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Logistic Regression with SAS: Build & Evaluate Models

位教师：EDUCBA

包含在中

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3个模块

深入了解一个主题并学习基础知识。

9 小时完成

灵活的计划

自行安排学习进度

3个模块

深入了解一个主题并学习基础知识。

9 小时完成

灵活的计划

自行安排学习进度

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.

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作业

11 项作业

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

该课程共有3个模块

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.

单元详情

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

7个视频总计114分钟

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

4个作业总计60分钟

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

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

8个视频总计79分钟

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

4个作业总计60分钟

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

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.