Machine derived contents note: Part 1: DESCRIPTIVE STATISTICS. 1. Statistics. Chapter Case Study: Americans, Here''s Looking at You. What Is Statistics? Introduction to Basic Terms. Measurability and Variability. Data Collection. Comparison of Probability and Statistics. Statistics and Technology. Return to Chapter Case Study. 2. Descriptive Analysis and Presentation of Single-Variable Data. Chapter Case Study: What Do People Do When They Are on the Internet? Graphic Presentation of Data. Graphs, Pareto Diagrams, and Stem-and-Leaf Displays. Frequency Distributions and Histograms. Numerical Descriptive Statistics. Measures of Central Tendency. Measures of Dispersion. Mean and Standard Deviation of Frequency Distribution. Measures of Position. Interpreting and Understanding Standard Deviation. The Art of Statistical Deception. Return to Chapter Case Study. 3. Descriptive Analysis and Presentation of Bivariate Data. Chapter Case Study: Duncan Wins First MVP. Bivariate Data. Linear Correlation. Linear Regression. Return to Chapter Case Study. Part 2: PROBABILITY. 4. Probability. Chapter Case Study: Statistics Students'' Favorite Candy. Concepts of Probability. The Nature of Probability. Probability of Events. Simple Sample Spaces. Rules of Probability. Calculating Probabilities of Compound Events. Mutually Exclusive Events and the Addition Rule. Independence, the Multiplication Rule, and Conditional Probability. Combining the Rules of Probability. Return to Chapter Case Study. 5. Probability Distributions (Discrete Variables). Chapter Case Study: Family Values and Family Togetherness. Random Variables. Probability Distributions of a Discrete Random Variable. Mean and Variance of a Discrete Probability Distribution. The Binomial Probability Distribution. Mean and Standard Deviation of the Binomial Distribution. Return to Chapter Case Study. 6. Normal Probability Distributions. Chapter Case Study: Aptitude Tests and Their Interpretation. Normal Probability Distributions. The Standard Normal Distributions. Applications of Normal Distributions. Notation. Normal Approximation of the Binomial. Return to Chapter Case Study. 7. Sample Variability. Chapter Case Study: The U.S. Census and Sampling It. Sampling Distributions. The Sampling Distribution of Sample Means. Application of the Sampling Distribution of Sample Means. Return to Chapter Case Study. Part 3: INFERENTIAL STATISTICS. 8. Introduction to Statistical Inferences. Chapter Case Study: Were They Shorter Back Then? The Nature of Estimation. Estimation of Mean µ (s Known). The Nature of Hypothesis Testing. Hypothesis Test of Mean µ (s Known): A Probability-Value Approach. Hypothesis Test of Mean µ (s Known): A Classical Approach. Return to Chapter Case Study. 9. Inferences Involving One Population. Chapter Case Study: Get Enough Daily Exercise? Inferences About Mean µ (s Unknown). Inferences About the Binomial Probability of Success. Inferences About the Variance and Standard Deviation. Return to Chapter Case Study. 10. Inferences Involving Two Populations. Chapter Case Study: Students, Credit Cards, and Debt. Dependent and Independent Samples. Inferences Concerning the Mean Difference Using Two Dependent Samples. Inferences Concerning the Difference Between Means Using Two Independent Samples. Inferences Concerning the Difference Between Proportions Using Two Independent Samples. Inferences Concerning the Ratio of Variances Using Two Independent Samples. Return to Chapter Case Study. 11. Ap.
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