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Regression Modeling Fundamentals
  • Level Professional
  • Duration 12 hours
  • Course by SAS
  • Offered by

About

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.

Modules

Welcome

    • 2 Videos
    • 1 Readings
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2 Videos

  • Welcome and Meet the Instructor
  • Demo: Exploring Ames Housing Data

1 Readings

  • Learner Prerequisites

Course Logistics and Data Setup

    • 4 Readings
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4 Readings

  • 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

Introduction

    • 1 Videos
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1 Videos

  • Overview

Stepwise Selection Using Significance Level

    • 6 Videos
    • 1 Readings
    • 2 Assignment
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2 Assignment

  • Question 4.01
  • Practice - Using PROC GLMSELECT to Perform Stepwise Selection

6 Videos

  • 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

1 Readings

  • Activity - Optional Stepwise Selection Method Code

Information Criterion and Other Selection Options

    • 4 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Practice - Using PROC GLMSELECT to Perform Other Model Selection Techniques

4 Videos

  • Scenario
  • Information Criteria
  • Adjusted R-Square and Mallows' Cp
  • Demo: Performing Model Selection Using PROC GLMSELECT

1 Readings

  • Information Criteria Penalty Components

All Possible Selection (Self-Study)

    • 1 Readings
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1 Readings

  • All-Possible Selection

Review

    • 1 Assignment
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1 Assignment

  • Model Building and Effect Selection

Introduction

    • 1 Videos
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1 Videos

  • Overview

Examining Residuals

    • 4 Videos
    • 1 Assignment
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1 Assignment

  • Practice: Using PROC REG to Examine Residuals

4 Videos

  • Scenario
  • Assumptions for Regression
  • Verifying Assumptions Using Residual Plots
  • Demo: Examining Residual Plots Using PROC REG

Influential Observations

    • 8 Videos
    • 2 Assignment
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2 Assignment

  • Question 5.01
  • Practice: Using PROC REG to Generate Potential Outliers

8 Videos

  • 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

Collinearity

    • 5 Videos
    • 3 Assignment
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3 Assignment

  • Question 5.02
  • Question 5.03
  • Practice: Using PROC REG to Assess Collinearity

5 Videos

  • Scenario
  • Exploring Collinearity
  • Visualizing Collinearity
  • Demo: Calculating Collinearity Diagnostics Using PROC REG
  • Using an Effective Modeling Cycle

Review

    • 1 Assignment
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1 Assignment

  • Model Post-Fitting for Inference

Introduction

    • 1 Videos
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1 Videos

  • Overview

Brief Introduction to Predictive Modeling

    • 6 Videos
    • 1 Readings
    • 2 Assignment
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2 Assignment

  • Question 6.01
  • Practice: Building a Predictive Model Using PROC GLMSELECT

6 Videos

  • Scenario
  • Predictive Modeling Terminology
  • Model Complexity
  • Building a Predictive Model
  • Model Assessment and Selection
  • Demo: Building a Predictive Model Using PROC GLMSELECT

1 Readings

  • Partitioning a Data Set Using PROC GLMSELECT

Scoring Predictive Models

    • 4 Videos
    • 1 Assignment
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1 Assignment

  • Practice: Scoring Using the SCORE Statement in PROC GLMSELECT

4 Videos

  • Scenario
  • Preparing for Scoring
  • Methods of Scoring
  • Demo: Scoring Data Using PROC PLM

Review

    • 1 Assignment
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1 Assignment

  • Model Building for Scoring and Prediction

Introduction

    • 1 Videos
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1 Videos

  • Overview

Describing Categorical Data

    • 3 Videos
    • 3 Assignment
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3 Assignment

  • Question 7.01
  • Question 7.02
  • Practice: Using PROC FREQ to Examine Distributions

3 Videos

  • Scenario
  • Associations between Categorical Variables
  • Demo: Examining the Distribution of Categorical Variables Using PROC FREQ and PROC UNIVARIATE

Tests of Association

    • 8 Videos
    • 5 Assignment
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5 Assignment

  • Question 7.03
  • Question 7.04
  • Question 7.05
  • Question 7.06
  • Practice: Using PROC FREQ to Perform Tests and Measures of Association

8 Videos

  • 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

Introduction to Logistic Regression

    • 5 Videos
    • 3 Assignment
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3 Assignment

  • Question 7.07
  • Question 7.08
  • Practice: Using PROC LOGISTIC to Perform a Binary Logistic Regression Analysis

5 Videos

  • 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

Logistic Regression with Categorical Predictors

    • 3 Videos
    • 3 Assignment
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3 Assignment

  • Question 7.09
  • Question 7.10
  • Practice: Using PROC LOGISTIC to Perform a Multiple Logistic Regression Analysis with Categorical Variables

3 Videos

  • Scenario
  • Specifying a Parameterization Method
  • Demo: Fitting a Multiple Logistic Regression Model with Categorical Predictors Using PROC LOGISTIC

Stepwise Selection with Interactions and Predictions

    • 5 Videos
    • 3 Assignment
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3 Assignment

  • Question 7.11
  • Question 7.12
  • Practice: Using PROC LOGISTIC to Perform Backward Elimination and PROC PLM to Generate Predictions

5 Videos

  • 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

Review

    • 1 Assignment
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1 Assignment

  • Categorical Data Analysis

Auto Summary

Unlock the essentials of statistical analysis with the "Regression Modeling Fundamentals" course, tailor-made for SAS software users. Offered by Coursera, this professional-level program delves into the core techniques of data science and AI. Participants will gain hands-on experience with t tests, ANOVA, and linear regression, along with a concise overview of logistic regression. Spanning 720 minutes, the course is thoughtfully designed to build a solid foundation in SAS/STAT software. Flexible subscription options, including Starter and Professional plans, make it accessible for both budding analysts and seasoned professionals seeking to enhance their statistical analysis capabilities. Perfect for those aiming to leverage SAS software for robust data insights, this course promises to elevate your analytical skills to new heights.

Jordan Bakerman
Instructor

Jordan Bakerman