Predictive Modeling with Logistic Regression using SAS

本课程是 SAS Statistical Business Analyst 专业证书的一部分

位教师：Marc Huber

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40 项作业

授课语言：英语（English）

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

积累 Data Analysis 领域的专业知识

本课程是 SAS Statistical Business Analyst 专业证书专项课程的一部分

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该课程共有7个模块

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.

涵盖的内容

1个视频7篇阅读材料

1个视频总计1分钟

Meet the Instructor 1分钟

7篇阅读材料总计71分钟

What You Learn in This Course 5分钟
Learner Prerequisites 1分钟
Using Forums and Getting Help 5分钟
Access SAS Software for this Course 10分钟
Set Up Data for This Course (REQUIRED) 30分钟
About the Demos and Practices in this Course 10分钟
Frequently Asked Questions 10分钟

In this module, you review the fundamentals of predictive modeling. Then you explore the business scenario data that is used throughout the course. Finally, you learn about common analytical challenges that you might encounter as a modeler.

涵盖的内容

15个视频1篇阅读材料6个作业

15个视频总计34分钟

Overview 1分钟
Introduction 0分钟
Goals of Predictive Modeling 2分钟
Terms for Elements in Predictive Modeling 1分钟
Basic Steps of Predictive Modeling 3分钟
Applications of Predictive Modeling 2分钟
Demonstration Scenario: Target Marketing for a Bank 2分钟
Demo: Examining the Code for Generating Descriptive Statistics and Frequency Tables 2分钟
Introduction 0分钟
Data Challenges 6分钟
Analytical Challenges 2分钟
Separate Sampling 2分钟
Avoiding the Optimism Bias: Honest Assessment 2分钟
Splitting the Data for Model Training and Assessment 3分钟
Demo: Splitting the Data 5分钟

1篇阅读材料总计10分钟

Summary 10分钟

6个作业总计75分钟

Practice: Exploring the Bank Data for the Target Marketing Project 20分钟
Practice: Exploring the Veterans' Organization Data Used in the Practices 20分钟
Question 1.01 5分钟
Question 1.02 5分钟
Question 1.03 5分钟
Practice: Splitting the Data 20分钟

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.

涵盖的内容

18个视频1篇阅读材料4个作业

18个视频总计54分钟

Overview 1分钟
Introduction 0分钟
Understanding the Logistic Regression Model 3分钟
Constraining the Posterior Probability Using the Logit Transformation 2分钟
Understanding the Fitted Surface 1分钟
Interpreting the Model by Calculating the Odds Ratio 3分钟
Understanding Logistic Discrimination 2分钟
Estimating Unknown Parameters Using Maximum Likelihood Estimation 2分钟
Interpreting Concordant, Discordant, and Tied Pairs 2分钟
Using PROC LOGISTIC to Fit Logistic Regression Models 0分钟
Demo: Fitting a Basic Logistic Regression Model, Part 1 8分钟
Demo: Fitting a Basic Logistic Regression Model, Part 2 12分钟
Scoring New Cases 0分钟
Demo: Scoring New Cases 8分钟
Introduction 0分钟
Understanding the Effect of Oversampling 1分钟
Understanding the Offset 2分钟
Demo: Correcting for Oversampling 7分钟

1篇阅读材料总计10分钟

Summary 10分钟

4个作业总计60分钟

Question 2.01 5分钟
Question 2.02 5分钟
Practice: Fitting a Logistic Regression Model 20分钟
Fitting the Model Review 30分钟

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.

涵盖的内容

26个视频9个作业

26个视频总计76分钟

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

9个作业总计105分钟

Question 3.01 5分钟
Practice: Imputing Missing Values 20分钟
Question 3.02 5分钟
Question 3.03 5分钟
Question 3.04 5分钟
Practice: Collapsing the Levels of a Nominal Input 20分钟
Practice: Computing the Smoothed Weight of Evidence 20分钟
Question 3.05 5分钟
Practice: Reducing Redundancy by Clustering Variables 20分钟

