Machine generated contents note: 1.Introduction -- Fundamental Questions -- Question 1 Are Two Variables Related? -- Question 2 What Is the Direction of the Relationship Between Two Variables? -- Question 3 How Strong Is the Relationship Between Two Variables? -- Statistical Models -- Measuring the Error of the Model -- Model Comparison -- Summary -- pt. I FOUNDATIONS OF THE GENERAL LINEAR MODEL -- 2.Predicting Scores: The Mean and the Error of Prediction -- The Data -- The Model -- Error of the Model -- Counting Errors -- Sum of the Absolute Errors -- Sum of the Squared Errors -- Conceptual Versus Computational Formula -- Variance and Standard Deviation -- Another Example -- A Preview of Model Comparison -- Summary -- 3.Bivariate regression -- Bivariate Regression -- The Bivariate Regression Coefficient -- Calculate the Regression Coefficient -- Graph the Relationship Between X and Y -- Estimating the Error of the Model -- Centering the Predictor Variable: Mean Deviation of X --

Contents note continued: Summary -- 4.Model Comparison: The Simplest Model Versus a Regression Model -- Model Comparison -- 10 Steps to Compare Models -- Step 1 State the compact Model C and an Augmented Model A -- Step 2 Identify the Null Hypothesis (H0) -- Step 3 Count the Number of Parameters Estimated for Each Model -- Step 4 Calculate the Regression Equation -- Step 5 Compute the Total Sum of Squares (SSEc or SST) -- Step 6 Compute the Sum of Squares for Model A (SSEa) -- Step 7 Compute Sum of Squares Reduced (SSR) -- Step 8 Compute the Proportional Reduction in Error (PRE or R2) -- Step 9 Complete the Summary Table -- Step 10 Decide About H0 -- Model Comparison Example -- Step 1 Define the Initial, Compact Model C and an Augmented Model A -- Step 2 Identify the Null Hypothesis (H0) -- Step 3 Count the Number of Parameters Estimated in Each Model -- Step 4 Calculate the Regression Equation -- Step 5 Compute the Total Sum of Squares (SST or SSEc) --

Contents note continued: Step 6 Compute the Sum of Squares for Model A (SSEa) -- Step 7 Compute Sum of Squares Reduced (SSR) -- Step 8 Compute the Proportional Reduction in Error (PRE or R2) -- Step 9 Complete the Summary Table -- Step 10 Decide About H0 -- Summary -- pt. II FUNDAMENTAL STATISTICAL TESTS -- 5.Correlation: Traditional and Regression Approaches -- Magnitude -- Direction -- Picture the Correlation -- Calculate the Correlation -- Total or Combined Variance -- Common or Shared Variance -- Pearson Correlation Coefficient -- Demonstration -- Null Hypothesis and Correlation -- Correlation Coefficient and Variance Explained -- Summary -- 6.The Traditional t Test: Concepts and Demonstration -- Comparing Group Means -- t-Test Formula -- Using t to Decide About H0 -- The t Distribution -- Demonstration -- Summary -- 7.One-Way ANOVA: Traditional Approach -- ANOVA Concepts -- Begin With SST -- Compute Explained SS -- Compute Residual SSE -- ANOVA Summary Table -- Summary --

Contents note continued: 8.t Test, ANOVA, and the Bivariate Regression Approach -- Test Two Groups Using Model Comparison -- Step 1 State the Compact Model C and an Augmented Model A -- Step 2 Identify the Null Hypothesis -- Step 3 Count the Number of Parameters Estimated in Each Model -- Step 4 Calculate the Regression Equation -- Step 5 Compute the Total Sum of Squares -- Step 6 Compute the Sum of Squared Errors for Model A -- Step 7 Compute the Sum of Squares Reduced -- Step 8 Compute the Proportional Reduction in Error -- Step 9 Complete the Summary Table -- Step 10 Decide About H0 -- Compare the Results of t Test, ANOVA, and Bivariate Regression -- Summary -- pt. III ADDING COMPLEXITY -- 9.Model Comparison II: Multiple Regression -- Conducting the Omnibus Test -- Isolating the Effects of X1 and X2 -- Testing the Relationship Between X2 and Y -- Testing the Relationship Between X1 and Y -- Summary -- 10.Multiple Regression: When Predictors Interact --

