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Lessons

This course does not require any data setup before you begin, but you might want to start the Virtual Lab so you are ready to do exercises. The demos and exercises include instructions for accessing and working with the course data. When you come to a demo, click the Text Version button to open demonstration steps that you can follow.

Note: The demonstrations and practices in this course build on one another. If you intend to follow along, you should take these lessons in order and complete each lesson before moving to the next one.

Course Curriculum

Module 3 e-Course Features
Module 3 e-Course Features Details 00:00:00
1.1a Lesson 1 Introduction to Using SAS Enterprise Miner Course Overview
1.1a Lesson 1 Introduction to Using SAS Enterprise Miner Course Overview Details 00:00:00
1.2a The Analytic Workflow
1.2a The Analytic Workflow Details 00:00:00
1.2b Interface Tour
1.2b Interface Tour Details 00:00:00
1.2c The SEMMA Architecture
1.2c The SEMMA Architecture Details 00:00:00
1.2c1 Tools on the Sample Tab
1.2c1 Tools on the Sample Tab Details 00:00:00
1.2c2 Tools on the Explore Tab
1.2c2 Tools on the Explore Tab Details 00:00:00
1.2c3 Tools on the Model Tab
1.2c3 Tools on the Model Tab Details 00:00:00
1.2c4 Tools on the Modify Tab
1.2c4 Tools on the Modify Tab Details 00:00:00
1.2c5 Tools on the Assess Tab
1.2c5 Tools on the Assess Tab Details 00:00:00
1.2c6 Tools on the Utility Tab
1.2c6 Tools on the Utility Tab Details 00:00:00
1.2c7 Tools on the Credit Scoring Tab
1.2c7 Tools on the Credit Scoring Tab Details 00:00:00
1.2c8 Tools on the HPDM Tab
1.2c8 Tools on the HPDM Tab Details 00:00:00
1.2c9 Tools on the Applications Tab
1.2c9 Tools on the Applications Tab Details 00:00:00
1.2c10 The Text MiningTab Toolbar
1.2c10 The Text MiningTab Toolbar Details 00:00:00
1.2c11 Tools on the Time Series Tab
1.2c11 Tools on the Time Series Tab Details 00:00:00
1.2d SAS Enterprise Miner Architecture and Configuration
1.2d SAS Enterprise Miner Architecture and Configuration Details 00:00:00
1.3a The Business Problem Segmentation Analysis
1.3a The Business Problem Segmentation Analysis Details 00:00:00
1.3a1 Behind the Scenes Organization
1.3a1 Behind the Scenes Organization Details 00:00:00
1.4a What Is a Project
1.4a What Is a Project Details 00:00:00
1.4b Creating a New Project and a New Diagram
1.4b Creating a New Project and a New Diagram Details 00:00:00
1.5a What Is a Data Source
1.5a What Is a Data Source Details 00:00:00
1.5b Creating a New Data Source
1.5b Creating a New Data Source Details 00:00:00
1.6a Using the Explore Window
1.6a Using the Explore Window Details 00:00:00
1.6b Using the Plot Wizard
1.6b Using the Plot Wizard Details 00:00:00
1.6c Exploring the Variables in the CENSUS2000 Data
1.6c Exploring the Variables in the CENSUS2000 Data Details 00:00:00
1.6d Examining the Results
1.6d Examining the Results Details 00:00:00
1.6e Editing the Sample Method Property
1.6e Editing the Sample Method Property Details 00:00:00
1.6f Using the Variable Plot Window
1.6f Using the Variable Plot Window Details 00:00:00
1.6g Exploring Variables in the CENSUS2000 Data
1.6g Exploring Variables in the CENSUS2000 Data Details 00:00:00
1.6h Examining the Results
1.6h Examining the Results Details 00:00:00
1.7a Introduction to Filtering Data
1.7a Introduction to Filtering Data Details 00:00:00
1.7b Adding an Input Data Node and a Filter Node
1.7b Adding an Input Data Node and a Filter Node Details 00:00:00
1.8a The Business Problem Market Basket Analysis
1.8a The Business Problem Market Basket Analysis Details 00:00:00
1.8b Practice Create a New Diagram and a New Data Source Quiz
1.8b Practice Create a New Diagram and a New Data Source Quiz Details 00:00:00
1.8b Practice Create a New Diagram and a New Data Source Quiz Solution
1.8b Practice Create a New Diagram and a New Data Source Quiz Solution Details 00:00:00
1.8c Practice Explore a Data Source and Add It to a Diagram
1.8c Practice Explore a Data Source and Add It to a Diagram Details 00:00:00
1.8c Practice Explore a Data Source and Add It to a Diagram Solution
1.8c Practice Explore a Data Source and Add It to a Diagram Solution Details 00:00:00
Chapter 1 Lesson Summary
Chapter 1 Lesson Summary Details 00:00:00
Chapter 1 Quiz
Chapter 1 Quiz Details 00:00:00
Chapter 1 Quiz Feedback
Chapter 1 Quiz Feedback Details 00:00:00
2.1a Lesson 2 Pattern Discovery Using SAS Enterprise Miner Lesson Overview
2.1a Lesson 2 Pattern Discovery Using SAS Enterprise Miner Lesson Overview Details 00:00:00
2.2a Understanding Pattern Discovery Introduction
2.2a Understanding Pattern Discovery Introduction Details 00:00:00
2.2b Applications of Pattern Discovery
2.2b Applications of Pattern Discovery Details 00:00:00
2.2c Pattern Discovery Tools in SAS Enterprise Miner
2.2c Pattern Discovery Tools in SAS Enterprise Miner Details 00:00:00
