STAT

Part I: DESCRIPTIVE STATISTICS. 1. Statistics. What is Statistics? Measurability and Variability. Data Collection. Comparison of Probability and Statistics. Statistics and Technology. 2. Descriptive Analysis and Presentation of Single-Variable Data. Graphical 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. Measures of Position. Interpreting and Understanding Standard Deviation. The Art of Statistical Deception. Mean and Standard Deviation of Frequency Distribution (Optional). 3. Descriptive Analysis and Presentation of Bivariate Data. Bivariate Data. Linear Correlation. Linear Regression. Part II: PROBABILITY. 4. Probability. Probability of Events. Conditional Probability of Events. Rules of Probability. Mutually Exclusive Events. Independent Events. Mutually Exclusive, Independent Events - A Relationship? 5. Probability Distributions (Discrete Variables). Random Variables. Probability Distribution 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. 6. Normal Probability Distributions. Normal Probability Distributions. The Standard Normal Distribution. Applications of Normal Distributions. Notation. Normal Approximation of the Binomial. 7. Sample Variability. Sampling Distributions. The Sampling Distribution of Sample Means. Application of the Sampling Distribution of Sample Means. Part III: INFERENTIAL STATISTICS. 8. Introduction to Statistical Inferences. The Nature of Estimation. Estimation of a Mean ? (? known). The Nature of Hypothesis Testing. Hypothesis Test of Mean ? (? Known): A Probability Value Approach. Hypothesis Test of Mean ? (? Known): A Classical Approach. 9. Inferences Involving One Population. Inferences About Mean ? (? Unknown). Inferences About the Binomial Probability of Success. Inferences About Variance and Standard Deviation. 10. Inferences Involving Two Populations. 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 Variances Using Two Independent Samples. Part IV: MORE INFERENTIAL STATISTICS. 11. Applications of Chi-Square. Chi-Square Statistic. Inferences Concerning Multinomial Experiments. Inferences Concerning Contingency Tables. 12. Analysis of Variance. Introduction to the Analysis of Variance Technique. The Logic Behind ANOVA. Applications of Single-Factor ANOVA. 13. Linear Correlation and Regression. 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. 14. Elements of Nonparametric Statistics. Nonparametric Statistics. Comparing Statistical Tests. The Sign Test. The Mann-Whitney U Test. The Runs Test. Rank Correlation.