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Logistic Regression with SAS: Build & Evaluate Models

Instructor: EDUCBA

Included with

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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

Shareable certificate

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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.

Module details

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 videosTotal 114 minutes

Introduction to Logistic Regression Project using SAS Stat10 minutes
Insurance Dataset Explanation and Exploration16 minutes
Logistic Regression Demonstration Part 114 minutes
Logistic Regression Demonstration Part 227 minutes
Missing Values Imputation23 minutes
Categorical Inputs11 minutes
Categorical Inputs Continue13 minutes

4 assignmentsTotal 60 minutes

Introduction and Business Context10 minutes
Building the First Logistic Models10 minutes
Preparing Raw Data for Modeling10 minutes
Geaded-Logistic Regression Foundations and Data Setup – Graded Quiz30 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 videosTotal 79 minutes

Variable Clustering Part 112 minutes
Variable Clustering Part 27 minutes
Variable Clustering Part 38 minutes
Variable Screening11 minutes
Variable Screening Continue9 minutes
Exploring Nonlinear Relationships in Subset Selection12 minutes
Data Transformation for Linear Subset Selection11 minutes
Problem Framing and Logic Plots in Subset Selection10 minutes

4 assignmentsTotal 60 minutes

Variable Clustering for Data Reduction10 minutes
Screening Predictors for Importance10 minutes
Subset Selection Foundations10 minutes
Graded -Feature Engineering and Predictor Selection 30 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.