2.3a Cluster Analysis Introduction
2.3a Cluster Analysis Introduction Details 00:00:00
2.3b Selecting and Refining Inputs for Analysis
2.3b Selecting and Refining Inputs for Analysis Details 00:00:00
2.3c Determining the Number of Clusters to Create
2.3c Determining the Number of Clusters to Create Details 00:00:00
2.3d k-Means Clustering Algorithm
2.3d k-Means Clustering Algorithm Details 00:00:00
2.3e The Business Problem Segmentation Analysis
2.3e The Business Problem Segmentation Analysis Details 00:00:00
2.3f Selecting and Examining Inputs for Clustering
2.3f Selecting and Examining Inputs for Clustering Details 00:00:00
2.3g Transforming Variables to Fix Skewed Data
2.3g Transforming Variables to Fix Skewed Data Details 00:00:00
2.3h Adding a Cluster Node
2.3h Adding a Cluster Node Details 00:00:00
2.3i Evaluating the Number of Clusters
2.3i Evaluating the Number of Clusters Details 00:00:00
2.3j Specifying the Number of Clusters
2.3j Specifying the Number of Clusters Details 00:00:00
2.3k Viewing the Preliminary Cluster Analysis Results
2.3k Viewing the Preliminary Cluster Analysis Results Details 00:00:00
2.3l Creating Plots with the Graph Wizard
2.3l Creating Plots with the Graph Wizard Details 00:00:00
2.3m Profiling Segments
2.3m Profiling Segments Details 00:00:00
2.3n Creating a Scatter Plot of the Input Variables
2.3n Creating a Scatter Plot of the Input Variables Details 00:00:00
2.3o Creating a Bar Chart and a Plot
2.3o Creating a Bar Chart and a Plot Details 00:00:00
2.3p Using the Segment Profile Tool
2.3p Using the Segment Profile Tool Details 00:00:00
2.3q Practice Perform a Cluster Analysis
2.3q Practice Perform a Cluster Analysis Details 00:00:00
2.3q Practice Perform a Cluster Analysis With Solution
2.3q Practice Perform a Cluster Analysis With Solution Details 00:00:00
2.4a Market Basket Analysis Introduction
2.4a Market Basket Analysis Introduction Details 00:00:00
2.4b Evaluating the Strength of an Association
2.4b Evaluating the Strength of an Association Details 00:00:00
2.4c Interpreting Association Rules
2.4c Interpreting Association Rules Details 00:00:00
2.4d Association Analysis in SAS Enterprise Miner
2.4d Association Analysis in SAS Enterprise Miner Details 00:00:00
2.4e The Business Problem Market Basket Analysis
2.4e The Business Problem Market Basket Analysis Details 00:00:00
2.4f Adding an Associations Node
2.4f Adding an Associations Node Details 00:00:00
2.4g Running the Associations Node
2.4g Running the Associations Node Details 00:00:00
2.4h Using the Link Graph and Rules Table to Interpret the Results
2.4h Using the Link Graph and Rules Table to Interpret the Results Details 00:00:00
2.4i Exploring Associations by Using the Link Graph and Rules Table
2.4i Exploring Associations by Using the Link Graph and Rules Table Details 00:00:00
2.5a Sequence Analysis Introduction
2.5a Sequence Analysis Introduction Details 00:00:00
2.5b Performing a Sequence Analysis for the BANK Data
2.5b Performing a Sequence Analysis for the BANK Data Details 00:00:00
2.5c Practice Conduct a Market Basket Analysis
2.5c Practice Conduct a Market Basket Analysis Details 00:00:00
2.5c Practice Conduct a Market Basket Analysis Solution
2.5c Practice Conduct a Market Basket Analysis Solution Details 00:00:00
Chapter 2 Lesson Summary
Chapter 2 Lesson Summary Details 00:00:00
Chapter 2 Quiz
Chapter 2 Quiz Details 00:00:00
Chapter 2 Quiz Feedback
Chapter 2 Quiz Feedback Details 00:00:00
3.1a Introduction to Predictive Modeling Using SAS Enterprise Miner Lesson Overview
3.1a Introduction to Predictive Modeling Using SAS Enterprise Miner Lesson Overview Details 00:00:00
3.1b Predictive Modeling Applications
3.1b Predictive Modeling Applications Details 00:00:00
3.1c Training Data
3.1c Training Data Details 00:00:00
3.1d Score Data
3.1d Score Data Details 00:00:00
3.2a The Business Problem Donations Analysis
3.2a The Business Problem Donations Analysis Details 00:00:00
3.2b Understanding the Data
3.2b Understanding the Data Details 00:00:00
3.2b Understanding the Data SAS Data Table PVA97NK
3.2b Understanding the Data SAS Data Table PVA97NK Details 00:00:00
3.3a Using the Advanced Metadata Advisor
3.3a Using the Advanced Metadata Advisor Details 00:00:00
3.3b Defining a Data Source
3.3b Defining a Data Source Details 00:00:00
3.3c Predictive Modeling Tools in SAS Enterprise Miner
3.3c Predictive Modeling Tools in SAS Enterprise Miner Details 00:00:00
3.3d A Predictive Model's Tasks
3.3d A Predictive Model’s Tasks Details 00:00:00
3.4a Predicting New Cases Introduction
3.4a Predicting New Cases Introduction Details 00:00:00
3.4b Decisions
3.4b Decisions Details 00:00:00
3.4c Rankings
3.4c Rankings Details 00:00:00
3.4d Estimates
3.4d Estimates Details 00:00:00
3.5a Selecting Useful Inputs The Curse of Dimensionality
3.5a Selecting Useful Inputs The Curse of Dimensionality Details 00:00:00
