- David M. Levine
- David F. Stephan
- Kathryn A. Szabat
- 9781292338248
- 9th edition | Published by Pearson - Copyright © 2021

*courses in Introduction to Business Statistics.*

**The gold standard in learning Microsoft Excel ^{ }for business statistics**

*Statistics for Managers Using Microsoft*^{®}* Excel*^{®}*, ***9th Edition, Global Edition **helps students develop the knowledge of Excel needed in future careers. The authors present statistics in the context of specific business fields, and now include a full chapter on business analytics. Guided by principles set forth by ASA’s Guidelines for Assessment and Instruction (GAISE) reports and the authors’ diverse teaching experiences, the text continues to innovate and improve the way this course is taught to students. Current data throughout gives students valuable practice analyzing the types of data they will see in their professions, and the authors’ friendly writing style includes tips and learning aids throughout.

Preface

**First Things First**

FTF.1 Think Differently About Statistics

FTF.2 Business Analytics: The Changing Face of Statistics

FTF.3 Starting Point for Learning Statistics

FTF.4 Starting Point for Using Software

FTF.5 Starting Point for Using Microsoft Excel

**1. Defining and Collecting Data**

1.1 Defining Variables

1.2 Collecting Data

1.3 Types of Sampling Methods

1.4 Data Cleaning

1.5 Other Data Preprocessing Tasks

1.6 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 a Mix of Variables

2.7 Visualizing a Mix of Variables

2.8 Filtering and Querying Data 73

2.9 Pitfalls in Organizing and Visualizing Variables

**3. Numerical Descriptive Measures**

3.1 Measures of Central Tendency

3.2 Measures of Variation and Shape

3.3 Exploring Numerical Variables

3.4 Numerical Descriptive Measures for a Population

3.5 The Covariance and the Coefficient of Correlation

3.6 Descriptive 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 Distribution**s

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

7.4 Sampling from Finite Populations

**8. Confidence Interval Estimation**

8.1 Confidence Interval Estimate for the Mean (σ Known)

8.2 Confidence Interval Estimate for the Mean (σ 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 Determination for Finite Populations

8.8 Bootstrapping

**9. Fundamentals of Hypothesis Testing: One-Sample Tests**

9.1 Fundamentals of Hypothesis Testing

9.2 t Test of Hypothesis for the Mean (σ 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 One-Way ANOVA

11.2 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 for Two Independent Populations

12.5 Kruskal-Wallis Rank Test 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

12.8 Wilcoxon Signed Ranks Test for Two Related Populations

**13. Simple Linear Regression**

13.1 Simple Linear 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 Slope and 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 Evaluating Multiple Regression Models

14.3 Multiple Regression Residual Analysis

14.4 Inferences About the Population Regression Coefficients

14.5 Testing Portions of the Multiple Regression Model

14.6 Using Dummy Variables and Interaction Terms

14.7 Logistic Regression

14.8 Cross-Validation

**15. Multiple Regression Model Building**

15.1 The 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 Time-Series Component Factors

16.2 Smoothing an Annual Time Series

16.3 Least-Squares Trend Fitting and Forecasting

16.4 Autoregressive Modeling for Trend Fitting and Forecasting

16.5 Choosing an Appropriate Forecasting Model

16.6 Time-Series Forecasting of Seasonal Data

16.7 Index Numbers

**17. Business Analytics**

17.1 Business Analytics Overview

17.2 Descriptive Analytics

17.3 Decision Trees

17.4 Clustering

17.5 Association Analysis

17.6 Text Analytics

17.7 Prescriptive Analytics

**18. Getting Ready to Analyze Data in the Future**

18.1 Analyzing Numerical Variables

18.2 Analyzing Categorical Variables

**19. Statistical Applications in Quality Management (online)**

19.1 The Theory of Control Charts

19.2 Control Chart for the Proportion: The p Chart

19.3 The Red Bead Experiment: Understanding Process Variability

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

19.5 Control Charts for the Range and the Mean

19.6 Process Capability

19.7 Total Quality Management

19.8 Six Sigma

**20. Decision Making**

20.1 Payoff Tables and Decision Trees

20.2 Criteria for Decision Making

20.3 Decision Making with Sample Information

20.4 Utility

Appendices