Contents note continued: Mean Deviation Revisited -- The Interaction Term: A Cross Product of the Predictors -- Interpreting the Interaction -- Interaction Without Mean Deviation -- Summary -- 11.Two-Way ANOVA: Traditional Approach -- Grand and Group Means -- Partition the Sum of Squares -- Compute SST -- SSB for Sex -- SSB for Diagnosis -- Interaction of Sex and Diagnosis -- Begin the Summary Table -- Residual Sum of Squares -- Complete the Summary Table -- Interpret the F Values -- Interpret the Interaction -- Summary -- 12.Two-Way ANOVA: Model Comparison Approach -- Contrast Versus Dummy Codes -- Conducting the Omnibus Test -- Calculate the Error of the Omnibus Model -- Testing the Components of the Model -- Testing the Main Effect of Sex -- Testing the Main Effect of Diagnosis -- Testing the Interaction of Diagnosis and Sex -- Interpreting the Coefficients -- Summary -- 13.One-Way ANOVA With Three Groups: Traditional Approach -- Conducting the Analysis of Variance --

Contents note continued: Total Variance -- Variance Explained -- Compute the Residual Variance -- Complete the Summary Table -- Post Hoc analysis: Where's the Difference? -- Risk of Multiple Tests -- Calculate the LSD -- Summary -- 14.ANOVA With Three Groups: Model Comparison Approach -- Isolating Effects: Conceptualizing Linear Comparisons of Three Groups -- Contrast Codes With More Than Two Groups -- Comparing Models to Test the One-Way ANOVA -- Conducting the Omnibus Test -- Isolating the Effects of the Linear Predictor -- Isolating the Effects of the Quadratic Predictor -- Summary -- 15.Two-by-Three ANOVA: Complex Categorical Models -- Sum of Squares Between -- Sum of Squares Between for Sex -- Sum of Squares Between for Drug Abuse -- Interaction of Sex and Drug Abuse -- Sum of Squares Within -- Interpreting the Results -- Summary -- 16.Two-by-Three ANOVA: Model Comparison Approach -- Omnibus Model -- Compute Error for Model A --

Contents note continued: Isolate the Effects of Each Model A Predictor -- Comparison 1 Main Effect for Sex -- Comparison 2 Linear Main Effect for Drug Abuse (DAlinear) -- Comparison 3 Quadratic Main Effect for Drug Abuse (DAquadratic) -- Comparison 4 Interaction Between Sex and Linear Contrast for Drug Abuse (DAlinear) -- Comparison 5 Interaction Between Sex and Linear Contrast for Drug Abuse (DAlinear) -- Complete the Summary Table -- Summary -- 17.Analysis of Covariance: Continuous and Categorical Predictors -- Concept of Statistical Covariation -- Testing the Effects of Sex, Controlling for Age -- They Weren't Related, but Now They Are! -- Summary -- 18.Repeated Measures -- Repeated Measures "Matched Pairs" t Test -- Repeated Measures ANOVA: Model Comparison Approach -- Finding the Difference Between the Two DVs -- Summary -- 19.Multiple Repeated Measures -- Three Repeated Measures: Weighting Each Score -- Linear Change in Mean Scores Over Time --

Contents note continued: Model A and the Average Wlinear Score -- Quadratic Change in Mean Scores Over Time -- Combine the Two Analyses Into a Single Summary Table -- Summary -- 20.Mixed Between and Within Designs -- Main Effect Between Groups -- Main Effect Within Groups -- Groups-by-Treatment Interaction -- Summary -- A Final Comment -- APPENDICES -- Appendix A Research Designs -- Experiment -- Quasi-Experiment -- Associational Designs -- Appendix B Variables, Distributions, and Statistical Assumptions -- Types of Variables -- Ordinal Variables -- Nominal Variables -- Continuous Variable -- Distributions of Continuous Variables -- Operational Definition -- Statistical Assumptions -- Normal Distribution -- Homogeneity of Variances -- Appendix C Sampling and Sample Sizes -- Appendix D Null Hypothesis, Statistical Decision Making, and Statistical Power -- Null Hypothesis -- Statistical Decision -- Statistical Power.