3.5b Redundancy and Irrelevancy
3.5b Redundancy and Irrelevancy Details 00:00:00
3.6a Optimizing Complexity Model Complexity
3.6a Optimizing Complexity Model Complexity Details 00:00:00
3.7a Partitioning the Data Introduction
3.7a Partitioning the Data Introduction Details 00:00:00
3.7b Choosing a Model
3.7b Choosing a Model Details 00:00:00
3.7c Using the Data Partition Tool
3.7c Using the Data Partition Tool Details 00:00:00
3.7d Creating a Diagram and Using the Data Partition Tool
3.7d Creating a Diagram and Using the Data Partition Tool Details 00:00:00
3.8a Practicing What You've Learned The Business Problem Organic Purchase Analysis
3.8a Practicing What You’ve Learned The Business Problem Organic Purchase Analysis Details 00:00:00
3.8b Practice Define the Data Source for the Organic Purchase Analysis Project
3.8b Practice Define the Data Source for the Organic Purchase Analysis Project Details 00:00:00
3.8b Practice Define the Data Source for the Organic Purchase Analysis Project Solution
3.8b Practice Define the Data Source for the Organic Purchase Analysis Project Solution Details 00:00:00
3.8c Practice Create a diagram and partition the input data
3.8c Practice Create a diagram and partition the input data Details 00:00:00
3.8c Practice Create a diagram and partition the input data Solution
3.8c Practice Create a diagram and partition the input data Solution Details 00:00:00
Chapter 3 Lesson Summary
Chapter 3 Lesson Summary Details 00:00:00
Chapter 3 Quiz
Chapter 3 Quiz Details 00:00:00
Chapter 3 Quiz Feedback
Chapter 3 Quiz Feedback Details 00:00:00
4.1a Lesson 4 Decision Trees Using SAS Enterprise Miner Introduction
4.1a Lesson 4 Decision Trees Using SAS Enterprise Miner Introduction Details 00:00:00
4.2a Understanding Decision Trees Introduction
4.2a Understanding Decision Trees Introduction Details 00:00:00
4.2b Constructing Decision Trees
4.2b Constructing Decision Trees Details 00:00:00
4.2c Decision Tree Modeling Essentials
4.2c Decision Tree Modeling Essentials Details 00:00:00
4.2d Prediction Rules
4.2d Prediction Rules Details 00:00:00
4.2e Simple Prediction Illustration
4.2e Simple Prediction Illustration Details 00:00:00
4.2f Decision Tree Nodes
4.2f Decision Tree Nodes Details 00:00:00
4.2g Decision Tree Leaves
4.2g Decision Tree Leaves Details 00:00:00
4.2h Split Search
4.2h Split Search Details 00:00:00
4.2i Factors That Complicate Split Search
4.2i Factors That Complicate Split Search Details 00:00:00
4.2j Depth Adjustment
4.2j Depth Adjustment Details 00:00:00
4.3a The Business ProblemDonations Analysis
4.3a The Business ProblemDonations Analysis Details 00:00:00
4.3b Understanding the Data
4.3b Understanding the Data Details 00:00:00
4.4a Interactively Creating a Decision Tree with One Split
4.4a Interactively Creating a Decision Tree with One Split Details 00:00:00
4.4b Interval Splitting
4.4b Interval Splitting Details 00:00:00
4.4c Defining Splits in Your Tree
4.4c Defining Splits in Your Tree Details 00:00:00
4.4d Automatic Growing
4.4d Automatic Growing Details 00:00:00
4.4e Growing a Decision Tree Automatically
4.4e Growing a Decision Tree Automatically Details 00:00:00
4.4f Interpreting Results
4.4f Interpreting Results Details 00:00:00
4.5a Understanding Pruning Introduction
4.5a Understanding Pruning Introduction Details 00:00:00
4.5b Pruning Strategy
4.5b Pruning Strategy Details 00:00:00
4.5c Binary targets
4.5c Binary targets Details 00:00:00
4.5d Decision Assessment
4.5d Decision Assessment Details 00:00:00
4.5e Ranking Assessment
4.5e Ranking Assessment Details 00:00:00
4.5f Estimate Assessment (only Pessimistic)
4.5f Estimate Assessment (only Pessimistic) Details 00:00:00
4.5g Average Square Error
4.5g Average Square Error Details 00:00:00
4.5h Predictive Modeling Assessments
4.5h Predictive Modeling Assessments Details 00:00:00
4.6a Using the Results Window to Assess a Decision Tree
4.6a Using the Results Window to Assess a Decision Tree Details 00:00:00
4.6b Interpreting Results
4.6b Interpreting Results Details 00:00:00
4.6c Autonomous Tree Growth
4.6c Autonomous Tree Growth Details 00:00:00
4.6d Autonomous Tree Growth Options
4.6d Autonomous Tree Growth Options Details 00:00:00
4.6e Growing a Decision Tree Autonomously
4.6e Growing a Decision Tree Autonomously Details 00:00:00
4.7a Multi-Way Splits
4.7a Multi-Way Splits Details 00:00:00
4.7b Split Worth Criteria
4.7b Split Worth Criteria Details 00:00:00
4.7c Three Methods of Calculation
4.7c Three Methods of Calculation Details 00:00:00
4.7d Stopping Rules
4.7d Stopping Rules Details 00:00:00
4.7e Method and Assessment Measure Properties
4.7e Method and Assessment Measure Properties Details 00:00:00
4.7f Missing Values and Surrogate Rules
4.7f Missing Values and Surrogate Rules Details 00:00:00
4.8a The Business Problem Organic Purchase Analysis
4.8a The Business Problem Organic Purchase Analysis Details 00:00:00