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

涵盖的内容

23个视频1篇阅读材料12个作业

23个视频总计92分钟

Introduction 0分钟
Detecting Nonlinear Relationships 4分钟
Demo: Performing Variable Screening, Part 1 6分钟
Demo: Performing Variable Screening, Part 2 4分钟
Univariate Binning and Smoothing 3分钟
Demo: Creating Empirical Logit Plots 10分钟
Remedies for Nonlinear Relationships 2分钟
Demo: Accommodating a Nonlinear Relationship, Part 1 6分钟
Demo: Accommodating a Nonlinear Relationship, Part 2 8分钟
Introduction 0分钟
Specifying a Subset Selection Method in PROC LOGISTIC 2分钟
Best-Subsets Selection 1分钟
Stepwise Selection 3分钟
Backward Elimination 2分钟
Scalability of the Subset Selection Methods in PROC LOGISTIC 3分钟
Detecting Interactions 3分钟
BIC-based Significance Level 3分钟
Demo: Detecting Interactions 7分钟
Demo: Using Backward Elimination to Subset the Variables 4分钟
Demo: Displaying Odds Ratios for Variables Involved in Interactions 4分钟
Demo: Creating an Interaction Plot 3分钟
Demo: Using the Best-Subsets Selection Method 4分钟
Demo: Using Fit Statistics to Select a Model 10分钟

1篇阅读材料总计10分钟

Summary of Preparing the Input Variables, Parts 1 and 2 10分钟

12个作业总计160分钟

Question 3.06 5分钟
Practice: Performing Variable Screening 20分钟
Practice: Creating Empirical Logit Plots 20分钟
Question 3.07 5分钟
Question 3.08 5分钟
Question 3.09 5分钟
Practice: Using Forward Selection to Detect Interactions 20分钟
Question 3.10 5分钟
Practice: Using Backward Elimination to Subset the Variables 20分钟
Question 3.11 5分钟
Practice: Using Fit Statistics to Select a Model 20分钟
Preparing the Input Variables Review 30分钟

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.

涵盖的内容

30个视频1篇阅读材料9个作业

30个视频总计78分钟

Overview 1分钟
Introduction 0分钟
Fit versus Complexity 2分钟
Assessing Models when Target Event Data Is Rare 2分钟
Demo: Preparing the Validation Data 5分钟
Introduction 0分钟
Understanding the Confusion Matrix 5分钟
Measuring Performance across Cutoffs by Using the ROC Curve 4分钟
Choosing Depth by Using the Gains Chart 3分钟
Effects of Oversampled Data on Performance Measures 3分钟
Adjusting a Confusion Matrix for Oversampling 1分钟
Demo: Measuring Model Performance Based on Commonly-Used Metrics 7分钟
Introduction 0分钟
Understanding the Effect of Cutoffs on Confusion Matrices 1分钟
Understanding the Profit Matrix 2分钟
Choosing the Optimal Cutoff by Using the Profit Matrix 3分钟
Using the Central Cutoff 1分钟
Using Profit to Assess Fit 0分钟
Calculating Sampling Weights 1分钟
Demo: Using a Profit Matrix to Measure Model Performance 6分钟
Introduction 0分钟
Plotting Class Separation 2分钟
Assessing Overall Predictive Power 3分钟
Demo: Using the K-S Statistic to Measure Model Performance 2分钟
Introduction 0分钟
Comparing ROC Curves of Several Models" 2分钟
Demo: Comparing ROC Curves to Measure Model Performance 4分钟
Using Macros to Compare Many Models 1分钟
Demo: Comparing and Evaluating Many Models, Part 1 8分钟
Demo: Comparing and Evaluating Many Models, Part 2 7分钟

1篇阅读材料总计10分钟

Summary 10分钟

9个作业总计85分钟

Question 4.01 5分钟
Question 4.02 5分钟
Question 4.03 5分钟
Practice: Assessing Model Performance 20分钟
Question 4.04 5分钟
Question 4.05 5分钟
Question 4.06 5分钟
Question 4.07 5分钟
Measuring Model Performance Review 30分钟

涵盖的内容

1篇阅读材料1个应用程序项目

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

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Predictive Modeling with Logistic Regression using SAS

Predictive Modeling with Logistic Regression using SAS

您将获得的技能

您将学习的工具

要了解的详细信息

了解顶级公司的员工如何掌握热门技能

积累 Data Analysis 领域的专业知识

该课程共有7个模块

Course Overview and Logistics

涵盖的内容

Understanding Predictive Modeling

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Fitting the Model

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Preparing the Input Variables, Part 1

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Preparing the Input Variables, Part 2

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Measuring Model Performance

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SAS Certification Practice Exam - Statistical Business Analysis Using SAS®9: Regression and Modeling

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Logistic Regression with SAS: Build & Evaluate Models

SPSS: Apply & Interpret Logistic Regression Models

Logistic Regression with R: Build & Predict

Predictive Analytics with SAS: Build & Deploy Models

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