Applied Logistic Regression with SAS Stat

Name: Applied Logistic Regression with SAS Stat
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Exploring logistic regression modeling techniques with SAS Stat for predictive analytics.

Created byEDUCBA Bridging the Gap

Last updated 3/2024

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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.

Course content

4 sections • 21 lectures • 4h 18m total length

Introduction to Logistic Regression Project using SAS Stat10:11
Build a logistic regression model in SAS stat using a banking case study to predict responses to a new insurance product, with data exploration, preparation, oversampling adjustments, and feature selection.
Insurance Dataset Explanation and Exploration15:31
Explore the insurance dataset for a binary outcome, identify predictors and their scales, examine missing values, and discuss preparing categorical variables with dummy coding and level-of-measurement considerations.

Logistic Regression Demonstration Part 114:16
select predictor variables from a banking dataset and fit a proc logistic model, then interpret effect plots, odds ratios, and interactions.
Logistic Regression Demonstration Part 227:00
Explore binary logistic regression using SAS proc logistic, focusing on model information, convergence, fit statistics, odds ratios, confidence intervals, effect plots, and interpreting predictor significance via maximum likelihood estimates.
Missing Values Imputation22:37
Impute missing values and create missing-value indicators to prepare input variables for logistic regression in SAS. Explore oversampling adjustments and techniques like median imputation and regression-based methods.
Categorical Inputs11:29
Learn how to handle categorical inputs in logistic regression by coding with dummy variables, setting a reference level, and reducing categories using the chi-square based green source approach.
Categorical Inputs Continue13:02
Use chi-square statistics and associated p-values to determine the optimal five-cluster grouping of branch codes, reducing 50 categorical inputs to five categories for SAS logistic regression.

Variable Clustering Part 111:30
Learn variable clustering to reduce dimensionality before modeling, using principal component analysis on imputed data with missing indicators, then select one representative variable per cluster for the final logistic model.
Variable Clustering Part 26:50
Explore eigenvalue-based clustering that splits data into clusters and converges, yielding 41 clusters from 64 predictors, and learn to pick cluster representatives via business knowledge or one minus R-square ratio.
Variable Clustering Part 37:46
Learn how variable clustering uses principal component analysis to reduce variables to 41 clusters with representatives, guided by eigenvalues, factor loadings, and a 0.7 threshold.
Variable Screening11:09
Screen predictor variables after clustering by applying univariate screening with Spearman and Hoeffding correlations to assess each variable's association with the binary target, insurance.
Variable Screening Continue8:51
Create a SAS dataset to compare Spearman and Hoeffding ranks, analyze with a scatter plot and reference lines, and screen out weak predictors by updating the screened macro variable.

Subset Selection Part 111:59
Expose non-linear relation between balance and empirical logit, replace zero spike with the mean, and use rank and means procedures to plot empirical logits across bins for linearization.
Subset Selection Part 211:12
Apply percentile-based bin transformations using proc rank to create bean rank groups for a predictor. Transform the balance variable into rank groups and update screen variables for automated SAS modeling.
Subset Selection Part 310:02
Explore subset selection in logistic regression, using backward, forward, and stepwise elimination to reduce 39 predictors, and apply the screen variable and proc logistic for odds ratio plots.
Subset Selection Part 49:22
Explore backward elimination in SAS logistic regression, using the fast option to approximate and refit remaining predictors, iterate steps, and stop when no variables meet the 0.01 significance threshold.
Subset Selection Part 510:33
Illustrates backward elimination in logistic regression using a fast option versus iterative steps, removing non-significant predictors and presenting final model with significant variables and interpreted odds ratios.
Subset Selection Part 610:34
Explore subset selection in logistic regression using SAS PROC LOGISTIC to generate combinations and score them with chi square statistics, then use information criteria to pick the best.
Subset Selection Part 79:12
Explore subset selection in SAS: score 39 logistic regression models using proc logistic, with based subsets and macro loops to compute BIC and fit statistics saved in SAS datasets.
Subset Selection Part 89:44
Evaluate misclassification rate from confusion matrices across one- and two-variable models, then apply log likelihood and BIC in a 39-model subset selection to pick the 21-variable final model.
Logit Plots15:17
Use logit plots to test linearity between predictors and the logit, compute empirical logits, and decide when to transform balance and other variables for SAS logistic regression.

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.

Applied Logistic Regression with SAS Stat

What you'll learn

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Course content

Introduction2 lectures • 26min

Logistic Regression Demonstration5 lectures • 1hr 28min

Variable Clustering5 lectures • 46min

Subset Selection9 lectures • 1hr 38min

Requirements

Description

Who this course is for: