Predictive Modeling with Logistic Regression using SAS
- 4.5
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Approx. 17 hours to complete
Course Summary
Learn how to use SAS for predictive modeling using logistic regression. This course covers the basics of logistic regression, data preparation, model selection, and model evaluation.Key Learning Points
- Learn how to use SAS for predictive modeling using logistic regression
- Understand the basics of logistic regression
- Master data preparation techniques
- Learn model selection and evaluation
Related Topics for further study
Learning Outcomes
- Understand the basics of logistic regression
- Master data preparation techniques
- Learn model selection and evaluation
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of SAS programming
- Familiarity with statistical concepts
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Applied Predictive Modeling
- Data Science Essentials
- Python Data Science Toolbox
Related Education Paths
- SAS Certified Predictive Modeler Using SAS Enterprise Miner
- SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling
Related Books
Description
This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. This course also discusses selecting variables and interactions, recoding categorical variables based on the smooth weight of evidence, assessing models, treating missing values, and using efficiency techniques for massive data sets. You learn to use logistic regression to model an individual's behavior as a function of known inputs, create effect plots and odds ratio plots, handle missing data values, and tackle multicollinearity in your predictors. You also learn to assess model performance and compare models.
Outline
- Course Overview and Logistics
- Meet the Instructor
- What You Learn in This Course
- Learner Prerequisites
- Using Forums and Getting Help
- Access SAS Software for this Course
- Set Up Data for This Course (REQUIRED)
- About the Demos and Practices in this Course
- Understanding Predictive Modeling
- Overview
- Introduction
- Goals of Predictive Modeling
- Terms for Elements in Predictive Modeling
- Basic Steps of Predictive Modeling
- Applications of Predictive Modeling
- Demonstration Scenario: Target Marketing for a Bank
- Demo: Examining the Code for Generating Descriptive Statistics and Frequency Tables
- Introduction
- Data Challenges
- Analytical Challenges
- Separate Sampling
- Avoiding the Optimism Bias: Honest Assessment
- Splitting the Data for Model Training and Assessment
- Demo: Splitting the Data
- Summary
- Practice: Exploring the Bank Data for the Target Marketing Project
- Practice: Exploring the Veterans' Organization Data Used in the Practices
- Question 1.01
- Question 1.02
- Question 1.03
- Practice: Splitting the Data
- Fitting the Model
- Overview
- Introduction
- Understanding the Logistic Regression Model
- Constraining the Posterior Probability Using the Logit Transformation
- Understanding the Fitted Surface
- Interpreting the Model by Calculating the Odds Ratio
- Understanding Logistic Discrimination
- Estimating Unknown Parameters Using Maximum Likelihood Estimation
- Interpreting Concordant, Discordant, and Tied Pairs
- Using PROC LOGISTIC to Fit Logistic Regression Models
- Demo: Fitting a Basic Logistic Regression Model, Part 1
- Demo: Fitting a Basic Logistic Regression Model, Part 2
- Scoring New Cases
- Demo: Scoring New Cases
- Introduction
- Understanding the Effect of Oversampling
- Understanding the Offset
- Demo: Correcting for Oversampling
- Summary
- Question 2.01
- Question 2.02
- Practice: Fitting a Logistic Regression Model
- Fitting the Model Review
- Preparing the Input Variables, Part 1
- Overview
- Introduction
- Reasons for Missing Data
- Complete Case Analysis
- Methods for Imputing Missing Values
- Missing Value Imputation with Missing Value Indicator Variables
- Demo: Imputing Missing Values
- Cluster Imputation
- Introduction
- Problems Caused by Categorical Inputs
- Solutions to Problems Caused by Categorical Inputs
- Linking to Other Data Sets
- Collapsing Categories by Thresholding
- Collapsing Categories by Using Greenacre's Method
- Demo: Collapsing the Levels of a Nominal Input, Part 1
- Demo: Collapsing the Levels of a Nominal Input, Part 2
- Replacing Categorical Levels by Using Smoothed Weight-of-Evidence Coding
- Demo: Computing the Smoothed Weight of Evidence
