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Predictive Modeling with Logistic Regression using SAS

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Predictive Modeling with Logistic Regression using SAS

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Gain insight into a topic and learn the fundamentals.
4.6

63 reviews

Intermediate level
Some related experience required
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
4.6

63 reviews

Intermediate level
Some related experience required
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

Build your Data Analysis expertise

This course is part of the SAS Statistical Business Analyst Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate from SAS

There are 6 modules in this course

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.

Welcome! In this module, you will review the fundamentals of predictive modeling. First we'll get you started by setting up the course environment. Then you explore the business scenario data that is used throughout the course. You’ll learn the goals of predictive modeling, key terms and model elements, and the basic workflow used to build predictive models, along with common real-world applications. You’ll also work through practical scenarios to explore data using descriptive statistics and frequency tables, and you’ll examine the code used to generate these summaries. Finally, you’ll learn about common data and analytical challenges.

What's included

16 videos6 readings6 assignments

16 videos•Total 34 minutes
  • Meet the Instructor•1 minute
  • Overview•1 minute
  • Introduction•0 minutes
  • Goals of Predictive Modeling•2 minutes
  • Terms for Elements in Predictive Modeling•1 minute
  • Basic Steps of Predictive Modeling•3 minutes
  • Applications of Predictive Modeling•2 minutes
  • Demonstration Scenario: Target Marketing for a Bank•2 minutes
  • Demo: Examining the Code for Generating Descriptive Statistics and Frequency Tables•2 minutes
  • Introduction•0 minutes
  • Data Challenges•6 minutes
  • Analytical Challenges•2 minutes
  • Separate Sampling•2 minutes
  • Avoiding the Optimism Bias: Honest Assessment•2 minutes
  • Splitting the Data for Model Training and Assessment•3 minutes
  • Demo: Splitting the Data•5 minutes
6 readings•Total 46 minutes
  • What You Learn in This Course•5 minutes
  • Learner Prerequisites•1 minute
  • Access SAS Software & Set up Data•10 minutes
  • About the Demos and Practices in this Course•10 minutes
  • Frequently Asked Questions•10 minutes
  • Summary•10 minutes
6 assignments•Total 100 minutes
  • Module-End Graded Assessment•30 minutes
  • Practice: Exploring the Bank Data for the Target Marketing Project•20 minutes
  • Practice: Exploring the Veterans' Organization Data Used in the Practices•20 minutes
  • Knowledge Check: Model Training & Sampling•5 minutes
  • Knowledge Check: Splitting the Data•5 minutes
  • Practice: Splitting the Data•20 minutes

In this module, you investigate the concepts behind the logistic regression model. Then you learn to use the LOGISTIC procedure to fit a logistic regression model. Finally, you learn how to score new cases and adjust the model for oversampling.

What's included

18 videos1 reading4 assignments

18 videos•Total 54 minutes
  • Overview•1 minute
  • Introduction•0 minutes
  • Understanding the Logistic Regression Model•3 minutes
  • Constraining the Posterior Probability Using the Logit Transformation•2 minutes
  • Understanding the Fitted Surface•1 minute
  • Interpreting the Model by Calculating the Odds Ratio•3 minutes
  • Understanding Logistic Discrimination•2 minutes
  • Estimating Unknown Parameters Using Maximum Likelihood Estimation•2 minutes
  • Interpreting Concordant, Discordant, and Tied Pairs•2 minutes
  • Using PROC LOGISTIC to Fit Logistic Regression Models•0 minutes
  • Demo: Fitting a Basic Logistic Regression Model, Part 1•8 minutes
  • Demo: Fitting a Basic Logistic Regression Model, Part 2•12 minutes
  • Scoring New Cases•0 minutes
  • Demo: Scoring New Cases•8 minutes
  • Introduction•0 minutes
  • Understanding the Effect of Oversampling•1 minute
  • Understanding the Offset•2 minutes
  • Demo: Correcting for Oversampling•7 minutes
1 reading•Total 10 minutes
  • Summary•10 minutes
4 assignments•Total 60 minutes
  • Fitting the Model Review•30 minutes
  • Question 2.01•5 minutes
  • Question 2.02•5 minutes
  • Practice: Fitting a Logistic Regression Model•20 minutes

In this module, you learn how to deal with common problems with your predictor variables such as missing values, categorical predictors with many levels, a high number of redundant predictors, and nonlinear relationships with the response variable.

