Description: 1. What Is Statistics? Introduction to Statistics. Types of Data. 2. Data Reduction: Descriptive Representations. Frequency Distributions. Frequency Curves. Other Graphical and Pictorial Representations. Exploratory Data Analysis. Sample Computer Output. 3. Numerical Summary Measures. Populations, Samples, and Summary Measures. Measures of Central Location. Measures of Variability or Dispersion. Sample Computer Output. Some Uses of the Mean and Standard Deviation. Measures of Central Location and Dispersion from Grouped Data. 4. Probability. Probabilities and Events. Assigning Probabilities to Events. Probabilities for Combined Events. Bayes' Theorem. 5. Random Variables. Random Variables and Their Distributions. Some Discrete Probability Distributions. Continuous Probability Distributions. Sample Computer Output. 6. Sampling Distributions. Sampling Procedures. Sampling Distributions. Distribution of Sample Means. 7. Statistical Inference: Confidence Intervals. Confidence Interval Estimates for One Population Mean, m. Determining Sample Size for Estimating m. Confidence Interval Estimates for the Difference Between Two Population Means m1-m2. Paired Observations. Confidence Interval Estimates for the Difference Between Two Population Proportions p1-p2. Confidence Interval Estimates for Population Variance. Confidence Interval Estimates for the Ratio of Two Population Variances. 8. Statistical Inference: Testing Hypotheses. The Basics of Hypothesis. Testing. Test of Hypothesis: One Population Mean. Test of Hypothesis: One Population Proportion. Test of Hypothesis: Two Population Means (Independent Samples). Test of Hypothesis: Two Population Proportions (Independent Samples). Test of Hypothesis: One Population Variance. Test of Hypothesis: Two Population Variances. 9. Analysis of Variance. Introduction: Background of the Herzog Problem. One Way Analysis of Variance (ANOVA). Comparing Pairs of Means. Two-Way Analysis of Variance: Herzog Revisited. Sample Computer Output. 10. The Regression and Correlation Models. Introduction to Linear Regression. The Fitted Regression Line and Its Mathematics. Fitting a Regression Line. Standard Deviation and Residuals. The Coefficient of Determination, r2: Another Measure of Regression Line Fit. Regression Model. Inferences about the Population Regression Coefficient. Confidence Interval for mYXo, the Population Mean of Y at Xo. Prediction Interval of a New Y Value at Xo. Comparing Confidence Intervals and Prediction Intervals. Sample Computer Output. Limitations and Cautions When Performing Regression Analysis. Correlation Analysis. Inferences about the Population Correlation Coefficient. 11. Multiple Regression and Modeling. Introduction. Multiple Regression Equation. Fitting the Multiple Regression. Equation. Inferences in Multiple Regression. Regression Modeling Residual Analysis: Checking Model Adequacy. Other Modeling Issues. 12.

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