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Course Curriculum

0.1a- Meet the Instructor
0.1a- Meet the Instructor 00:00:00
0.1b- What You Learn in This Course
0.1b- What You Learn in This Course 00:00:00
0.1c- Using e-Course Features
0.1c- Using e-Course Features 00:00:00
0.2a- Setup for Base SAS
0.2a- Setup for Base SAS 00:00:00
0.2b- Setup for SAS Studio
0.2b- Setup for SAS Studio 00:00:00
Help and Resources for Predictive Modeling Using Logistic Regression
Help and Resources for Predictive Modeling Using Logistic Regression 00:00:00
1.1a Lesson Overview
1.1a Lesson Overview 00:00:00
1.2a- Introduction
1.2a- Introduction 00:00:00
1.2b- Goals of Predictive Modeling
1.2b- Goals of Predictive Modeling 00:00:00
1.2c- Terms for Elements in Predictive Modeling
1.2c- Terms for Elements in Predictive Modeling 00:00:00
1.2d- Basic Steps of Predictive Modeling
1.2d- Basic Steps of Predictive Modeling 00:00:00
1.2e- Applications of Predictive Modeling
1.2e- Applications of Predictive Modeling 00:00:00
1.2f- Demonstration Scenario Target Marketing for a Bank
1.2f- Demonstration Scenario Target Marketing for a Bank 00:00:00
1.2g-Examining the Code for Generating Descriptive Statistics and Frequency Tables
1.2g-Examining the Code for Generating Descriptive Statistics and Frequency Tables 00:00:00
1.2h- Activity Exploring the Bank Data for the Target Marketing ProjectPractice 1
1.2h- Activity Exploring the Bank Data for the Target Marketing Project Practice 1 00:00:00
1.2h- Activity Answer Exploring the Bank Data for the Target Marketing ProjectPractice 1
1.2h- Activity Answer Exploring the Bank Data for the Target Marketing ProjectPractice 1 00:00:00
1.2i- Practice 1
1.2i- Practice 1 00:00:00
1.2i- Practice 1 Solution
1.2i- Practice 1 Solution 00:00:00
1.3a- Introduction
1.3a- Introduction 00:00:00
1.3b- Data Challenges
1.3b- Data Challenges 00:00:00
1.3c- Analytical Challenges
1.3c- Analytical Challenges 00:00:00
1.3d- Separate Sampling
1.3d- Separate Sampling 00:00:00
1.3f- Avoiding the Optimism Bias Honest Assessment
1.3f- Avoiding the Optimism Bias Honest Assessment 00:00:00
1.3g- Splitting the Data for Model Training and Assessment
1.3g- Splitting the Data for Model Training and Assessment 00:00:00
1.3i- Splitting the Data
1.3i- Splitting the Data 00:00:00
1.3k- Practice 2
1.3k- Practice 2 00:00:00
1.3k- Practice 2 Solution
1.3k- Practice 2 Solution 00:00:00
1.4a- Lesson Summary
1.4a- Lesson Summary 00:00:00
2.1a- Lesson Overview
2.1a- Lesson Overview 00:00:00
2.2a- Introduction
2.2a- Introduction 00:00:00
2.2b- Understanding the Logistic Regression Model
2.2b- Understanding the Logistic Regression Model 00:00:00
2.2c- Constraining the Posterior Probability Using the Logit Transformation
2.2c- Constraining the Posterior Probability Using the Logit Transformation 00:00:00
2.2d- Understanding the Fitted Surface
2.2d- Understanding the Fitted Surface 00:00:00
2.2e- Interpreting the Model by Calculating the Odds Ratio
2.2e- Interpreting the Model by Calculating the Odds Ratio 00:00:00
2.2g- Understanding Logistic Discrimination
2.2g- Understanding Logistic Discrimination 00:00:00
2.2h- Estimating Unknown Parameters Using Maximum Likelihood Estimation
2.2h- Estimating Unknown Parameters Using Maximum Likelihood Estimation 00:00:00
