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雷达卡
ebook: regression with SPSS (2010)
This ebook is from ucla.edu. it really good for beginner of statistician (not for beginner of SPSS programme).
contents
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Regression with SPSS
Chapter 1 - Simple and Multiple Regression
Chapter Outline
1.0 Introduction
1.1 A First Regression Analysis
1.2 Examining Data
1.3 Simple linear regression
1.4 Multiple regression
1.5 Transforming variables
1.6 Summary
1.7 For more information
Regression with SPSS
Chapter 2 - Regression Diagnostics
Chapter Outline
2.0 Regression Diagnostics
2.1 Unusual and Influential data
2.2 Tests on Normality of Residuals
2.3 Tests on Nonconstant Error of Variance
2.4 Tests on Multicollinearity
2.5 Tests on Nonlinearity
2.6 Model Specification
2.7 Issues of Independence
2.8 Summary
2.9 For more information
Regression with SPSS
Chapter 3 - Regression with Categorical Predictors
Chapter Outline
3.0 Regression with Categorical Predictors
3.1 Regression with a 0/1 variable
3.2 Regression with a 1/2 variable
3.3 Regression with a 1/2/3 variable
3.4 Regression with multiple categorical predictors
3.5 Categorical predictor with interactions
3.6 Continuous and Categorical variables
3.7 Interactions of Continuous by 0/1 Categorical variables
3.8 Continuous and Categorical variables, interaction with 1/2/3 variable
3.9 Summary
3.10 For more information
Chapter 4 - Beyond Ordinary Least Squares Regression (under development)
Chapter 5: Additional coding systems for categorical variables in regression analysis
Chapter Outline
5.1 Simple Coding
5.2 Forward Difference Coding
5.3 Backward Difference Coding
5.4 Helmert Coding
5.5 Reverse Helmert Coding
5.6 Deviation Coding
5.7 Orthogonal Polynomial Coding
5.8 User-Defined Coding
5.9 Summary
5.10 For more information
Regression with SPSS
Chapter 6: More on Interactions of Categorical Variables
Draft Version
This is a draft version of this chapter. Comments and suggestions to improve this draft are welcome.
Chapter Outline
6.1. Analysis with 2 categorical variables
6.2. Simple effects
6.2.1 Analyzing Simple Effects Using MANOVA and GLM
6.2.2 Analyzing Simple Effects Using REGRESSION
6.3. Simple Comparisons
6.3.1 Analyzing Simple Comparisons Using MANOVA and GLM
6.3.2 Analyzing Simple Comparisons Using REGRESSION
6.4. Partial Interaction
6.4.1 Analyzing partial interactions Using MANOVA and GLM
6.4.2 Analyzing partial interactions Using REGRESSION
6.5. Interaction contrasts
6.5.1 Analyzing interaction contrasts using MANOVA and GLM
6.5.2 Analyzing interaction contrasts using REGRESSION
6.6 Computing Adjusted Means
6.6.1 Computing Adjusted Means via MANOVA and GLM
6.6.2 Computing Adjusted Means via REGRESSION
6.7 More Details on Meaning of the Coefficients
6.8 Simple Effects via Dummy Coding vs. Effect Coding
6.8.1 Example 1. Simple effects of yr_rnd at levels of mealcat
6.8.2 Example 2. Simple effects of mealcat at levels of yr_rnd
Regression with SPSS
Chapter 7: Categorical and Continuous Predictors and Interactions
Chapter Outline
1. Continuous and categorical predictors without interaction
2. Continuous and categorical predictors with interaction
3. Show slopes for each group
3.1 Show slopes by performing separate analyses
3.2 Show slopes for each group from one analysis
4. Compare slopes across groups
5. Simple effects and simple comparisons of group, strategy 1
5.1 Simple effects and comparisons when meals is 1 sd below mean
5.2 Simple effects and comparisons when meals is at the mean
5.3 Simple effects and comparisons when meals is 1 sd above the mean
6. Simple effects, simple group and interaction comparisons, strategy 2
7. More on predicted values
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