Part One: DESCRIPTIVE STATISTICS. 1. Statistics. Chapter Case Study. What Is Statistics? Introduction to Basic Terms. Measurability and Variability. Data Collection. Comparison of Probability and Statistics. Statistics and Technology. Chapter Summary. 2. Descriptive Analysis and Presentation of Single-Variable Data. Chapter Case Study. Graphs, Pareto Diagrams, and Stem-and-Leaf Displays. Frequency Distributions and Histograms. 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. Chapter Summary. 3. Descriptive Analysis and Presentation of Bivariate Data. Chapter Case Study. Bivariate Data. Linear Correlation. Linear Regression. Chapter Summary. Part Two: PROBABILITY. 4. Probability. Chapter Case Study. The Nature of Probability. Probability of Events. Simple Sample Spaces. Rules of Probability. Mutually Exclusive Events and the Addition Rule. Independence, the Multiplication Rule, and Conditional Probability. Combining the Rules of Probability. Chapter Summary. 5. Probability Distributions (Discrete Variables). Chapter Case Study. 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. Chapter Summary. 6. Normal Probability Distributions. Chapter Case Study. Normal Probability Distributions. The Standard Normal Distribution. Applications of Normal Distributions. Notation. Normal Approximation of the Binomial. Chapter Summary. 7. Sample Variability. Chapter Case Study. Sampling Distributions. The Central Limit Theorem. Application of the Central Limit Theorem. Chapter Summary. Part Three: INFERENTIAL STATISTICS. 8. Introduction to Statistical Inferences. Chapter Case Study. The Nature of Estimation. Estimation of Mean µ (ó Known). The Nature of Hypothesis Testing. Hypothesis Test of Mean µ (ó Known): A Probability-Value Approach. Hypothesis test of Mean µ (ó Known): A Classical Approach. Chapter Summary. 9. Inferences Involving One Population. Chapter Case Study. Inferences About Mean µ (ó Unknown). Inferences About the Binomial Probability of Success. Inferences About Variance and Standard Deviation. Chapter Summary. 10. Inferences Involving Two Populations. Chapter Case Study. Independent and Dependent 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 Variance Using Two Independent Samples. Chapter Summary. Part Four: MORE INFERENTIAL STATISTICS. 11. Applications of Chi-Square. Chapter Case Study. Chi-Square Statistic. Inferences Concerning Multinomial Experiments. Inferences Concerning Contingency Tables. Chapter Summary. 12. Analysis of Variance. Chapter Case Study. Introduction to the Analysis of Variance Technique. The Logic Behind ANOVA. Applications of Single-Factor ANOVA. Chapter Summary. 13. Linear Correlation and Regression Analysis. Chapter Case Study. Linear Correlation Analysis. Inferences About the Linear Correlation Coefficient. Linear Regression Analysis. Inferences Concerning the Slope of the Regression Line. Confidence Interval Estimates for Regression. Understanding the Relationship Between Correlation and Regression. Chapter Summary. 14. Elements of Nonparametric Statistics. Chapter Case Study. Nonparametric Statistics. Comparing Statistical Tests. The Sign Test. The Mann-Whitney U Test. The Runs Test. Rank Correlation. Chapter Summary. References. Appendix A: Data Sets. Appendix B: Tables. Binomial Probabilities. Probabilities for the Standard Normal Distribution. Critical Values of Students'' t Distribution. Critical values of the Chi-Square Dis