2.2i- Interpreting Concordant, Discordant, and Tied Pairs
2.2i- Interpreting Concordant, Discordant, and Tied Pairs 00:00:00
2.2j- Using PROC LOGISTIC to Fit Logistic Regression Models
2.2j- Using PROC LOGISTIC to Fit Logistic Regression Models 00:00:00
2.2k- Fitting a Basic Logistic Regression Model, Part 1
2.2k- Fitting a Basic Logistic Regression Model, Part 1 00:00:00
2.2l- Fitting a Basic Logistic Regression Model, Part 2
2.2l- Fitting a Basic Logistic Regression Model, Part 2 00:00:00
2.2n- Scoring New Cases
2.2n- Scoring New Cases 00:00:00
2.2o- Scoring New Cases
2.2o- Scoring New Cases 00:00:00
2.3a- Introduction
2.3a- Introduction 00:00:00
2.3b- Understanding the Effect of Oversampling
2.3b- Understanding the Effect of Oversampling 00:00:00
2.3c- Understanding the Offset
2.3c- Understanding the Offset 00:00:00
2.3d- Correcting for Oversampling
2.3d- Correcting for Oversampling 00:00:00
2.3e- Practice 1
2.3e- Practice 1 00:00:00
2.3e- Practice 1 Solution
2.3e- Practice 1 Solution 00:00:00
2.3f- Summary
2.3f- Summary 00:00:00
2.3g- Quiz
2.3g- Quiz 00:00:00
2.3g- Quiz SOlution
2.3g- Quiz Solution 00:00:00
3.1a- Lesson Overview
3.1a- Lesson Overview 00:00:00
3.2a- Introduction
3.2a- Introduction 00:00:00
3.2b- Reasons for Missing Data
3.2b- Reasons for Missing Data 00:00:00
3.2c- Complete Case Analysis
3.2c- Complete Case Analysis 00:00:00
3.2d- Methods for Imputing Missing Values
3.2d- Methods for Imputing Missing Values 00:00:00
3.2e- Missing Value Imputation with Missing Value Indicator Variables
3.2e- Missing Value Imputation with Missing Value Indicator Variables 00:00:00
3.2g- Imputing Missing Values
3.2g- Imputing Missing Values 00:00:00
3.2h- Cluster Imputation
3.2h- Cluster Imputation 00:00:00
3.2i- Practice 1
3.2i- Practice 1 00:00:00
3.2i- Practice 1 Solution
3.2i- Practice 1 Solution 00:00:00
3.3a- Introduction
3.3a- Introduction 00:00:00
3.3b- Activity
3.3b- Activity 00:00:00
3.3b- Activity Solution
3.3b- Activity Solution 00:00:00
3.3c- Problems Caused by Categorical Inputs
3.3c- Problems Caused by Categorical Inputs 00:00:00
3.3d- Solutions to Problems Caused by Categorical Inputs
3.3d- Solutions to Problems Caused by Categorical Inputs 00:00:00
3.3e- Linking to Other Data Sets
3.3e- Linking to Other Data Sets 00:00:00
3.3f- Collapsing Categories by Thresholding
3.3f- Collapsing Categories by Thresholding 00:00:00
3.3g- Collapsing Categories by Using Greenacre's Method
3.3g- Collapsing Categories by Using Greenacre’s Method 00:00:00
3.3i- Collapsing the Levels of a Nominal Input, Part 1
3.3i- Collapsing the Levels of a Nominal Input, Part 1 00:00:00
3.3k- Collapsing the Levels of a Nominal Input, Part 2
3.3k- Collapsing the Levels of a Nominal Input, Part 2 00:00:00
3.3l- Practice 2
3.3l- Practice 2 00:00:00
3.3l- Practice 2 Solution
3.3l- Practice 2 Solution 00:00:00
3.3m- Replacing Categorical Levels by Using Smoothed Weight-of-Evidence Coding
3.3m- Replacing Categorical Levels by Using Smoothed Weight-of-Evidence Coding 00:00:00
3.3n- Computing the Smoothed Weight of Evidence
3.3n- Computing the Smoothed Weight of Evidence 00:00:00
3.3o- Practice 3
3.3o- Practice 3 00:00:00
3.3o- Practice 3 Solution
3.3o- Practice 3 Solution 00:00:00
3.4a- Introduction
3.4a- Introduction 00:00:00
3.4b- Problem of Redundancy
