Statistics Additional
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 |
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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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