## Table of Contents

1. Descriptive Statistics

1.1 Statistics and the decision-making process

1.2 Collecting data

1.3 Understanding data

1.4 Data and spreadsheets

1.5 Introduction to data visualization

1.6 Bar charts

1.7 Measures of center

1.8 Histograms

1.9 Measures of variability

1.10 Relative measures

1.11 Box plots

1.12 Scatter plots

1.13 Line charts

1.14 Case study: Ten-year employment projections

2. Probability

Â Â 2.1 Introduction to probability

Â Â Â 2.2 Addition rule and complements

Â Â Â 2.3 Multiplication rule and independence

Â Â Â 2.4 Conditional probability

Â Â Â 2.5 Bayes’ Theorem

Â Â Â 2.6 Combinations and permutations

3. Discrete Probability Distributions

Â Â Â 3.1 Introduction to random variables

Â Â Â 3.2 Properties of discrete probability distributions

Â Â Â 3.3 Binomial distribution

Â Â Â 3.4 Poisson distribution

Â Â Â 3.5 Hypergeometric distribution

4. Continuous Probability Distributions

Â Â Â 4.1 Properties of continuous probability distributions

Â Â Â 4.2 Normal distribution

Â Â Â 4.3 Sampling distributions and the Central Limit Theorem

Â Â Â 4.4 Studentâ€™s t-Distribution

Â Â Â 4.5 F-distribution

Â Â Â 4.6 Chi-square distribution

5. Confidence Intervals

Â Â Â 5.1 Confidence intervals

Â Â Â 5.2 Confidence intervals for population means

Â Â Â 5.3 Confidence intervals for population proportions

Â Â Â 5.4 Case study: Self-employment

6. Hypothesis Testing

Â Â Â 6.1 Hypothesis testing

Â Â Â 6.2 Hypothesis tests for a population mean

Â Â Â 6.3 Hypothesis test for a population proportion

Â Â Â 6.4 Hypothesis tests for the difference between two population means

Â Â Â 6.5 Hypothesis test for the difference between two population proportions

Â Â Â 6.6 One-way analysis of variance (one-way ANOVA)

Â Â Â 6.7 Case study: Fluent company names

7. Linear Regression

Â Â Â 7.1 Introduction to linear regression

Â Â Â 7.2 Least squares method

Â Â Â 7.3 Linear regression assumptions

Â Â Â 7.4 Correlation and coefficient of determination

Â Â Â 7.5 Interpreting fitted models

Â Â Â 7.6 Confidence and prediction intervals

Â Â Â 7.7 Testing linear regression parameters

Â Â Â 7.8 Case study: Activity levels for parents and children

8. Multiple Linear Regression

Â Â Â 8.1 Introduction to multiple regression

Â Â Â 8.2 Multiple regression assumptions and diagnostics

Â Â Â 8.3 Coefficient of multiple determination

Â Â Â 8.4 Multicollinearity

Â Â Â 8.5 Interpreting multiple regression models

Â Â Â 8.6 Confidence and prediction intervals for MLR models

Â Â Â 8.7 Testing multiple regression parameters

Â Â Â 8.8 Case study: Cars

9. Chi-square Tests for Categorical Data

Â Â Â 9.1 Categorical data

Â Â Â 9.2 Fisher’s exact test

Â Â Â 9.3 Introduction to chi-square tests

Â Â Â 9.4 Chi-square test for homogeneity and independence

Â Â Â 9.5 Case study: Flu Vaccines

10. Appendix A: Distribution Tables

Â Â Â 10.1 t-distribution table

Â Â Â 10.2 z-distribution table

Â Â Â 10.3 Chi-squared distribution table

11. Appendix B: CSV Files

11.1 Data sets

12. Appendix C: Applets

Â Â Â 12.1 Graphing and descriptive statistics

Â Â Â 12.2 Discrete probability distributions

Â Â Â 12.3 Continuous probability distributions

Â Â Â 12.4 Sampling distributions

Â Â Â 12.5 Hypothesis tests for one population

Â Â Â 12.6 Hypothesis tests for two populations

13. Appendix D: Excel

Â Â Â 13.1 Excel formulas

Â Â Â 13.2 Data Analysis Toolpak

## Provide an interactive introduction to the growing field of statistics with zyBooks

**Statistics for Decision Making** covers descriptive and inferential techniques, probability theory, and prediction using linear models.

- Examples using Microsoft Excel are provided to build practical skills in statistics
- Interactive applets are used to aid conceptual understanding
- Participation activities and challenge activities use real-world datasets to illustrate statistical techniques
- Case studies exemplify the statistical analysis process from start to finish

## What is a zyBook?

** Statistics for Decision MakingÂ **is a web-native, interactive zyBook that helps students visualize concepts to learn faster and more effectively than with a traditional textbook. (Check out our research.)

Since 2012, over 1,200 universities and colleges across the country have adopted web-native zyBooks to transform their STEM education.

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## Senior Contributors

**Alan Bass**

*Senior Content Developer, Mathematics / M.S. in Mathematics / University of North Carolina Wilmington*

**Heather Berrier**

*Content Developer, Mathematics / Ph.D. in Physics and Astronomy / University of California, Irvine*

**Chris Chan**

*Director, Content Development / M.A. in Mathematics / San Francisco State University*

**Zoe Fox**

*Associate Content Developer, Statistics / B.S. in Mathematics and Statistics / University of British Columbia*

**Ayla SÃ¡nchez**

*Senior Content Developer, Statistics / Ph.D. in Mathematics / Tufts University*

## Contributors

**Susan Lauer***Â *

*Content Developer, Mathematics / Ph.D. in Mathematics / Auburn University*

**Aimee Schwab-McCoy***Â *

*Manager, Content Development, Data Science and Statistics / Ph.D. in Statistics / University of Nebraska-Lincoln*