3.4b- Problem of Redundancy 00:00:00
3.4c- Variable Clustering Method
3.4c- Variable Clustering Method 00:00:00
3.4d- Understanding Principal Components
3.4d- Understanding Principal Components 00:00:00
3.4e- Divisive Clustering
3.4e- Divisive Clustering 00:00:00
3.4f- PROC VARCLUS Syntax
3.4f- PROC VARCLUS Syntax 00:00:00
3.4g- Selecting a Representative Variable from Each Cluster
3.4g- Selecting a Representative Variable from Each Cluster 00:00:00
3.4h- Reducing Redundancy by Clustering Variables
3.4h- Reducing Redundancy by Clustering Variables 00:00:00
3.4j- Practice 4
3.4j- Practice 4 00:00:00
3.4j- Practice 4 Answer
3.4j- Practice 4 Answer 00:00:00
3.5a- Introduction
3.5a- Introduction 00:00:00
3.5b- Detecting Nonlinear Relationships
3.5b- Detecting Nonlinear Relationships 00:00:00
3.5c- Performing Variable Screening, Part 1
3.5c- Performing Variable Screening, Part 1 00:00:00
3.5d- Performing Variable Screening, Part 2
3.5d- Performing Variable Screening, Part 2 00:00:00
3.5f- Practice 5
3.5f- Practice 5 00:00:00
3.5f- Practice 5 Solution
3.5f- Practice 5 Solution 00:00:00
3.5g- Univariate Binning and Smoothing
3.5g- Univariate Binning and Smoothing 00:00:00
3.5h- Creating Empirical Logit Plots
3.5h- Creating Empirical Logit Plots 00:00:00
3.5i- Practice 6
3.5i- Practice 6 00:00:00
3.5i- Practice 6 Solution
3.5i- Practice 6 Solution 00:00:00
3.5j- Remedies for Nonlinear Relationships
3.5j- Remedies for Nonlinear Relationships 00:00:00
3.5l- Accommodating a Nonlinear Relationship, Part 1
3.5l- Accommodating a Nonlinear Relationship, Part 1 00:00:00
3.5m- Accommodating a Nonlinear Relationship, Part 2
3.5m- Accommodating a Nonlinear Relationship, Part 2 00:00:00
3.6a- Introduction
3.6a- Introduction 00:00:00
3.6b- Specifying a Subset Selection Method in PROC LOGISTIC
3.6b- Specifying a Subset Selection Method in PROC LOGISTIC 00:00:00
3.6c- Best-Subsets Selection 054
3.6c- Best-Subsets Selection 054 00:00:00
3.6d- Stepwise Selection
3.6d- Stepwise Selection 00:00:00
3.6e- Backward Elimination
3.6e- Backward Elimination 00:00:00
3.6f- Scalability of the Subset Selection Methods in PROC LOGISTIC
3.6f- Scalability of the Subset Selection Methods in PROC LOGISTIC 00:00:00
3.6h- Detecting Interactions
3.6h- Detecting Interactions 00:00:00
3.6i- BIC-based Significance Level
3.6i- BIC-based Significance Level 00:00:00
3.6j- Detecting Interactions
3.6j- Detecting Interactions 00:00:00
3.6k- Practice 7
3.6k- Practice 7 00:00:00
3.6k- Practice 7 SOlution
3.6k- Practice 7 SOlution 00:00:00
3.6l- Using Backward Elimination to Subset the Variables
3.6l- Using Backward Elimination to Subset the Variables 00:00:00
3.6n- Practice 8
3.6n- Practice 8 00:00:00
3.6n- Practice 8 Solution
3.6n- Practice 8 Solution 00:00:00
3.6o- Displaying Odds Ratios for Variables Involved in Interactions
3.6o- Displaying Odds Ratios for Variables Involved in Interactions 00:00:00
3.6p- Creating an Interaction Plot
3.6p- Creating an Interaction Plot 00:00:00
3.6q- Using the Best-Subsets Selection Method
3.6q- Using the Best-Subsets Selection Method 00:00:00
3.6r- Using Fit Statistics to Select a Model
3.6r- Using Fit Statistics to Select a Model 00:00:00
3.6t- Practice 9
3.6t- Practice 9 00:00:00
3.6t- Practice 9 Solution
3.6t- Practice 9 Solution 00:00:00
3.7a Summary