4.8b Practice Create and Compare Two Different Decision Trees
4.8b Practice Create and Compare Two Different Decision Trees Details 00:00:00
4.8b Practice Create and Compare Two Different Decision Trees Solution
4.8b Practice Create and Compare Two Different Decision Trees Solution Details 00:00:00
Chapter 4 Lesson Summary
Chapter 4 Lesson Summary Details 00:00:00
Chapter 4 Quiz
Chapter 4 Quiz Details 00:00:00
Chapter 4 Quiz Feedback
Chapter 4 Quiz Feedback Details 00:00:00
5.1a Lesson 5 Regression Models Using SAS Enterprise Miner Introduction
5.1a Lesson 5 Regression Models Using SAS Enterprise Miner Introduction Details 00:00:00
5.2a Understanding Model Essentials
5.2a Understanding Model Essentials Details 00:00:00
5.2b Linear Regression
5.2b Linear Regression Details 00:00:00
5.2c Logistic Regression
5.2c Logistic Regression Details 00:00:00
5.2d The Logit Link Function Used to Make Predictions
5.2d The Logit Link Function Used to Make Predictions Details 00:00:00
5.2e Properties of a Logistic Regression Model
5.2e Properties of a Logistic Regression Model Details 00:00:00
5.2f Prediction Estimates
5.2f Prediction Estimates Details 00:00:00
5.3a The Business Problem Donations Analysis
5.3a The Business Problem Donations Analysis Details 00:00:00
5.3b Understanding the Data SAS Data Table PVA97NK
5.3b Understanding the Data SAS Data Table PVA97NK Details 00:00:00
5.3b Understanding the Data
5.3b Understanding the Data Details 00:00:00
5.4a Beyond the Prediction Formula
5.4a Beyond the Prediction Formula Details 00:00:00
5.4b Missing Values and Regression Modeling
5.4b Missing Values and Regression Modeling Details 00:00:00
5.4c The Prediction Formula Used to Build Models
5.4c The Prediction Formula Used to Build Models Details 00:00:00
5.4d Missing Values and Replacement Strategies
5.4d Missing Values and Replacement Strategies Details 00:00:00
5.4e Dealing with Missing Values
5.4e Dealing with Missing Values Details 00:00:00
5.4f Using the Impute Tool to Create New Values
5.4f Using the Impute Tool to Create New Values Details 00:00:00
5.5a Using the Regression Tool
5.5a Using the Regression Tool Details 00:00:00
5.5b Running a Regression Node
5.5b Running a Regression Node Details 00:00:00
5.5c Interpreting the Results of the Regression Tool
5.5c Interpreting the Results of the Regression Tool Details 00:00:00
5.6a Selecting Useful Regression Inputs Introduction
5.6a Selecting Useful Regression Inputs Introduction Details 00:00:00
5.6b Example Forward Sequential Selection
5.6b Example Forward Sequential Selection Details 00:00:00
5.6c Example Backward Sequential Selection
5.6c Example Backward Sequential Selection Details 00:00:00
5.6d Example Stepwise Sequential Selection
5.6d Example Stepwise Sequential Selection Details 00:00:00
5.6e Using the Stepwise Selection Method to Perform Regression
5.6e Using the Stepwise Selection Method to Perform Regression Details 00:00:00
5.6f Interpreting the Results of a Stepwise Regression
5.6f Interpreting the Results of a Stepwise Regression Details 00:00:00
5.7a Optimizing Regression Complexity
5.7a Optimizing Regression Complexity Details 00:00:00
5.7b Choosing the Optimal Model in the Selection Sequence
5.7b Choosing the Optimal Model in the Selection Sequence Details 00:00:00
5.7c Interpreting Results to Choose an Optimal Model
5.7c Interpreting Results to Choose an Optimal Model Details 00:00:00
5.8a Transforming Inputs Introduction
5.8a Transforming Inputs Introduction Details 00:00:00
5.8b Using the Tranform Variables Tool to Transform Inputs
5.8b Using the Tranform Variables Tool to Transform Inputs Details 00:00:00
5.8c Interpreting Results Based on Transformed Variables
5.8c Interpreting Results Based on Transformed Variables Details 00:00:00
5.9a Categorical Input Coding and Consolidation
5.9a Categorical Input Coding and Consolidation Details 00:00:00
5.9b Using the Replacement Tool to Combine Input Levels
5.9b Using the Replacement Tool to Combine Input Levels Details 00:00:00
5.9c Interpreting Results After Collapsing Categorical Inputs
5.9c Interpreting Results After Collapsing Categorical Inputs Details 00:00:00
5.10a Standard Logistic Regression
5.10a Standard Logistic Regression Details 00:00:00
5.10b Adding Polynomial Regression Terms Selectively
5.10b Adding Polynomial Regression Terms Selectively Details 00:00:00
5.10c Adding Polynomial Regression Terms Autonomously
5.10c Adding Polynomial Regression Terms Autonomously Details 00:00:00
5.10d Interpreting the Results of Polynomial Regression Run Autonomously
5.10d Interpreting the Results of Polynomial Regression Run Autonomously Details 00:00:00
5.11a The Business Problem Organic Purchase Analysis
5.11a The Business Problem Organic Purchase Analysis Details 00:00:00
5.11b Practice Impute Missing Values and Run the Regression Node
5.11b Practice Impute Missing Values and Run the Regression Node Details 00:00:00