What's included

26 videos9 assignments

26 videos•Total 76 minutes
  • Overview•1 minute
  • Introduction•0 minutes
  • Reasons for Missing Data•3 minutes
  • Complete Case Analysis•2 minutes
  • Methods for Imputing Missing Values•3 minutes
  • Missing Value Imputation with Missing Value Indicator Variables•4 minutes
  • Demo: Imputing Missing Values•4 minutes
  • Cluster Imputation•2 minutes
  • Introduction•0 minutes
  • Problems Caused by Categorical Inputs•4 minutes
  • Solutions to Problems Caused by Categorical Inputs•1 minute
  • Linking to Other Data Sets•1 minute
  • Collapsing Categories by Thresholding•1 minute
  • Collapsing Categories by Using Greenacre's Method•3 minutes
  • Demo: Collapsing the Levels of a Nominal Input, Part 1•6 minutes
  • Demo: Collapsing the Levels of a Nominal Input, Part 2•10 minutes
  • Replacing Categorical Levels by Using Smoothed Weight-of-Evidence Coding•3 minutes
  • Demo: Computing the Smoothed Weight of Evidence•5 minutes
  • Introduction•0 minutes
  • Problem of Redundancy•2 minutes
  • Variable Clustering Method•1 minute
  • Understanding Principal Components•5 minutes
  • Divisive Clustering•4 minutes
  • PROC VARCLUS Syntax•1 minute
  • Selecting a Representative Variable from Each Cluster•1 minute
  • Demo: Reducing Redundancy by Clustering Variables•9 minutes
9 assignments•Total 105 minutes
  • Question 3.01•5 minutes
  • Practice: Imputing Missing Values•20 minutes
  • Question 3.02•5 minutes
  • Question 3.03•5 minutes
  • Question 3.04•5 minutes
  • Practice: Collapsing the Levels of a Nominal Input•20 minutes
  • Practice: Computing the Smoothed Weight of Evidence•20 minutes
  • Question 3.05•5 minutes
  • Practice: Reducing Redundancy by Clustering Variables•20 minutes

In this module, you learn how to select the most predictive variables to use in your model.

What's included

23 videos1 reading12 assignments

23 videos•Total 92 minutes
  • Introduction•0 minutes
  • Detecting Nonlinear Relationships•4 minutes
  • Demo: Performing Variable Screening, Part 1•6 minutes
  • Demo: Performing Variable Screening, Part 2•4 minutes
  • Univariate Binning and Smoothing•3 minutes
  • Demo: Creating Empirical Logit Plots•10 minutes
  • Remedies for Nonlinear Relationships•2 minutes
  • Demo: Accommodating a Nonlinear Relationship, Part 1•6 minutes
  • Demo: Accommodating a Nonlinear Relationship, Part 2•8 minutes
  • Introduction•0 minutes
  • Specifying a Subset Selection Method in PROC LOGISTIC•2 minutes
  • Best-Subsets Selection•1 minute
  • Stepwise Selection•3 minutes
  • Backward Elimination•2 minutes
  • Scalability of the Subset Selection Methods in PROC LOGISTIC•3 minutes
  • Detecting Interactions•3 minutes
  • BIC-based Significance Level•3 minutes
  • Demo: Detecting Interactions•7 minutes
  • Demo: Using Backward Elimination to Subset the Variables•4 minutes
  • Demo: Displaying Odds Ratios for Variables Involved in Interactions•4 minutes
  • Demo: Creating an Interaction Plot•3 minutes
  • Demo: Using the Best-Subsets Selection Method•4 minutes
  • Demo: Using Fit Statistics to Select a Model•10 minutes
1 reading•Total 10 minutes
  • Summary of Preparing the Input Variables, Parts 1 and 2•10 minutes
12 assignments•Total 160 minutes
  • Preparing the Input Variables Review•30 minutes
  • Question 3.06•5 minutes
  • Practice: Performing Variable Screening•20 minutes
  • Practice: Creating Empirical Logit Plots•20 minutes
  • Question 3.07•5 minutes
  • Question 3.08•5 minutes
  • Question 3.09•5 minutes
  • Practice: Using Forward Selection to Detect Interactions•20 minutes
  • Question 3.10•5 minutes
  • Practice: Using Backward Elimination to Subset the Variables•20 minutes
  • Question 3.11•5 minutes
  • Practice: Using Fit Statistics to Select a Model•20 minutes

