Applied Logistic Regression with SAS Stat
What you'll learn
- Introduction to logistic regression projects using SAS Stat.
- Understanding and exploring an insurance dataset for analysis.
- Demonstration of logistic regression modeling techniques.
- Handling missing values and imputation in the dataset.
- Dealing with categorical inputs in logistic regression.
- Variable clustering techniques for data preprocessing.
- Variable screening methods to identify significant predictors.
- Subset selection strategies for model refinement.
- Interpretation of logit plots for assessing model performance and insights.
Requirements
- Students or anyone taking this course should have some familiarity with SAS. There are no basic skills required to take this course.
Description
Welcome to the Logistic Regression Project using SAS Stat course! In this course, you will delve into the fundamentals of logistic regression analysis and its application in real-world scenarios using SAS Stat. Logistic regression is a powerful statistical technique commonly used for binary classification tasks, such as predicting the likelihood of an event occurring or not.
Throughout this course, you will learn how to analyze and model data using logistic regression techniques, specifically tailored to the context of insurance datasets. By the end of the course, you will have a solid understanding of how to build, evaluate, and interpret logistic regression models, making informed decisions based on data-driven insights.
Whether you're a beginner looking to enhance your statistical analysis skills or an experienced data analyst seeking to expand your knowledge of logistic regression in SAS Stat, this course offers valuable insights and practical knowledge to advance your proficiency in predictive modeling. Get ready to embark on a journey into the world of logistic regression with SAS Stat!
Section 1: Introduction
In this section, students will receive an introduction to the logistic regression project using SAS Stat. Lecture 1 provides an overview of the logistic regression project, setting the stage for understanding the subsequent lectures. Lecture 2 delves into the explanation and exploration of the insurance dataset, offering insights into the data students will be working with throughout the course.
Section 2: Logistic Regression Demonstration
Students will gain hands-on experience with logistic regression in this section. Lecture 3 and Lecture 4 present a demonstration of logistic regression, divided into two parts for comprehensive understanding. Lecture 5 covers techniques for handling missing values, while Lecture 6 and Lecture 7 focus on dealing with categorical inputs, an essential aspect of logistic regression modeling.
Section 3: Variable Clustering
In this section, students will learn about variable clustering, an important technique for simplifying complex datasets. Lecture 8, Lecture 9, and Lecture 10 delve into variable clustering, offering a step-by-step guide to its implementation. Lecture 11 and Lecture 12 further explore variable screening techniques to identify the most influential variables for the regression model.
Section 4: Subset Selection
Subset selection is crucial for building an effective logistic regression model. Lecture 13 to Lecture 21 cover various aspects of subset selection, including its rationale and practical implementation. Students will learn how to select the most relevant subsets of variables to optimize the predictive power of their models. Additionally, Lecture 21 introduces logit plots, providing insights into the relationship between predictor variables and the log-odds of the response variable.
This course equips students with the knowledge and skills needed to perform logistic regression analysis effectively using SAS Stat, from data exploration to model interpretation.
Who this course is for:
- Data analysts and scientists interested in mastering logistic regression modeling techniques using SAS Stat.
- Professionals working in insurance or related industries aiming to enhance their analytical skills for risk assessment and prediction.
- Students and researchers seeking practical knowledge in logistic regression and its application in real-world projects.
- Individuals familiar with SAS software looking to expand their proficiency in statistical analysis and modeling.
Instructor
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