5.11b Practice Impute Missing Values and Run the Regression Node
5.11b Practice Impute Missing Values and Run the Regression Node Solution Details 00:00:00
6.1a Neural Networks Using SAS Enterprise Miner Introduction
6.1a Neural Networks Using SAS Enterprise Miner Introduction Details 00:00:00
6.1b Neural Network Structure
6.1b Neural Network Structure Details 00:00:00
6.2a The Neural Network Prediction Formula Introduction
6.2a The Neural Network Prediction Formula Introduction Details 00:00:00
6.2b Establishing a Prediction Formula
6.2b Establishing a Prediction Formula Details 00:00:00
6.2c Obtaining a Prediction
6.2c Obtaining a Prediction Details 00:00:00
6.2d Beyond the Prediction Formula
6.2d Beyond the Prediction Formula Details 00:00:00
6.3a The Business Problem Donations Analysis
6.3a The Business Problem Donations Analysis Details 00:00:00
6.3b Understanding the Data
6.3b Understanding the Data Details 00:00:00
6.4b Adding a Neural Network Node to the Diagram
6.4b Adding a Neural Network Node to the Diagram Details 00:00:00
6.4c Exploring the Results
6.4c Exploring the Results Details 00:00:00
6.4e Exploring the Results
6.4e Exploring the Results Details 00:00:00
6.5a Selecting Neural Netowrk Inputs
6.5a Selecting Neural Netowrk Inputs Details 00:00:00
6.5b Reducing the Number of Inputs in the Neural Network Model
6.5b Reducing the Number of Inputs in the Neural Network Model Details 00:00:00
6.6a Optimizing Neural Netowrk Complexity Introduction
6.6a Optimizing Neural Netowrk Complexity Introduction Details 00:00:00
6.6b Fit Statistic versus Optimization Iteration
6.6b Fit Statistic versus Optimization Iteration Details 00:00:00
6.6c Example Fit Statistic versus Optimization Iteration
6.6c Example Fit Statistic versus Optimization Iteration Details 00:00:00
6.6d Changing the Number of Hidden Units Manually
6.6d Changing the Number of Hidden Units Manually Details 00:00:00
6.7a Using the AutoNeural Tool
6.7a Using the AutoNeural Tool Details 00:00:00
6.7b Using the Autoneural Tool to Explore Neural Networks with Increasing Hidden Unit Costs
6.7b Using the Autoneural Tool to Explore Neural Networks with Increasing Hidden Unit Costs Details 00:00:00
6.7c Exploring the Output
6.7c Exploring the Output Details 00:00:00
6.8a The Business Problem Organic Purchase Analysis
6.8a The Business Problem Organic Purchase Analysis Details 00:00:00
6.8b Practice Add a Neural Network Node to the Diagram
6.8b Practice Add a Neural Network Node to the Diagram Details 00:00:00
6.8b Practice Add a Neural Network Node to the Diagram Solution
6.8b Practice Add a Neural Network Node to the Diagram Solution Details 00:00:00
6.9a Other Modeling Tools Introduction
6.9a Other Modeling Tools Introduction Details 00:00:00
6.9b Rule Introduction
6.9b Rule Introduction Details 00:00:00
6.9c Dmine Regression
6.9c Dmine Regression Details 00:00:00
6.9d DMNeural
6.9d DMNeural Details 00:00:00
6.9e Memory-Based Reasoning (MBR)
6.9e Memory-Based Reasoning (MBR) Details 00:00:00
Chapter 6 Lesson Summary
Chapter 6 Lesson Summary Details 00:00:00
Chapter 6 Quiz
Chapter 6 Quiz Details 00:00:00
Chapter 6 Quiz Feedback
Chapter 6 Quiz Feedback Details 00:00:00
7.1a Lesson 7Model Assessment Using SAS Enterprise Miner Introduction
7.1a Lesson 7Model Assessment Using SAS Enterprise Miner Introduction Details 00:00:00
7.1b Understanding Model Assessment
7.1b Understanding Model Assessment Details 00:00:00
7.1b Understanding Model Assessment Tools on the Assess Tab
7.1b Understanding Model Assessment Tools on the Assess Tab Details 00:00:00
7.2a The Business Problem Donations Analysis
7.2a The Business Problem Donations Analysis Details 00:00:00
7.2b Understanding the Data
7.2b Understanding the Data Details 00:00:00
7.3a Comparing Models
7.3a Comparing Models Details 00:00:00
7.3b About Summary Statistics
7.3b About Summary Statistics Details 00:00:00
7.3c Viewing Summary Statistics for the Model Comparison Node
7.3c Viewing Summary Statistics for the Model Comparison Node Details 00:00:00
7.3d Interpreting the Summary Statistics in the Output Window
7.3d Interpreting the Summary Statistics in the Output Window Details 00:00:00
7.3e About Statistical Graphics
7.3e About Statistical Graphics Details 00:00:00
7.3e Applying a Model to Validation Data
7.3e Applying a Model to Validation Data Details 00:00:00
7.3f Sensitivity Charts
7.3f Sensitivity Charts Details 00:00:00
7.3g ROC Charts
7.3g ROC Charts Details 00:00:00
7.3h Response Rate Charts
7.3h Response Rate Charts Details 00:00:00
7.3i Cumulative Gains Charts and Cumulative Lift Charts
7.3i Cumulative Gains Charts and Cumulative Lift Charts Details 00:00:00
7.3j Viewing Statistical Graphics for the Model Comparison Node
7.3j Viewing Statistical Graphics for the Model Comparison Node Details 00:00:00
7.4a What Is Separate Sampling
7.4a What Is Separate Sampling Details 00:00:00
7.4b Adjusting for Separate Sampling
