中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
您将学到什么
Implement logistic regression models with SAS.
Prepare datasets with imputation and categorical encoding.
Evaluate models using clustering, screening, and confusion matrices.
您将获得的技能
- Statistical Modeling
- Statistical Methods
- Predictive Analytics
- Regression Analysis
- Applied Machine Learning
- Data Cleansing
- SAS (Software)
- Data Transformation
- Classification And Regression Tree (CART)
- Exploratory Data Analysis
- Predictive Modeling
- Data Processing
- Data Manipulation
- Statistical Analysis
- Feature Engineering
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最近已更新！
September 2025
作业
11 项作业
授课语言：英语（English）
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该课程共有3个模块
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.
涵盖的内容
7个视频4个作业1个插件
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.
涵盖的内容
8个视频4个作业
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.
涵盖的内容
6个视频3个作业
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