Even You Can Learn Statistics and Analytics: An Easy to Understand Guide

by David Levine, David Stephan

Overview

THE GUIDE FOR ANYONE AFRAID TO LEARN STATISTICS & ANALYTICS UPDATED WITH NEW EXAMPLES & EXERCISES

This book discusses statistics and analytics using plain language and avoiding mathematical jargon. If you thought you couldn't learn these data analysis subjects because they were too technical or too mathematical, this book is for you!

This edition delivers more everyday examples and end-of-chapter exercises and contains updated instructions for using Microsoft Excel. You'll use downloadable data sets and spreadsheet solutions, template-based solutions you can put right to work. Using this book, you will understand the important concepts of statistics and analytics, including learning the basic vocabulary of these subjects.

• Create tabular and visual summaries and learn to avoid common charting errors
• Gain experience working with common descriptive statistics measures including the mean, median, and mode; and standard deviation and variance, among others
• Understand the probability concepts that underlie inferential statistics
• Learn how to apply hypothesis tests, using Z, t, chi-square, ANOVA, and other techniques
• Develop skills using regression analysis, the most commonly-used Inferential statistical method
• Explore results produced by predictive analytics software
• Choose the right statistical or analytic techniques for any data analysis task
• Optionally, read the "Equation Blackboards," designed for readers who want to learn about the mathematical foundations of selected methods

Product Details

ISBN-13:	9780137654765
Publisher:	Pearson Education
Publication date:	06/06/2022
Series:	Pearson Business Analytics Series
Edition description:	4th ed.
Pages:	400
Product dimensions:	6.90(w) x 9.13(h) x 0.88(d)

About the Author

David M. Levine and David F. Stephan are part of a writing team known for their series of business statistics textbooks that include Basic Business Statistics, Business Statistics: A First Course, and Statistics for Managers Using Microsoft Excel. In long teaching careers at Baruch College, both were known for their classroom innovations, with Levine being honored with a Presidential Excellence Award for Distinguished Teaching Award and Stephan granted the privilege to design and develop the College's first computer-based classroom. Both are active members of the Data, Analytics and Statistics Instruction SIG of the Decision Sciences Institute.

Levine is Professor Emeritus of Information Systems at Baruch College. He is nationally recognized innovator in business statistics education and is also the coauthor of Applied Statistics for Engineers and Scientists Using Microsoft Excel and Minitab. Levine is also the author or coauthor of four books about statistical quality management: Statistics for Six Sigma Green Belts and Champions, Six Sigma for Green Belts and Champions, Design for Six Sigma for Green Belts and Champions, and Quality Management, 3rd Edition. He has published articles in various journals, including Psychometrika, The American Statistician, Communications in Statistics, Multivariate Behavioral Research, Journal of Systems Management, Quality Progress, and The American Anthropologist, and has given numerous talks at American Statistical Association, Decision Sciences Institute, and Making Statistics More Effective in Schools of Business conferences.

During his more than 20 years at Baruch College, Stephan devised techniques for teaching computer applications such as Microsoft Excel in a business context and developed future-forward courses that explored the effects of emerging digital technologies. He also served as the associate director of a U.S. Department of Education FIPSE project that successfully integrated interactive media into classroom instruction for the humanities.. Stephan is also the developer of PHStat, the statistics add-in for Microsoft Excel distributed by Pearson Education.

Table of Contents

Introduction: The Even You Can Learn Statistics and Analytics Owner's Manual xiii

Chapter 1 Fundamentals of Statistics 1

1.1 The First Three Words of Statistics 2

1.2 The Fourth and Fifth Words 4

1.3 The Branches of Statistics 4

1.4 Sources of Data 5

1.5 Sampling Concepts 7

1.6 Sample Selection Methods 8

Chapter 2 Presenting Data in Tables and Charts 15

2.1 Presenting Categorical Variables 15

2.2 Presenting Numerical Variables 23

2.3 "Bad" Charts 29

Chapter 3 Descriptive Statistics 45

3.1 Measures of Central Tendency 45

3.2 Measures of Position 49

3.3 Measures of Variation 54

3.4 Shape of Distributions 59

Chapter 4 Probability 75

4.1 Events 75

4.2 More Definitions 76

4.3 Some-Rules of Probability 78

4.4 Assigning Probabilities 81

Chapter 5 Probability Distributions 87

5.1 Probability Distributions for Discrete Variables 87

5.2 The Binomial and Poisson Probability Distributions 93

5.3 Continuous Probability Distributions and the Normal Distribution 100

5.4 The Normal Probability Plot 108

Chapter 6 Sampling Distributions and Confidence Intervals 121

6.1 Foundational Concepts 122

6.2 Sampling Error and Confidence Intervals 125

6.3 Confidence Interval Estimate for the Mean Using the t Distribution (σ Unknown) 128

6.4 Confidence Interval Estimation for Categorical Variables 131

6.5 Confidence Interval Estimation When Normality Cannot Be Assumed 134

Chapter 7 Fundamentals of Hypothesis Testing 145

7.1 The Null and Alternative Hypotheses 145

7.2 Hypothesis Testing Issues 147

7.3 Decision-Making Risks 149

7.4 Performing Hypothesis Testing 150

7.5 Types of Hypothesis Tests 152

Chapter 8 Hypothesis Testing: Z and t Tests 157

8.1 Test for the Difference Between Two Proportions 157

8.2 Test for the Difference Between the Means of Two Independent Groups 163

8.3 The Paired t Test 168

Chapter 9 Hypothesis Testing: Chi-Square Tests and the One-Way Analysis of Variance (ANOVA) 183

9.1 Chi-Square Test for Two-Way Tables 183

9.2 One-Way Analysis of Variance (ANOVA): Testing for the Differences Among the Means of More Than Two Groups 191

Chapter 10 Simple Linear Regression 211

10.1 Basics of Regression Analysis 211

10.2 Developing a Simple Linear Regression Model 214

10.3 Measures of Variation 221

10.4 Inferences About the Slope 226

10.5 Common Mistakes When Using Regression Analysis 229

Chapter 11 Multiple Regression 243

11.1 The Multiple Regression Model 243

11.2 Coefficient of Multiple Determination 246

11.3 The Overall F Test 246

11.4 Residual Analysis for the Multiple Regression Model 247

11.5 Inferences Concerning the Population Regression Coefficients 248

Chapter 12 Introduction to Analytics 259

12.1 Basic Concepts 259

12.2 Descriptive Analytics 265

12.3 Typical Descriptive Analytics Visualizations 269

Chapter 13 Predictive Analytics 279

13.1 Predictive Analytics Methods 279

13.2 More About Predictive Models 281

13.3 Tree Induction 284

13.4 Clustering 287

13.5 Association Analysis 290

Appendix A Microsoft Excel Operation and Configuration 299

A.1 Conventions for Keystroke and Mouse Operations 299

A.2 Microsoft Excel Technical Configuration 300

Appendix B Review of Arithmetic and Algebra 301

Assessment Quiz 301

Symbols 303

Answers to Quiz 310

Appendix C Statistical Tables 311

Appendix D Spreadsheet Tips 339

Chart Tips 339

Function Tips 341

Appendix E Advanced Techniques 343

Advanced How-To Tips 343

Analysis ToolPak Tips 349

Appendix F Documentation for Downloadable Files 353

F1 Downloadable Data Files 353

F2 Downloadable Spreadsheet Solution Files 355

Index 357