7.4b Adjusting for Separate Sampling Details 00:00:00
7.4c Decision Processing
7.4c Decision Processing Details 00:00:00
7.4d Adjusting for Separate Sampling
7.4d Adjusting for Separate Sampling Details 00:00:00
7.4e Interpreting Your Results
7.4e Interpreting Your Results Details 00:00:00
7.5a About Profit Matrices
7.5a About Profit Matrices Details 00:00:00
7.5b Defining Outcomes and Actions for the Donations Analysis
7.5b Defining Outcomes and Actions for the Donations Analysis Details 00:00:00
7.5c Defining Profit Consequences for the Solicit Decision
7.5c Defining Profit Consequences for the Solicit Decision Details 00:00:00
7.5e Creating a Profit Matrix
7.5e Creating a Profit Matrix Details 00:00:00
7.5g Interpreting the Output from the Model Comparison Tool
7.5g Interpreting the Output from the Model Comparison Tool Details 00:00:00
7.5h Calculating Expected Profit
7.5h Calculating Expected Profit Details 00:00:00
7.5i Using Average Profit to Evaluate Model Performance
7.5i Using Average Profit to Evaluate Model Performance Details 00:00:00
7.5j Evaluating Model Profit
7.5j Evaluating Model Profit Details 00:00:00
7.5k Cumulative % Captured Response Chart for the Donations Analysis
7.5k Cumulative % Captured Response Chart for the Donations Analysis Details 00:00:00
7.5l Optimizing with Profit
7.5l Optimizing with Profit Details 00:00:00
7.6a The Business Problem Organic Purchase Analysis
7.6a The Business Problem Organic Purchase Analysis Details 00:00:00
7.6a The Business Problem Organic Purchase Analysis SAS Data Table Organics
7.6a The Business Problem Organic Purchase Analysis SAS Data Table Organics Details 00:00:00
Chapter 7 Lesson Summary
Chapter 7 Lesson Summary Details 00:00:00
Chapter 7 Quiz
Chapter 7 Quiz Details 00:00:00
Chapter 7 Quiz Feedback
Chapter 7 Quiz Feedback Details 00:00:00
8.1a Lesson 8 Model Implementation Using SAS Enterprise Miner
8.1a Lesson 8 Model Implementation Using SAS Enterprise Miner Details 00:00:00
8.1b What Is Model Implementation
8.1b What Is Model Implementation Details 00:00:00
8.2a The Business Problem Donations Analysis
8.2a The Business Problem Donations Analysis Details 00:00:00
8.2b Understanding the Data
8.2b Understanding the Data Details 00:00:00
8.3a Internally Scored Data Sets
8.3a Internally Scored Data Sets Details 00:00:00
8.3b Creating a Score Data Source
8.3b Creating a Score Data Source Details 00:00:00
8.3c Scoring New Data Using the Score Tool
8.3c Scoring New Data Using the Score Tool Details 00:00:00
8.4a Exporting a Scored Table Introduction
8.4a Exporting a Scored Table Introduction Details 00:00:00
8.4b Exporting the Scored Data Set from the Score Node
8.4b Exporting the Scored Data Set from the Score Node Details 00:00:00
8.5a Score Code Modules
8.5a Score Code Modules Details 00:00:00
8.5b Examining Score Code (Continued)
8.5b Examining Score Code (Continued) Details 00:00:00
8.5c Creating a SAS Score Code Module
8.5c Creating a SAS Score Code Module Details 00:00:00
8.5d Creating C Score Code and Java Score
8.5d Creating C Score Code and Java Score Details 00:00:00
8.6a The Business Problem Organic Purchase Analysis
8.6a The Business Problem Organic Purchase Analysis Details 00:00:00
8.6b Practice Create a Score Data Source and Score It Using the Score Tool
8.6b Practice Create a Score Data Source and Score It Using the Score Tool Details 00:00:00
8.6b Practice Create a Score Data Source and Score It Using the Score Tool Solution
8.6b Practice Create a Score Data Source and Score It Using the Score Tool Solution Details 00:00:00
Chapter 8 Lesson Summary
Chapter 8 Lesson Summary Details 00:00:00
Chapter 8 Quiz
Chapter 8 Quiz Details 00:00:00
Chapter 8 Quiz Feedback
Chapter 8 Quiz Feedback Details 00:00:00
9.1a Lesson 9 Special Topics Using SAS Enterprise Miner Lesson Overview
9.1a Lesson 9 Special Topics Using SAS Enterprise Miner Lesson Overview Details 00:00:00
9.2a The Business Problem Donations Analysis
9.2a The Business Problem Donations Analysis Details 00:00:00
9.2b Understanding the Data
9.2b Understanding the Data Details 00:00:00
9.3a Ensemble Models
9.3a Ensemble Models Details 00:00:00
9.3b Creating an Ensemble Model
9.3b Creating an Ensemble Model Details 00:00:00
9.3c Viewing the Results
9.3c Viewing the Results Details 00:00:00
9.4a Input Selection Alternatives
9.4a Input Selection Alternatives Details 00:00:00
9.4b Variable Selection Node
9.4b Variable Selection Node Details 00:00:00
9.4c Working with the Variable Selection Tool
9.4c Working with the Variable Selection Tool Details 00:00:00
9.4d Additional Available Options
9.4d Additional Available Options Details 00:00:00
9.4e Using the Decision Tree Tool for Selecting Inputs
9.4e Using the Decision Tree Tool for Selecting Inputs Details 00:00:00
9.4f Variable Importance Output
9.4f Variable Importance Output Details 00:00:00
9.5a Categorical Input Consolidation
