the ‘p’ in p-value is for ‘probability’ and so the student needs to understand the material that chapter, 3 or 4 in virtually every text. Otherwise, I liked the presentation given here in ch. 4 on discrete distributions, but since the authors very nicely tie them together, that material is not presented in a ‘modular’ fashion at all.

The topics of the text are presented in the normal progression. There is some possibility of changing the order of presentation after hypothesis testing (Ch.9), but not before that. Once again, this is how introductory stats works.

Interface is fine, although I absolutely hate scrolling through a pdf. Students would definitely want a print version.

I ain’t found no grammar errors.

Found nothing culturally insensitive. Seems inclusive. All groups are subject to statistics.

Finally, the unscripted part of the review. There is a lot to like about this presentation of the subject. Some parts are quite enjoyable. Here are my criticisms, in order of my view of their increasing importance. 1. The type-setting (if that is the right term) of the formulas in generally pretty lousy. For example, x-bar is always shown with the bar about a mile above the x. The integral on p. 284 looks really bad. Those formulas for r are terrible-looking. I always use MathType for this stuff. On the subject of formulas, why the predilection for ‘computing’ formulas, rather than ‘definition’ formulas? Many authors do this. Who, except programmers, cares how the computer does it? The definition formula offers insight. 2. I like the chapters on discrete and continuous distributions. I think that those on confidence intervals and Anova are not well-written and will be confusing to students. With the chapter on regression, the authors are forced to yield to the complexity of the subject and necessarily trail off into territory that the typical business student has no chance to follow. 3. Ok, here is the 500-lb statistician in the room: My guess is that, except at some rather rarified and/or old-fashioned institutions, virtually everyone teaching introductory statistics now insists on a reliable automated homework/quiz course platform, like MyStatLab. The difficultly/impossibility of doing that is why I don’t write and provide my own free text. We use a Pearson e-book that provides that service to our two-semester sequence for $104.95.

## Table of Contents

Preface

- 1 Sampling and Data
- 2 Descriptive Statistics
- 3 Probability Topics
- 4 Discrete Random Variables
- 5 Continuous Random Variables
- 6 The Normal Distribution
- 7 The Central Limit Theorem
- 8 Confidence Intervals
- 9 Hypothesis Testing with One Sample
- 10 Hypothesis Testing with Two Samples
- 11 The Chi-Square Distribution
- 12 F Distribution and One-Way ANOVA
- 13 Linear Regression and Correlation

Statistical Tables

Mathematical Phrases, Symbols, and Formulas

## Ancillary Material

## About the Book

*Introductory Business Statistics* is designed to meet the scope and sequence requirements of the one-semester statistics course for business, economics, and related majors. Core statistical concepts and skills have been augmented with practical business examples, scenarios, and exercises. The result is a meaningful understanding of the discipline, which will serve students in their business careers and real-world experiences.

## About the Contributors

### Authors

**Lex Holmes** is a Professor in the Economics department at University of Oklahoma, Norman, OK

**Barbara Illowsky** is a Professor of Mathematics & Statistics at De Anza College.

**Susan Dean** is a Professor in the Mathematics department at De Anza College, Cupertino, CA.