Course Summary
Learn how to use SAS software to build and interpret regression models for analyzing and predicting complex data sets.Key Learning Points
- Understand the basic concepts of regression modeling
- Learn how to use SAS software for regression modeling
- Gain hands-on experience by working with real-world datasets
Related Topics for further study
Learning Outcomes
- Build and interpret regression models using SAS software
- Analyze and predict complex data sets
- Apply regression modeling techniques to real-world scenarios
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics
- Familiarity with SAS software
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Applied Predictive Modeling
- Data Analysis and Statistical Inference
- Introduction to Data Science in Python
Related Education Paths
Related Books
Description
This introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t tests, ANOVA, and linear regression, and includes a brief introduction to logistic regression.
Outline
- Course Overview (Review from Introduction to Statistics: Hypothesis Testing)
- Welcome and Meet the Instructor
- Demo: Exploring Ames Housing Data
- Learner Prerequisites
- Access SAS Software for this Course
- Follow These Instructions to Set Up Data for This Course
- Completing Demos and Practices
- Using Forums and Getting Help
- Model Building and Effect Selection
- Overview
- Scenario
- Approaches to Selecting Models
- The All-Possible Regressions Approach to Model Building
- The Stepwise Selection Approach to Model Building
- Interpreting p-Values and Parameter Estimates
- Demo: Performing Stepwise Regression Using PROC GLMSELECT
- Scenario
- Information Criteria
- Adjusted R-Square and Mallows' Cp
- Demo: Performing Model Selection Using PROC GLMSELECT
- Activity - Optional Stepwise Selection Method Code
- Information Criteria Penalty Components
- Question 4.01
- Practice - Using PROC GLMSELECT to Perform Stepwise Selection
- Practice - Using PROC GLMSELECT to Perform Other Model Selection Techniques
- Model Building and Effect Selection
- Model Post-Fitting for Inference
- Overview
- Scenario
- Assumptions for Regression
- Verifying Assumptions Using Residual Plots
- Demo: Examining Residual Plots Using PROC REG
- Scenario
- Identifying Influential Observations
- Checking for Outliers with STUDENT Residuals
- Checking for Influential Observations
- Detecting Influential Observations with DFBETAS
- Demo: Looking for Influential Observations Using PROC GLMSELECT and PROC REG
- Demo: Examining the Influential Observations Using PROC PRINT
- Handling Influential Observations
- Scenario
- Exploring Collinearity
- Visualizing Collinearity
- Demo: Calculating Collinearity Diagnostics Using PROC REG
- Using an Effective Modeling Cycle
- Practice: Using PROC REG to Examine Residuals
- Question 5.01
- Practice: Using PROC REG to Generate Potential Outliers
- Question 5.02
- Question 5.03
- Practice: Using PROC REG to Assess Collinearity
- Model Post-Fitting for Inference
- Model Building for Scoring and Prediction
- Overview
- Scenario
- Predictive Modeling Terminology
- Model Complexity
- Building a Predictive Model
- Model Assessment and Selection
- Demo: Building a Predictive Model Using PROC GLMSELECT
- Scenario
- Preparing for Scoring
- Methods of Scoring
- Demo: Scoring Data Using PROC PLM
- Partitioning a Data Set Using PROC GLMSELECT
- Question 6.01
- Practice: Building a Predictive Model Using PROC GLMSELECT
- Practice: Scoring Using the SCORE Statement in PROC GLMSELECT
- Model Building for Scoring and Prediction
- Categorical Data Analysis
- Overview
- Scenario
- Associations between Categorical Variables
- Demo: Examining the Distribution of Categorical Variables Using PROC FREQ and PROC UNIVARIATE
- Scenario
- The Pearson Chi-Square Test
- Odds Ratios
- Demo: Performing a Pearson Chi-Square Test of Association Using PROC FREQ
- Scenario
- The Mantel-Haenszel Chi-Square Test
- The Spearman Correlation Statistic
- Demo: Detecting Ordinal Associations Using PROC FREQ
- Scenario
- Modeling a Binary Response
- Demo: Fitting a Binary Logistic Regression Model Using PROC LOGISTIC
- Interpreting the Odds Ratio
- Comparing Pairs to Assess the Fit of a Logistic Regression Model
- Scenario
- Specifying a Parameterization Method
- Demo: Fitting a Multiple Logistic Regression Model with Categorical Predictors Using PROC LOGISTIC
- Scenario
- Interactions between Variables
- Demo: Fitting a Multiple Logistic Regression Model with Interactions Using PROC LOGISTIC
- Demo: Fitting a Multiple Logistic Regression Model with All Odds Ratios Using PROC LOGISTIC
- Demo: Generating Predictions Using PROC PLM
- Question 7.01
- Question 7.02
- Practice: Using PROC FREQ to Examine Distributions
- Question 7.03
- Question 7.04
- Question 7.05
- Question 7.06
- Practice: Using PROC FREQ to Perform Tests and Measures of Association
- Question 7.07
- Question 7.08
- Practice: Using PROC LOGISTIC to Perform a Binary Logistic Regression Analysis
- Question 7.09
- Question 7.10
- Practice: Using PROC LOGISTIC to Perform a Multiple Logistic Regression Analysis with Categorical Variables
- Question 7.11
- Question 7.12
- Practice: Using PROC LOGISTIC to Perform Backward Elimination and PROC PLM to Generate Predictions
- Categorical Data Analysis
Summary of User Reviews
This course on regression modeling using SAS has received positive reviews overall from users. Many users found the course to be comprehensive and easy to follow.Key Aspect Users Liked About This Course
The course is comprehensive and easy to follow.Pros from User Reviews
- Comprehensive coverage of regression modeling using SAS
- Easy-to-follow lectures and demonstrations
- In-depth explanations of statistical concepts
- Real-life examples and case studies to reinforce learning
- Engaging and knowledgeable instructor
Cons from User Reviews
- Some users found the course to be too basic or not challenging enough
- The course may be too specific to SAS software for some users' needs
- Some users experienced technical difficulties with the online platform
- The course may not be suitable for users without prior knowledge of SAS or statistics
- The course may be too time-consuming for some users
Jordan Bakerman
4.8 Raiting
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