9.5a Categorical Input Consolidation Details 00:00:00
9.5b Consolidating Categorical Inputs
9.5b Consolidating Categorical Inputs Details 00:00:00
9.5c The Bonferonni Adjustment and Other Property Changes
9.5c The Bonferonni Adjustment and Other Property Changes Details 00:00:00
9.6a Surrogate Models
9.6a Surrogate Models Details 00:00:00
9.6b Understanding Decision Segments Using Surrogate Models
9.6b Understanding Decision Segments Using Surrogate Models Details 00:00:00
9.6c Additional Details
9.6c Additional Details Details 00:00:00
Case Studies Using SAS Enterprise Miner Lesson Overview
Case Studies Using SAS Enterprise Miner Lesson Overview Details 00:00:00
2.1a Banking Segmentation Scenario
2.1a Banking Segmentation Scenario Details 00:00:00
2.1b Practice Define a Data Source and View the Preliminary Statistics
2.1b Practice Define a Data Source and View the Preliminary Statistics Details 00:00:00
2.1b Practice Define a Data Source and View the Preliminary Statistics Solution
2.1b Practice Define a Data Source and View the Preliminary Statistics Solution Details 00:00:00
2.1c Practice Transform Variables in the PROFILE Data Source
2.1c Practice Transform Variables in the PROFILE Data Source Details 00:00:00
2.1c Practice Transform Variables in the PROFILE Data Source Solution
2.1c Practice Transform Variables in the PROFILE Data Source Solution Details 00:00:00
2.1d Practice Generate a Three-Dimensional Scatter Plot of the Inputs
2.1d Practice Generate a Three-Dimensional Scatter Plot of the Inputs Details 00:00:00
2.1d Practice Generate a Three-Dimensional Scatter Plot of the Inputs Solution
2.1d Practice Generate a Three-Dimensional Scatter Plot of the Inputs Solution Details 00:00:00
2.1e Practice Run a Cluster Analysis to Create Segments
2.1e Practice Run a Cluster Analysis to Create Segments Details 00:00:00
2.1e Practice Run a Cluster Analysis to Create Segments Solution
2.1e Practice Run a Cluster Analysis to Create Segments Solution Details 00:00:00
2.1f Practice Interpret the Segments
2.1f Practice Interpret the Segments Details 00:00:00
2.1f Practice Interpret the Segments Solution
2.1f Practice Interpret the Segments Solution Details 00:00:00
2.1g Practice Deploy the Results of the Segmentation Analysis
2.1g Practice Deploy the Results of the Segmentation Analysis Details 00:00:00
2.1g Practice Deploy the Results of the Segmentation Analysis Solution
2.1g Practice Deploy the Results of the Segmentation Analysis Solution Details 00:00:00
3.1a Web Site Usage Associations Scenario
3.1a Web Site Usage Associations Scenario Details 00:00:00
3.1b Practice Define a Data Source and View Preliminary Statistics
3.1b Practice Define a Data Source and View Preliminary Statistics Details 00:00:00
3.1b Practice Define a Data Source and View Preliminary Statistics Solution
3.1b Practice Define a Data Source and View Preliminary Statistics Solution Details 00:00:00
3.1c Practice Run an Association Analysis
3.1c Practice Run an Association Analysis Details 00:00:00
3.1c Practice Run an Association Analysis Solution
3.1c Practice Run an Association Analysis Solution Details 00:00:00
4.1a Credit Risk Scenario
4.1a Credit Risk Scenario Details 00:00:00
4.1b Practice Define a Data Source and View Preliminary Statistics
4.1b Practice Define a Data Source and View Preliminary Statistics Details 00:00:00
4.1b Practice Define a Data Source and View Preliminary Statistics Solution
4.1b Practice Define a Data Source and View Preliminary Statistics Solution Details 00:00:00
4.1c Practice Prepare the Data for Analysis
4.1c Practice Prepare the Data for Analysis Details 00:00:00
4.1c Practice Prepare the Data for Analysis Solution
4.1c Practice Prepare the Data for Analysis Solution Details 00:00:00
4.1d Practice Perform a Simple Stepwise Regression
4.1d Practice Perform a Simple Stepwise Regression Details 00:00:00
4.1d Practice Perform a Simple Stepwise Regression Solution
4.1d Practice Perform a Simple Stepwise Regression Solution Details 00:00:00
4.1e Practice Run a Neural Network Analysis
4.1e Practice Run a Neural Network Analysis Details 00:00:00
4.1e Practice Run a Neural Network Analysis Solution
4.1e Practice Run a Neural Network Analysis Solution Details 00:00:00
4.1f Practice Transform Variables and Run a Transformed Stepwise Regression
4.1f Practice Transform Variables and Run a Transformed Stepwise Regression Details 00:00:00
4.1f Practice Transform Variables and Run a Transformed Stepwise Regression Solution
4.1f Practice Transform Variables and Run a Transformed Stepwise Regression Solution Details 00:00:00
4.1g Practice Perform a Bucket Transformation and Run a Stepwise Regression
4.1g Practice Perform a Bucket Transformation and Run a Stepwise Regression Details 00:00:00
4.1g Practice Perform a Bucket Transformation and Run a Stepwise Regression Solution
4.1g Practice Perform a Bucket Transformation and Run a Stepwise Regression Solution Details 00:00:00
