SAS Institute Of Management Studies - Regression Modeling Fundamentals
- Offered byCoursera
Regression Modeling Fundamentals at Coursera Overview
Duration | 12 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course |
Credential | Certificate |
Regression Modeling Fundamentals at Coursera Highlights
- Taught by top companies and universities.
- Affordable programs and 7 day free trial.
- Shareable Certificate upon completion.
Regression Modeling Fundamentals at Coursera Course details
- 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.
Regression Modeling Fundamentals at Coursera Curriculum
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
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
All-Possible Selection
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
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