3.7a Summary 00:00:00
3.7b- Quiz
3.7b- Quiz 00:00:00
3.7b- Quiz Solution
3.7b- Quiz Solution 00:00:00
4.1a- Lesson Overview
4.1a- Lesson Overview 00:00:00
4.2a- Introduction
4.2a- Introduction 00:00:00
4.2b- Fit versus Complexity
4.2b- Fit versus Complexity 00:00:00
4.2c- Assessing Models when Target Event Data Is Rare
4.2c- Assessing Models when Target Event Data Is Rare 00:00:00
4.2e- Preparing the Validation Data
4.2e- Preparing the Validation Data 00:00:00
4.3a- Introduction
4.3a- Introduction 00:00:00
4.3b- Understanding the Confusion Matrix
4.3b- Understanding the Confusion Matrix 00:00:00
4.3c- Measuring Performance across Cutoffs by Using the ROC Curve
4.3c- Measuring Performance across Cutoffs by Using the ROC Curve 00:00:00
4.3d- Choosing Depth by Using the Gains Chart
4.3d- Choosing Depth by Using the Gains Chart 00:00:00
4.3e- Effects of Oversampled Data on Performance Measures
4.3e- Effects of Oversampled Data on Performance Measures 00:00:00
4.3f- Adjusting a Confusion Matrix for Oversampling
4.3f- Adjusting a Confusion Matrix for Oversampling 00:00:00
4.3h- Measuring Model Performance Based on Commonly-Used Metrics
4.3h- Measuring Model Performance Based on Commonly-Used Metrics 00:00:00
4.3k- Practice 1
4.3k- Practice 1 00:00:00
4.3k- Practice 1 Solution
4.3k- Practice 1 Solution 00:00:00
4.4a- Introduction
4.4a- Introduction 00:00:00
4.4b- Understanding the Effect of Cutoffs on Confusion Matrices
4.4b- Understanding the Effect of Cutoffs on Confusion Matrices 00:00:00
4.4c- Understanding the Profit Matrix
4.4c- Understanding the Profit Matrix 00:00:00
4.4d- Choosing the Optimal Cutoff by Using the Profit Matrix
4.4d- Choosing the Optimal Cutoff by Using the Profit Matrix 00:00:00
4.4e- Using the Central Cutoff
4.4e- Using the Central Cutoff 00:00:00
4.4f- Using Profit to Assess Fit
4.4f- Using Profit to Assess Fit 00:00:00
4.4h- Calculating Sampling Weights
4.4h- Calculating Sampling Weights 00:00:00
4.4i- Using a Profit Matrix to Measure Model Performance
4.4i- Using a Profit Matrix to Measure Model Performance 00:00:00
4.5a- Introduction
4.5a- Introduction 00:00:00
4.5b- Plotting Class Separation
4.5b- Plotting Class Separation 00:00:00
4.5c- Assessing Overall Predictive Power
4.5c- Assessing Overall Predictive Power 00:00:00
4.5d- Using the K-S Statistic to Measure Model Performance
4.5d- Using the K-S Statistic to Measure Model Performance 00:00:00
4.6a- Introduction
4.6a- Introduction 00:00:00
4.6b- Comparing ROC Curves of Several Models
4.6b- Comparing ROC Curves of Several Models 00:00:00
4.6c- Comparing ROC Curves to Measure Model Performance
4.6c- Comparing ROC Curves to Measure Model Performance 00:00:00
4.6d- Using Macros to Compare Many Model
4.6d- Using Macros to Compare Many Model 00:00:00
4.6e- Comparing and Evaluating Many Models, Part 1
4.6e- Comparing and Evaluating Many Models, Part 1 00:00:00
4.6f- Comparing and Evaluating Many Models, Part 2
4.6f- Comparing and Evaluating Many Models, Part 2 00:00:00
4.7a- Summary
4.7a- Summary 00:00:00
4.7b- Quiz
4.7b- Quiz 00:00:00
4.7b- Quiz Answer
4.7b- Quiz Answer 00:00:00
Course Notes Predictive Modeling Using Logistic Regression File
Course Notes Predictive Modeling Using Logistic Regression File 00:00:00

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