Logistic Regression with SAS: Build & Evaluate Models

Instructor: EDUCBA

Access provided by University of Arkansas at Pine Bluff

3 modules

Gain insight into a topic and learn the fundamentals.

9 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

9 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Implement logistic regression models with SAS.
Prepare datasets with imputation and categorical encoding.
Evaluate models using clustering, screening, and confusion matrices.

Skills you'll gain

Tools you'll learn

Details to know

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Assessments

11 assignments

Taught in English

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There are 3 modules in this course

By completing this course, learners will be able to implement logistic regression models in SAS, prepare datasets through missing value imputation and categorical encoding, analyze predictors using clustering and screening, and evaluate models with confusion matrices and logit plots. Designed for aspiring data scientists, analysts, and business professionals, this course blends statistical rigor with hands-on SAS demonstrations.

Learners will benefit by gaining both technical knowledge and practical skills to solve real-world classification problems, such as predicting customer behavior, assessing risk, or identifying fraud. Unlike generic statistical tutorials, this course uniquely emphasizes feature engineering, subset selection, and SAS-specific implementation to ensure models are not only accurate but also interpretable and business-ready. Through structured modules, learners progress from foundational concepts to advanced evaluation, ensuring they can confidently build, optimize, and validate logistic regression models. By the end, participants will have mastered the end-to-end workflow of logistic regression in SAS, positioning themselves for success in data-driven roles across industries.

This module introduces learners to the foundations of logistic regression and the importance of data preparation when working in SAS. Students explore the basics of binary classification, apply logistic regression using PROC LOGISTIC, and prepare datasets by handling missing values and encoding categorical variables. By the end of this module, learners will have the skills to structure datasets correctly and build their first logistic regression models in SAS.

What's included

7 videos4 assignments

7 videos Total 114 minutes

Introduction to Logistic Regression Project using SAS Stat 10 minutes
Insurance Dataset Explanation and Exploration 16 minutes
Logistic Regression Demonstration Part 1 14 minutes
Logistic Regression Demonstration Part 2 27 minutes
Missing Values Imputation 23 minutes
Categorical Inputs 11 minutes
Categorical Inputs Continue 13 minutes

4 assignments Total 60 minutes

Geaded-Logistic Regression Foundations and Data Setup – Graded Quiz 30 minutes
Introduction and Business Context 10 minutes
Building the First Logistic Models 10 minutes
Preparing Raw Data for Modeling 10 minutes

This module focuses on advanced data preparation techniques to improve logistic regression performance. Learners examine variable clustering to reduce redundancy, use screening techniques to evaluate predictor importance, and explore subset selection methods to refine model inputs. The emphasis is on selecting the most relevant predictors, improving efficiency, and ensuring model stability in SAS.

What's included

8 videos4 assignments

8 videos Total 79 minutes

Variable Clustering Part 1 12 minutes
Variable Clustering Part 2 7 minutes
Variable Clustering Part 3 8 minutes
Variable Screening 11 minutes
Variable Screening Continue 9 minutes
Exploring Nonlinear Relationships in Subset Selection 12 minutes
Data Transformation for Linear Subset Selection 11 minutes
Problem Framing and Logic Plots in Subset Selection 10 minutes

4 assignments Total 60 minutes

Graded -Feature Engineering and Predictor Selection 30 minutes
Variable Clustering for Data Reduction 10 minutes
Screening Predictors for Importance 10 minutes
Subset Selection Foundations 10 minutes

This module advances into model building strategies and performance evaluation. Students explore stepwise and backward elimination techniques to refine predictors, implement models using PROC LOGISTIC and ODS, and assess model performance with misclassification analysis, confusion matrices, and logit plots. Learners will gain the ability to build robust logistic regression models and validate them effectively in SAS.