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Written by two of the most respected leaders in statistics education, JUST THE ESSENTIALS OF ELEMENTARY STATISTICS, Third Edition, is the "essentials" version of Johnson and Kuby's market-leading Eighth Edition of ELEMENTARY STATISTICS. This version is tailored to students who need a working knowledge of statistics, but do not have a strong background in mathematics. The text promotes learning, understanding, and motivation by presenting statistics in a context that relates to personal experiences. Statistics is presented as a useful tool in understanding the world around us through real world applications in areas such as business, economics, engineering, and the physical and natural sciences.
Part 1: DESCRIPTIVE STATISTICS. 1. Statistics. Chapter Case Study: Americans, Heres 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 Distribuation of Sample Means. Application of the Sampling Distribuation 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 (o Known). The Nature of Hypothesis Testing. Hypothesis Test of Mean µ (σ Known): A Probability-Value Approach. Hypothesis test of Mean µ (σ 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 (σ Known). 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. Part 4: MORE INFERENTIAL STATISTICS. 11. Applications of Chi-Square. Chapter Case Study: Cooling Your Mouth After a Great Hot Taste. Chi-Square Statistic. Inferences Concerning Multinomial Experiments. Inferences Concerning Contingency Tables. Return to Chapter Case Study. 12. Analysis of Variance. Chapter Case Study: Time Spent Reading the Newspaper. Introduction to the Analysis of Variance Technique. The Logic Behind ANOVA. Applications of Single-Factor ANOVA. Return to Chapter Case Study. 13. Linear Correlation and Regression Analysis. Chapter Case Study: Wheat! Beautiful Golden Wheat! 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. Return to Chapter Case Study. 14. Elements of Nonparametric Statistics. Chapter Case Study: Teenagers Attitudes. Nonparametric Statistics. Comparing Statistical Tests. The Sign Test. The Mann-Whitney U Test. The Runs Test. Rank Correlation. Return to Chapter Case Study. Appendices. Appendix A: Basic Principles of Counting. Appendix B: Tables. Answers to Selected Exercises. Answers to Chapter Practice Tests. Computer and Calculator Instructions. Index. Credits. Formula Card.