| A. Constructing and interpreting graphical displays of distributions of univariate data (dotplot, stemplot, histogram, cumulative frequency plot) |
| 1. Center and spread |
2.3, 2.4 |
| 2. Clusters and gaps |
2.2 |
| 3. Outliers and other unusual features |
2.3, 2.5 |
| 4. Shape |
2.2 |
| B. Summarizing distributions of univariate data |
| 1. Measuring center: median, mean |
2.3 |
| 2. Measuring spread: range, interquartile range, standard deviation |
2.4,2.5 |
| 3. Measuring position: quartiles, percentiles, standardized scores (z-scores) |
2.5 |
| 4. Using boxplots |
2.5 |
| 5. The effect of changing units on summary measures |
|
| C. Comparing distributions of univariate data (dotplots, back-to back stemplots, parallel boxplots) |
| 1. Comparing center and spread: within group, between group variation |
Ch. 2 |
| 2. Comparing clusters and gaps |
Ch. 2 |
| 3. Comparing outliers and other unusual features |
Ch. 2 |
| 4. Comparing shapes |
Ch. 2 |
| D. Exploring bivariate data |
| 1. Analyzing patterns in scatterplots |
3.2 |
| 2. Correlation and linearity |
3.2 |
| 3. Least-squares regression line |
3.3 |
| 4. Residual plots, outliers, and influential points |
3.3, 3.4 |
| 5. Transformations to achieve linearity: logarithmic and power transformations |
11.5 |
| E. Exploring categorical data |
| 1. Frequency tables and bar charts |
3.1 |
| 2. Marginal and joint frequencies for two-way tables |
3.1 |
| 3. Conditional relative frequencies and association |
3.1 |
| 4. Comparing distributions using bar charts |
3.1 |
| A. Overview of methods of data collection |
| 1. Census |
4.1 |
| 2. Sample survey |
4.1 |
| 3. Experiment |
4.1 |
| 4. Observational study |
4.1 |
| B. Planning and conducting surveys |
| 1. Characteristics of a well-designed and well-conducted survey |
4.2 |
| 2. Populations, samples, and random selection |
4.2 |
| 3. Sources of bias in surveys |
4.2 |
| 4. Sampling methods, including simple random sampling, stratified random sampling, and cluster sampling |
4.4 |
| C. Planning and conducting experiments |
| 1. Characteristics of a well-designed and well-conducted experiment |
4.3 |
| 2. Treatments, control groups, experimental units, random assignments, and replication |
4.3 |
| 3. Sources of bias and confounding, including placebo effect and blinding |
4.2, 4.3 |
| 4. Completely randomized design |
4.4 |
| 5. Randomized block design, including matched pairs design |
4.4 |
| D. Generalizability of results and types of conclusions that can be drawn from observational studies, experiments, and surveys |
Ch.4 |
| A. Probability |
| 1. Interpreting probability, including long-run relative frequency interpretation |
5.1 |
| 2. "Law of large numbers" concept |
5.1 |
| 3. Addition rule, multiplication rule, conditional probability, and independence |
5.2, 5.3 |
| 4. Discrete random variables and their probability distributions, including binomial and geometric |
6.1, 6.3 |
| 5. Simulation of random behavior and probability distributions |
5.4 |
| 6. Mean (expected value) and standard deviation of a random variable, and linear transformation of a random variable |
6.1 |
| B. Combining independent random variables |
| 1. Notion of independence versus dependence |
9.1, 5.2 |
| 2. Mean and standard deviation for sums and differences of independent random variables |
|
| C. The normal distribution |
| 1. Properties of the normal distribution |
6.2 |
| 2. Using tables of the normal distribution |
6.2 |
| 3. The normal distribution as a model for measurements |
6.2 |
| D. Sampling distributions |
| 1. Sampling distribution of a sample proportion |
6.4 |
| 2. Sampling distribution of a sample mean |
6.5 |
| 3. Central Limit Theorem |
6.5 |
| 4. Sampling distribution of a difference between two independent sample proportions |
9.1 |
| 5. Sampling distribution of a difference between two independent sample means |
9.2 |
| 6. Simulation of sampling distributions |
5.4 |
| 7. t-distribution |
7.3 |
| 8. Chi-square distribution |
10.2 |
| A. Estimation (point estimators and confidence intervals) |
| 1. Estimating population parameters and margins of error |
7.1 |
| 2. Properties of point estimators, including unbiasedness and variability |
7.1 |
| 3. Logic of confidence intervals, meaning of confidence level and confidence intervals, and properties of confidence intervals |
7.1 |
| 4. Large sample confidence interval for a proportion |
7.2 |
| 5. Large sample confidence interval for a difference between two proportions |
9.1 |
| 6. Confidence interval for a mean |
7.3 |
| 7. Confidence interval for a difference between two means (unpaired and paired) |
9.2 |
| 8. Confidence interval for the slope of a least-squares regression line |
11.4 |
| B. Tests of significance |
| 1. Logic of significance testing, null and alternative hypotheses; p-values; one- and two-sided tests; concepts of Type I and Type II errors; concept of power |
8.1, 8.4, 8.6 |
| 2. Large sample test for a proportion |
8.2 |
| 3. Large sample test for a difference between two proportions |
9.1 |
| 4. Test for a mean |
8.3 |
| 5. Test for a difference between two means (unpaired and paired) |
9.2 |
| 6. Chi-square test for goodness of fit, homogeneity of proportions, and independence (one- and two-way tables) |
10.2 |
| 7. Test for the slope of a least-squares regression line |
11.3 |
