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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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