Statistics Book Cover

## Textbook Resources

### TEACHER RESOURCES

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Chapter 1: Representing Data
 Section 1.1 1.1.1 Visualizing Information 1.1.2 Histograms and Stem-and-Leaf Plots 1.1.3 Types of Data and Variables Section 1.2 1.2.1 Choosing Mean or Median 1.2.2 Variance and Standard Deviation 1.2.3 Sample Variance and Sample Standard Deviation 1.2.4 Investigating Data Representations Section 1.3 1.3.1 Percentiles 1.3.2 Z-Scores 1.3.3 Linear Transformations

Chapter 2: Two-Variable Quantitative Data
 Section 2.1 2.1.1 Scatterplots and Association 2.1.2 Line of Best Fit 2.1.3 Residuals 2.1.4 The Least Squares Regression Line 2.1.5 Using Technology to Find the LSRL Section 2.2 2.2.1 The Correlation Coefficient 2.2.2 Behavior of Correlation and the LSRL 2.2.3 Residual Plots 2.2.4 Association is Not Causation 2.2.5 Interpreting Correlation in Context

Chapter 3: Multivariable Categorical Data
 Section 3.1 3.1.1 Probabilities and Two-Way Frequency Tables 3.1.2 Association and Conditional Relative Frequency Tables 3.1.3 Probability Notation 3.1.4 Relative Frequency Tables and Conditional Probabilities 3.1.5 Analyzing False Positives Section 3.2 3.2.1 Probability Trees 3.2.2 Problem Solving with Categorical Data 3.2.3 Simulations of Probability

Chapter 4: Studies and Experiments
 Section 4.1 4.1.1 Survey Design I 4.1.2 Samples and the Role of Randomness 4.1.3 Sampling When Random is Not Possible 4.1.4 Observational Studies and Experiments 4.1.5 Survey Design II (optional) Section 4.2 4.2.1 Cause and Effect with Experiments 4.2.2 Experimental Design I 4.2.3 Experimental Design II 4.2.4 Experimental Design III

Chapter 5: Density Functions and Normal Distributions
 Section 5.1 5.1.1 Relative Frequency Histograms and Random Variables 5.1.2 Introduction to Density Functions 5.1.3 The Normal Probability Density Function Section 5.2 5.2.1 The Inverse Normal Function 5.2.2 The Standard Normal Distribution and Z-Scores 5.2.3 Additional Practice Problems

Chapter 6: Discrete Probability Distributions
 Section 6.1 6.1.1 Mean and Variance of a Discrete Random Variable 6.1.2 Linear Combinations of Independent Random Variables 6.1.3 Exploring the Variability of X – X Section 6.2 6.2.1 Introducing the Binomial Setting 6.2.2 Binomial Probability Density Function 6.2.3 Exploring Binomial pdf and cdf 6.2.4 Shape, Center, and Spread of the Binomial Distribution 6.2.5 Normal Approximation to the Binomial Distribution Section 6.3 6.3.1 Introduction to the Geometric Distribution 6.3.2 Binomial and Geometric Practice

Chapter 7: Variability in Categorical Sampling
 Section 7.1 7.1.1 Introduction to Sampling Distributions 7.1.2 Simulating Sampling Distributions of Sample Proportions 7.1.3 Formulas for the Sampling Distributions of Sample Proportions Section 7.2 7.2.1 Confidence Interval for a Population Proportion 7.2.2 Confidence Levels for Confidence Intervals 7.2.3 Changing the Margin of Error in Confidence Intervals 7.2.4 Evaluating Claims with Confidence Intervals

Chapter 8: Drawing Conclusions with Categorical Data
 Section 8.1 8.1.1 Introduction to Hypothesis Testing 8.1.2 Hypothesis Tests for Proportions 8.1.3 Alternative Hypotheses and Two-Tailed Tests Section 8.2 8.2.1 Types of Errors in Hypothesis Testing 8.2.2 Power of a Test Section 8.3 8.3.1 The Difference Between Two Proportions 8.3.2 Two-Sample Proportion Hypothesis Tests 8.3.3 More Proportion Inference

Chapter 9: Chi-Squared Inference Procedures
 Section 9.1 9.1.1 Introduction to the Chi-Squared Distribution 9.1.2 Chi-Squared Goodness of Fit 9.1.3 More Applications of Chi-Squared Goodness of Fit Section 9.2 9.2.1 Chi-Squared Test for Independence 9.2.2 Chi-Squared Test for Homogeneity of Proportions 9.2.3 Practicing and Recognizing Chi-Squared Inference Procedures

Chapter 10: Drawing Conclusions With Quantitative Data
 Section 10.1 10.1.1 Quantitative Sampling Distributions 10.1.2 More Sampling Distributions Section 10.2 10.2.1 The Central Limit Theorem 10.2.2 Using the Normal Distribution with Means Section 10.3 10.3.1 Introducing the t-Distribution 10.3.2 Calculating Confidence Intervals for μ 10.3.3 z-Tests and t-Tests for Population Means

Chapter 11: Comparing Means and Identifying Tests
 Section 11.1 11.1.1 Paired and Independent Data from Surveys and Experiments 11.1.2 Paired Inference Procedures 11.1.3 Tests for the Difference of Two Means 11.1.4 Two-Sample Mean Inference with Experiments and Two-Sample Confidence Intervals Section 11.2 11.2.1 Inference in Different Situations 11.2.2 Identifying and Implementing an Appropriate Test

Chapter 12: Inference for Regression
 Section 12.1 12.1.1 Sampling Distribution of the Slope of the Regression Line 12.1.2 Inference for the Slope of the Regression Line Section 12.2 12.2.1 Transforming Data to Achieve Linearity 12.2.2 Using Logarithms to Achieve Linearity

Chapter 13: ANOVA and Beyond!
 Section 13.1 13.1.1 Modeling With the Chi-Squared Distribution 13.1.2 Introducing the F-Distribution Section 13.2 13.2.1 One-Way ANOVA Section 13.3 13.3.1 Sign Test: Introduction to Nonparametric Inference 13.3.2 Mood’s Median Test

### RESOURCE PAGES

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Chapter 1: Representing Data
Chapter 2: Two-Variable Quantitative Data
Chapter 3: Multivariable Categorical Data
Chapter 4: Studies and Experiments
Chapter 5: Density Functions and Normal Distributions

Chapter 6: Discrete Probability Distributions
Chapter 7: Variability in Categorical Sampling

Chapter 8: Drawing Conclusions with Categorical Data

Chapter 9: Chi-Squared Inference Procedures

Chapter 10: Drawing Conclusions with Quantitative Data
Chapter 11: Comparing Means and Identifying Tests

Chapter 12: Inference for Regression
Chapter 13: ANOVA and Beyond!

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