# Book: Statistics for Managers Using Microsoft Excel

Book: Statistics for Managers Using Microsoft Excel

For one- or two-semester business statistics courses. Not a new book, but a popular one (8th edition.)

This text is the gold standard for learning how to use Excel in business statistics, helping students gain the understanding they need to be successful in their careers. The authors present statistics in the context of specific business fields; full chapters on business analytics further prepare students for success in their professions. Current data throughout the text lets students practice analyzing the types of data they will see in their professions. The friendly writing style include tips throughout to encourage learning.

The book also integrates PHStat, an add-in that bolsters the statistical functions of Excel.

Click on picture to zoom in

Table of Contents

First Things FirstFTF.1 Think Differently About Statistics

FTF.2 Business Analytics: The Changing Face of Statistics

FTF.3 Getting Started Learning Statistics

FTF.4 Preparing to Use Microsoft Excel for Statistics

1. Defining and Collecting Data

1.1 Defining Variables

1.2 Collecting Data

1.3 Types of Sampling Methods

1.4 Data Preparation

1.5 Types of Survey Errors

2. Organizing and Visualizing Variables

2.1 Organizing Categorical Variables

2.2 Organizing Numerical Variables

2.3 Visualizing Categorical Variables

2.4 Visualizing Numerical Variables

2.5 Visualizing Two Numerical Variables

2.6 Organizing and Visualizing a Mix of Variables

2.7 The Challenge in Organizing and Visualizing Variables

3. Numerical Descriptive Measures

3.1 Central Tendency

3.2 Variation and Shape

3.3 Exploring Numerical Data

3.4 Numerical Descriptive Measures for a Population

3.5 The Covariance and the Coefficient of Correlation

3.6 Statistics: Pitfalls and Ethical Issues

4. Basic Probability

4.1 Basic Probability Concepts

4.2 Conditional Probability

4.3 Ethical Issues and Probability

4.4 Bayes’ Theorem

4.5 Counting Rules

5. Discrete Probability Distributions

5.1 The Probability Distribution for a Discrete Variable

5.2 Binomial Distribution

5.3 Poisson Distribution

5.4 Covariance of a Probability Distribution and its Application in Finance

5.5 Hypergeometric Distribution

6. The Normal Distribution and Other Continuous Distributions

6.1 Continuous Probability Distributions

6.2 The Normal Distribution

6.3 Evaluating Normality

6.4 The Uniform Distribution

6.5 The Exponential Distribution

6.6 The Normal Approximation to the Binomial Distribution

7. Sampling Distributions

7.1 Sampling Distributions

7.2 Sampling Distribution of the Mean

7.3 Sampling Distribution of the Proportion

8. Confidence Interval Estimation

8.1 Confidence Interval Estimate for the Mean (Known)

8.2 Confidence Interval Estimate for the Mean (Unknown)

8.3 Confidence Interval Estimate for the Proportion

8.4 Determining Sample Size

8.5 Confidence Interval Estimation and Ethical Issues

8.6 Application of Confidence Interval Estimation in Auditing

8.7 Estimation and Sample Size Estimation for Finite Populations

8.8 Bootstrapping

9. Fundamentals of Hypothesis Testing: One-Sample Tests

9.1 Fundamentals of Hypothesis-Testing Methodology

9.2 t Test of Hypothesis for the Mean (Unknown)

