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.
Logistic Regression with SAS: Build & Evaluate Models
Logistic Regression with SAS: Build & Evaluate Models
Instructor: EDUCBA
Access provided by Iran University of Science and Technology
Gain insight into a topic and learn the fundamentals.
7 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
- Regression Analysis
- Data Transformation
- Statistical Methods
- Statistical Modeling
- Data Cleansing
- SAS (Software)
- Statistical Analysis
- Predictive Analytics
- Data Processing
- Data Manipulation
- Applied Machine Learning
- Predictive Modeling
- Classification And Regression Tree (CART)
- Exploratory Data Analysis
- Feature Engineering
Details to know
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Assessments
11 assignments
Taught in English
Recently updated!
September 2025
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There are 3 modules in this course
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 assignments1 plugin
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
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.
What's included
6 videos3 assignments
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