SAS ebook: Regression with SAS-经管之家官网！

SAS ebook:
This ebook is from UCLA.edu, it is very good for beginner of SAS statistics user, the book give very detail very deep description on how to use SAS for statistical analysis.  (For beginner of statistical analysis, NOT for SAS beginner).
I also upload SPSS, STATA ebook here.
zgp480
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contents
Regression with SAS
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 regressionw
    1.4 Multiple regression
    1.5 Transforming variables
    1.6 Summary
    1.7 For more information
Regression with SAS
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 SAS 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
Regression with SAS Chapter 4 - Beyond OLS
Chapter Outline
4.1 Robust Regression Methods
4.1.1 Regression with Robust Standard Errors
4.1.2 Using the Proc Genmod for Clustered Data
4.1.3 Robust Regression
4.1.4 Quantile Regression
4.2 Constrained Linear Regression
4.3 Regression with Censored or Truncated Data
4.3.1 Regression with Censored Data
4.3.2 Regression with Truncated Data
4.4 Regression with Measurement Error
4.5 Multiple Equation Regression Models
4.5.1 Seemingly Unrelated Regression
4.5.2 Multivariate Regression
4.6 Summary
Regression with SAS
Chapter 5: Additional coding systems for categorical variables in regression analysis  
Regression with SAS
Chapter 6 - More on Interactions of Categorical Predictors
Chapter Outline
   6.0 Introduction
   6.1. Analysis with two categorical variables
   6.2. Simple effects
     6.2.1 Analyzing simple effects using PROC GLM
    6.2.2 Analyzing Simple Effects Using  PROC REG
   6.3. Simple comparisons
     6.3.1 Analyzing simple comparisons using PROC REG
     6.3.2 Analyzing simple comparisons using PROC GLM
   6.4. Partial Interaction
     6.4.1 Analyzing partial interactions using PROC GLM
     6.4.2 Analyzing partial interactions using PROC REG
   6.5. Interaction contrasts
     6.5.1 Analyzing interaction contrasts using PROC GLM
     6.5.2 Analyzing interaction contrasts using PROC REG
   6.6. Computing adjusted means
     6.6.1 Computing adjusted means via PROC GLM
     6.6.1 Computing adjusted means via PROC REG
   6.7. More details on meaning of coefficients
   6.8. Simple effects via dummy coding versus 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 SAS
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 and simple comparisons of group, strategy 2
    7. More on predicted values