1.1 Statistics and the decision-making process
1.2 Collecting data
1.3 Understanding data
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

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

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

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

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

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

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

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

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

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

11.1 Data sets

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

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 1,700 academic institutions have adopted web-native zyBooks to transform their STEM education.

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• Build quizzes and exams with included test questions

## Senior Contributors

Alan Bass
Senior Content Developer, Mathematics / MS in Mathematics, University of North Carolina, Wilmington

Heather Berrier
Content Developer, Mathematics / PhD in Physics and Astronomy, University of California, Irvine

Chris Chan
Director, Content Development / MA in Mathematics, San Francisco State University

Zoe Fox
Associate Content Developer, Statistics / BS in Mathematics and Statistics, University of British Columbia

Ayla SÃ¡nchez
Senior Content Developer, Statistics / PhD in Mathematics, Tufts University

## Contributors

Susan LauerÂ
Content Developer, Mathematics / PhD in Mathematics A,uburn University

Aimee Schwab-McCoyÂ
Manager, Content Development, Data Science and Statistics / PhD in Statistics, University of Nebraska-Lincoln