- Introduction
- Problem of Redundancy
- Variable Clustering Method
- Understanding Principal Components
- Divisive Clustering
- PROC VARCLUS Syntax
- Selecting a Representative Variable from Each Cluster
- Demo: Reducing Redundancy by Clustering Variables
- Question 3.01
- Practice: Imputing Missing Values
- Question 3.02
- Question 3.03
- Question 3.04
- Practice: Collapsing the Levels of a Nominal Input
- Practice: Computing the Smoothed Weight of Evidence
- Question 3.05
- Practice: Reducing Redundancy by Clustering Variables
- Preparing the Input Variables, Part 2
- Introduction
- Detecting Nonlinear Relationships
- Demo: Performing Variable Screening, Part 1
- Demo: Performing Variable Screening, Part 2
- Univariate Binning and Smoothing
- Demo: Creating Empirical Logit Plots
- Remedies for Nonlinear Relationships
- Demo: Accommodating a Nonlinear Relationship, Part 1
- Demo: Accommodating a Nonlinear Relationship, Part 2
- Introduction
- Specifying a Subset Selection Method in PROC LOGISTIC
- Best-Subsets Selection
- Stepwise Selection
- Backward Elimination
- Scalability of the Subset Selection Methods in PROC LOGISTIC
- Detecting Interactions
- BIC-based Significance Level
- Demo: Detecting Interactions
- Demo: Using Backward Elimination to Subset the Variables
- Demo: Displaying Odds Ratios for Variables Involved in Interactions
- Demo: Creating an Interaction Plot
- Demo: Using the Best-Subsets Selection Method
- Demo: Using Fit Statistics to Select a Model
- Summary of Preparing the Input Variables, Parts 1 and 2
- Question 3.06
- Practice: Performing Variable Screening
- Practice: Creating Empirical Logit Plots
- Question 3.07
- Question 3.08
- Question 3.09
- Practice: Using Forward Selection to Detect Interactions
- Question 3.10
- Practice: Using Backward Elimination to Subset the Variables
- Question 3.11
- Practice: Using Fit Statistics to Select a Model
- Preparing the Input Variables Review
- Measuring Model Performance
- Overview
- Introduction
- Fit versus Complexity
- Assessing Models when Target Event Data Is Rare
- Demo: Preparing the Validation Data
- Introduction
- Understanding the Confusion Matrix
- Measuring Performance across Cutoffs by Using the ROC Curve
- Choosing Depth by Using the Gains Chart
- Effects of Oversampled Data on Performance Measures
- Adjusting a Confusion Matrix for Oversampling
- Demo: Measuring Model Performance Based on Commonly-Used Metrics
- Introduction
- Understanding the Effect of Cutoffs on Confusion Matrices
- Understanding the Profit Matrix
- Choosing the Optimal Cutoff by Using the Profit Matrix
- Using the Central Cutoff
- Using Profit to Assess Fit
- Calculating Sampling Weights
- Demo: Using a Profit Matrix to Measure Model Performance
- Introduction
- Plotting Class Separation
- Assessing Overall Predictive Power
- Demo: Using the K-S Statistic to Measure Model Performance
- Introduction
- Comparing ROC Curves of Several Models"
- Demo: Comparing ROC Curves to Measure Model Performance
- Using Macros to Compare Many Models
- Demo: Comparing and Evaluating Many Models, Part 1
- Demo: Comparing and Evaluating Many Models, Part 2
- Summary
- Question 4.01
- Question 4.02
- Question 4.03
- Practice: Assessing Model Performance
- Question 4.04
- Question 4.05
- Question 4.06
- Question 4.07
- Measuring Model Performance Review
- SAS Certification Practice Exam - Statistical Business Analysis Using SAS®9: Regression and Modeling
- About the Certification Exam
Summary of User Reviews
The SAS Predictive Modeling Using Logistic Regression course on Coursera has received positive reviews from users. Many users found the course to be comprehensive and informative, with practical examples and clear explanations.Key Aspect Users Liked About This Course
Comprehensive and informative course with practical examples and clear explanations.Pros from User Reviews
- Well-structured curriculum
- Engaging and knowledgeable instructors
- Hands-on assignments and quizzes that reinforce learning
- Real-world applications of logistic regression
- Flexible pacing and scheduling
Cons from User Reviews
- Some users found the course to be too basic
- The pace may be too slow for advanced learners
- Limited interaction with instructors and peers
- Some technical issues with the platform
- Cost may be prohibitive for some learners
Michael J Patetta
4.5 Raiting
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