In this module, you learn how to assess the performance of your model and how to determine allocation rules that maximize profit. Finally, you learn how to generate a family of increasingly complex predictive models and how to select the best model.

What's included

30 videos1 reading9 assignments

30 videos•Total 78 minutes
  • Overview•1 minute
  • Introduction•0 minutes
  • Fit versus Complexity•2 minutes
  • Assessing Models when Target Event Data Is Rare•2 minutes
  • Demo: Preparing the Validation Data•5 minutes
  • Introduction•0 minutes
  • Understanding the Confusion Matrix•5 minutes
  • Measuring Performance across Cutoffs by Using the ROC Curve•4 minutes
  • Choosing Depth by Using the Gains Chart•3 minutes
  • Effects of Oversampled Data on Performance Measures•3 minutes
  • Adjusting a Confusion Matrix for Oversampling•1 minute
  • Demo: Measuring Model Performance Based on Commonly-Used Metrics•7 minutes
  • Introduction•0 minutes
  • Understanding the Effect of Cutoffs on Confusion Matrices•1 minute
  • Understanding the Profit Matrix•2 minutes
  • Choosing the Optimal Cutoff by Using the Profit Matrix•3 minutes
  • Using the Central Cutoff•1 minute
  • Using Profit to Assess Fit•0 minutes
  • Calculating Sampling Weights•1 minute
  • Demo: Using a Profit Matrix to Measure Model Performance•6 minutes
  • Introduction•0 minutes
  • Plotting Class Separation•2 minutes
  • Assessing Overall Predictive Power•3 minutes
  • Demo: Using the K-S Statistic to Measure Model Performance•2 minutes
  • Introduction•0 minutes
  • Comparing ROC Curves of Several Models"•2 minutes
  • Demo: Comparing ROC Curves to Measure Model Performance•4 minutes
  • Using Macros to Compare Many Models•1 minute
  • Demo: Comparing and Evaluating Many Models, Part 1•8 minutes
  • Demo: Comparing and Evaluating Many Models, Part 2•7 minutes
1 reading•Total 10 minutes
  • Summary•10 minutes
9 assignments•Total 85 minutes
  • Measuring Model Performance Review•30 minutes
  • Question 4.01•5 minutes
  • Question 4.02•5 minutes
  • Question 4.03•5 minutes
  • Practice: Assessing Model Performance•20 minutes
  • Question 4.04•5 minutes
  • Question 4.05•5 minutes
  • Question 4.06•5 minutes
  • Question 4.07•5 minutes

What's included

1 reading1 app item

1 reading•Total 10 minutes
  • About the Certification Exam•10 minutes
1 app item•Total 60 minutes
  • Access the Practice Exam•60 minutes

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Instructor

Instructor ratings
4.7 (18 ratings)
SAS
2 Courses•8,870 learners

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Reviewed on Jun 14, 2021

Thank you so much to the instructor, Michael J Patetta for teaching this course!

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