9.3 One-Tail Tests

9.4 Z Test of Hypothesis for the Proportion

9.5 Potential Hypothesis-Testing Pitfalls and Ethical Issues

9.6 Power of the Test

10. Two-Sample Tests

10.1 Comparing the Means of Two Independent Populations

10.2 Comparing the Means of Two Related Populations

10.3 Comparing the Proportions of Two Independent Populations

10.4 F Test for the Ratio of Two Variances

10.5 Effect Size

11. Analysis of Variance

11.1 The Completely Randomized Design: One-Way ANOVA

11.2 The Factorial Design: Two-Way ANOVA

11.3 The Randomized Block Design

11.4 Fixed Effects, Random Effects, and Mixed Effects Models

12. Chi-Square and Nonparametric Tests

12.1 Chi-Square Test for the Difference Between Two Proportions

12.2 Chi-Square Test for Differences Among More Than Two Proportions

12.3 Chi-Square Test of Independence

12.4 Wilcoxon Rank Sum Test: A Nonparametric Method for Two Independent Populations

12.5 Kruskal-Wallis Rank Test: A Nonparametric Method for the One-Way ANOVA

12.6 McNemar Test for the Difference Between Two Proportions (Related Samples)

12.7 Chi-Square Test for the Variance or Standard Deviation

13. Simple Linear Regression

13.1 Types of Regression Models

13.2 Determining the Simple Linear Regression Equation

13.3 Measures of Variation

13.4 Assumptions of Regression

13.5 Residual Analysis

13.6 Measuring Autocorrelation: The Durbin-Watson Statistic

13.7 Inferences About the Slopeand Correlation Coefficient

13.8 Estimation of Mean Values and Prediction of Individual Values

13.9 Potential Pitfalls in Regression

14. Introduction to Multiple Regression

14.1 Developing a Multiple Regression Model

14.2 r2, Adjusted r2, and the Overall F Test

14.3 Residual Analysis for the Multiple Regression Model

14.4 Inferences Concerning the Population Regression Coefficients

14.5 Testing Portions of the Multiple Regression Model

14.6 Using Dummy Variables and Interaction Terms in Regression Models

14.7 Logistic Regression

15. Multiple Regression Model Building

15.1 Quadratic Regression Model

15.2 Using Transformations in Regression Models

15.3 Collinearity

15.4 Model Building

15.5 Pitfalls in Multiple Regression and Ethical Issues

16. Time-Series Forecasting

16.1 The Importance of Business Forecasting

16.2 Component Factors of Time-Series Models

16.3 Smoothing an Annual Time Series

16.4 Least-Squares Trend Fitting and Forecasting

16.5 Autoregressive Modeling for Trend Fitting and Forecasting

16.6 Choosing an Appropriate Forecasting Model

16.7 Time-Series Forecasting of Seasonal Data

16.8 Index Numbers

17. Getting Ready to Analyze Data in the Future

17.1 Analyzing Numerical Variables

17.2 Analyzing Categorical Variables

17.3 Introduction to Business Analytics

17.4 Descriptive Analytics

17.5 Predictive Analytics

18. Statistical Applications in Quality Management (online)

18.1 The Theory of Control Charts

18.2 Control Chart for the Proportion: The p Chart

18.3 The Red Bead Experiment: Understanding Process Variability

18.4 Control Chart for an Area of Opportunity: The c Chart

18.5 Control Charts for the Range and the Mean

18.6 Process Capability

18.7 Total Quality Managementice

18.8 Six Sigma

19. Decision Making (online)

19.1 Payoff Tables and Decision Trees

19.2 Criteria for Decision Making

19.3 Decision Making with Sample Information

19.4 Utility

Appendices

A. Basic Math Concepts and Symbols

A.1 Rules for Arithmetic Operations

A.2 Rules for Algebra: Exponents and Square Roots

A.3 Rules for Logarithms

A.4 Summation Notation

A.5 Statistical Symbols

A.6 Greek Alphabet

B. Important Excel and Minitab Skills and Concepts

B.1 Which Excel Do You Use?

B.2 Basic Operations

B.2 Formulas and Cell References

B.4 Entering a Formula

B.5 Formatting Cell Contents

B.6 Formatting Charts

B.7 Selecting Cell Ranges for Charts

B.8 Deleting the “Extra” Histogram Bar

B.9 Creating Histograms for Discrete Probability Distributions

C. Online Resources

C.1 About the Online Resources for This Book

C.2 Accessing the Online Resources

C.3 Details Online Resources

C.4 PHStat

D. Configuring Microsoft Excel

D.1 Getting Microsoft Excel Ready for Use

D.2 Checking for the Presence of the Analysis ToolPak or Solver Add-Ins

D.3 Configuring Microsoft Windows Excel Security Settings

D.4 Opening Pearson-Supplied Add-Ins

E. Tables

E.1 Table of Random Numbers

E.2 The Cumulative Standardized Normal Distribution

E.3 Critical Values of t

E.4 Critical Values of

E.5 Critical Values of F

E.6 Lower and Upper Critical Values, T1, of the Wilcoxon Rank Sum Test

E.7 Critical Values of the Studentized Range, Q

E.8 Critical Values, dI and dU, of the Durbin–Watson Statistic, D (Critical Values Are One-Sided)

E.9 Control Chart Factors

E.10 The Standardized Normal Distribution online

F. Useful Excel Knowledge

F.1 Useful Keyboard Shortcuts

F.2 Verifying Formulas and Worksheets

F.3 New Function Names

F.4 Understanding the Nonstatistical Functions

G. Software FAQs

G.1 PHStat FAQs

G.2 Microsoft Excel FAQs

Self-Test Solutions and Answers to Selected Even-Numbered Problems

Index

For more information, click here.

DSC Resources

Book and Resources for DSC Members

Comprehensive Repository of Data Science and ML Resources

Advanced Machine Learning with Basic Excel

Difference between ML, Data Science, AI, Deep Learning, and Statistics

Selected Business Analytics, Data Science and ML articles

Hire a Data Scientist | Search DSC | Find a Job

Post a Blog | Forum Questions

Link: Book: Statistics for Managers Using Microsoft Excel