1.1 Measures of center
1.3 Histograms
1.4 Experiments and events
1.5 Discrete random variables and their distributions
1.6 Properties of discrete probability distributions
1.7 Continuous probability distributions and properties
1.8 Specific distributions
1.9 Hypotheses
1.10 Hypothesis testing
1.11 Comparing two population means
1.12 Confidence intervals

2.1 Parameterized population models
2.2 Student’s t-test
2.3 Comparing 2 samples: 2-sample t-test
2.4 Comparing 3+ samples: ANOVA
2.5 Linear regression

3.1 Parametric vs. nonparametric statistics
3.2 Resampling: Randomization and bootstrapping
3.3 Wilcoxon rank-sum test
3.4 Kruskal-Wallis test
3.5 Multiple tests

4.1 Comparing samples having categorical data
4.2 2 samples, 2 categories: Fisher’s exact test
4.3 2 samples, 2 categories, large sample: Chi-square test
4.4 3+ samples and/or 3+ categories: Chi-square test
4.5 2 samples, 2 categories: Relative risk and odds ratios

5.1 Introduction to PCA
5.2 Calculating principal components for two variables
5.3 Extending PCA to more variables
5.4 Determining the number of components
5.5 Interpreting principal components

6.1 The multiple linear regression model
6.2 Model parameter estimation and testing
6.3 Regression diagnostics
6.4 Model interpretation
6.5 Categorical predictors
6.6 Transformations and Interactions
6.7 Multiple linear regression example

7.1 Introduction to the logistic regression model
7.2 Parameter estimation
7.3 Probability estimates
7.4 Inference 1: Wald tests
7.5 Inference 2: LR tests and AIC
7.6 Interpretation
7.7 Assessing fit

## What You’ll Find In This zyBook:

### More action with less text.

• An exceptionally student-focused coverage of statistics for data analytics
• Traditionally-hard topics are made learnable via hundreds of animations and learning questions
• Included background enables all students to succeed
• Commonly combined with “Fundamentals of Data Analytics“; numerous configurations possible

## The zyBooks Approach

### Less text doesn’t mean less learning.

Data analytics is one of the fastest growing subjects today and is useful in nearly all fields. The subject’s topics, with their underlying statistics, often pose difficulty for students. This zyBook represents entirely new material created specifically to help students master the subject. Written natively for the modern web, the zyBook teaches through hundreds of animations and learning questions in addition to concise, lucid text and figures.

The zyBook introduces intermediate techniques for data analytics, including non-parametric techniques, categorical analysis, principal component analysis, multiple regression, and logistic regression. The background statistics and parametric analysis chapters help students hit the ground running, even if they haven’t taken a statistics course in years.

Instructors can see student activity completion, can reconfigure the topics, and can even combine with other zyBooks. A common combination is with our “Fundamentals of Data Analytics” zyBook that covers introductory topics. Both zyBooks are appropriate for undergraduate and graduate courses.