Coursera
Coursera Logo

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 External Link Icon

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.
Details Icon

Regression Modeling Fundamentals
 at 
Coursera 
Course details

More about this course
  • 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

Regression Modeling Fundamentals
 at 
Coursera 
Admission Process

    Important Dates

    May 25, 2024
    Course Commencement Date

    Other courses offered by Coursera

    – / –
    3 months
    Beginner
    – / –
    20 hours
    Beginner
    – / –
    2 months
    Beginner
    – / –
    3 months
    Beginner
    View Other 6715 CoursesRight Arrow Icon
    qna

    Regression Modeling Fundamentals
     at 
    Coursera 

    Student Forum

    chatAnything you would want to ask experts?
    Write here...