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| Statistics, Data, and Statistical Thinking | |
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| the Science of Statistics | |
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| Types of Statistical Applications | |
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| Fundamental Elements of Statistics | |
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| Types of Data | |
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| Collecting Data | |
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| the Role of Statistics in Critical Thinking | |
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| Methods for Describing Sets of Data | |
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| Describing Qualitative Data | |
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| Graphical Methods for Describing Quantitative Data | |
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| Summation Notation | |
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| Numerical Measures of Central Tendency | |
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| Numerical Measures of Variability | |
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| Interpreting the Standard Deviation | |
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| Numerical Measures of Relative Standing | |
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| Methods for Detecting Outliers: Box Plots and z-Scores | |
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| Graphing Bivariate Relationships (Optional) | |
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| Distorting the Truth with Descriptive Techniques | |
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| Probability | |
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| Events, Sample Spaces, and Probability | |
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| Unions and Intersections | |
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| Complementary Events | |
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| the Additive Rule and Mutually Exclusive Events | |
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| Conditional Probability | |
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| the Multiplicative Rule and Independent Events | |
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| Random Sampling | |
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| Some Additional Counting Rules (Optional) | |
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| Bayes' Rule (Optional) | |
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| Random Variables and Probability Distributions | |
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| Two Types of Random Variables | |
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| Probability Distributions for Discrete Random Variables | |
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| Expected Values of Discrete Random Variables | |
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| the Binomial Random Variable | |
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| Continuous Probability Distributions | |
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| the Normal Distribution | |
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| Descriptive Methods for Assessing Normality | |
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| Approximating a Binomial Distribution with a Normal Distribution (Optional) | |
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| What is a Sampling Distribution? | |
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| the Sampling Distribution of (x-bar) and the Central Limit Theorem | |
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| Inferences Based on a Single Sample: Estimation with Confidence Intervals | |
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| Identifying and Estimating the Target Parameter | |
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| Confidence Interval for a Population Mean: Normal (z) Statistic | |
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| Confidence Interval for a Population Mean: Student's t-statistic | |
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| Large-Sample Confidence Interval for a Population Proportion | |
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| Determining the Sample Size | |
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| Confidence Interval for a Population Variance (Optional) | |
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| Inferences Based on a Single Sample: Tests of Hypothesis | |
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| the Elements of a Test of Hypothesis | |
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| Formulating Hypotheses and Setting Up the Rejection Region | |
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| Test of Hypothesis About a Population Mean: Normal (z) Statistic | |
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| Observed Significance Levels: p-Values | |
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| Test of Hypothesis About a Population Mean: Student's t-statistic | |
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| Large-Sample Test of Hypothesis About a Population Proportion | |
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| Calculating Type II Error Probabilities: More About � (Optional) | |
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| Test of Hypothesis About a Population Variance (Optional) | |
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| Single Population Inferences | |
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| Comparing Population Means | |
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| Identifying the Target Parameter | |
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| Comparing Two Population Means: Independent Sampling | |
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| Comparing Two Population Means: Paired Difference Experiments | |
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| Determining the Sample Size | |
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| the Completely Randomized Design: Single Factor | |
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| Comparing Two Populations: Independent Samples | |
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| Comparing Two Populations: Paired Difference Experiment | |
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| Comparing Population Proportions | |
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| Categorical Data and the Multinomial Distribution | |
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| Testing Categorical Probabilities: One-Way Table | |
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| Testing Categorical Probabilities: Two-Way (Contingency) Table | |
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| A Word of Caution About Chi-Square Tests | |
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| Comparing Two Population Proportions: Independent Sampling | |
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| Determining the Sample Size | |
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| Simple Linear Regression | |
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| Probabilistic Models | |
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| Fitting the Model: the Least Squares Approach | |
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| Model Assumptions | |
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| Assessing the Utility of the Model: Making Inferences About the Slope �1 | |
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| the Coefficients of Correlation and Determination | |
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| Using the Model for Estimation and Prediction | |
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| A Complete Example | |
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| Rank Correlation | |