4.1h Practice Perform a Binned Transformation and Run a Stepwise Regression
4.1h Practice Perform a Binned Transformation and Run a Stepwise Regression Details 00:00:00
4.1h Practice Perform a Binned Transformation and Run a Stepwise Regression Solution
4.1h Practice Perform a Binned Transformation and Run a Stepwise Regression Solution Details 00:00:00
4.1i Practice Perform an Optimal Discrete Transformation and Run a Stepwise Regression
4.1i Practice Perform an Optimal Discrete Transformation and Run a Stepwise Regression Details 00:00:00
4.1i Practice Perform an Optimal Discrete Transformation and Run a Stepwise Regression Solution
4.1i Practice Perform an Optimal Discrete Transformation and Run a Stepwise Regression Solution Details 00:00:00
4.1j Practice Assess the Prediction Models
4.1j Practice Assess the Prediction Models Details 00:00:00
4.1j Practice Assess the Prediction Models Solution
4.1j Practice Assess the Prediction Models Solution Details 00:00:00
5.1a Enrollment Management Scenario
5.1a Enrollment Management Scenario Details 00:00:00
5.1b Practice Define a Data Source and View Preliminary Statistics
5.1b Practice Define a Data Source and View Preliminary Statistics Details 00:00:00
5.1b Practice Define a Data Source and View Preliminary Statistics Solution
5.1b Practice Define a Data Source and View Preliminary Statistics Solution Details 00:00:00
5.1c Practice Create a Training Sample
5.1c Practice Create a Training Sample Details 00:00:00
5.1c Practice Create a Training Sample Solution
5.1c Practice Create a Training Sample Solution Details 00:00:00
5.1d Practice Configure Decision Processing
5.1d Practice Configure Decision Processing Details 00:00:00
5.1d Practice Configure Decision Processing Solution
5.1d Practice Configure Decision Processing Solution Details 00:00:00
5.1e Practice Prepare the Data and Perform a Stepwise Regression Analysis
5.1e Practice Prepare the Data and Perform a Stepwise Regression Analysis Details 00:00:00
5.1e Practice Prepare the Data and Perform a Stepwise Regression Analysis Solution
5.1e Practice Prepare the Data and Perform a Stepwise Regression Analysis Solution Details 00:00:00
5.1f Practice Run a Stepwise Regression with the In-State Input Variable
5.1f Practice Run a Stepwise Regression with the In-State Input Variable Details 00:00:00
5.1f Practice Run a Stepwise Regression with the In-State Input Variable Solution
5.1f Practice Run a Stepwise Regression with the In-State Input Variable Solution Details 00:00:00
5.1g Practice Add Neural Network and Decision Tree nodes to the Diagram
5.1g Practice Add Neural Network and Decision Tree nodes to the Diagram Details 00:00:00
5.1g Practice Add Neural Network and Decision Tree nodes to the Diagram Solution
5.1g Practice Add Neural Network and Decision Tree nodes to the Diagram Solution Details 00:00:00
5.1h Practice Assess the Models
5.1h Practice Assess the Models Details 00:00:00
5.1h Practice Assess the Models Solution
5.1h Practice Assess the Models Solution Details 00:00:00
5.1i Predictive Modeling on the In-State Cases
5.1i Predictive Modeling on the In-State Cases Details 00:00:00
5.1j Practice Create a New Diagram for the Analysis and Add Nodes
5.1j Practice Create a New Diagram for the Analysis and Add Nodes Details 00:00:00
5.1j Practice Create a New Diagram for the Analysis and Add Nodes Solution
5.1j Practice Create a New Diagram for the Analysis and Add Nodes Solution Details 00:00:00
5.1k Practice Filter the Data to Remove the Out-of-State Cases
5.1k Practice Filter the Data to Remove the Out-of-State Cases Details 00:00:00
5.1k Practice Filter the Data to Remove the Out-of-State Cases
5.1k Practice Filter the Data to Remove the Out-of-State Cases Solution Details 00:00:00
5.1l Practice Partition the Data and Perform Regression Analysis
5.1l Practice Partition the Data and Perform Regression Analysis Details 00:00:00
5.1l Practice Partition the Data and Perform Regression Analysis Solution
5.1l Practice Partition the Data and Perform Regression Analysis Solution Details 00:00:00
5.1m Practice Add Neural Network and Decision Tree Nodes to the Diagram
5.1m Practice Add Neural Network and Decision Tree Nodes to the Diagram Details 00:00:00
5.1m Practice Add Neural Network and Decision Tree Nodes to the Diagram Solution
5.1m Practice Add Neural Network and Decision Tree Nodes to the Diagram Solution Details 00:00:00
5.1n Practice Assess the Prediction Models for the In-State Cases
5.1n Practice Assess the Prediction Models for the In-State Cases Details 00:00:00
5.1n Practice Assess the Prediction Models for the In-State Cases Solution
5.1n Practice Assess the Prediction Models for the In-State Cases Solution Details 00:00:00
Chapter 5 Lesson Summary
Chapter 5 Lesson Summary Details 00:00:00
Chapter 5 Quiz
Chapter 5 Quiz Details 00:00:00
Chapter 5 Quiz Feedback
Chapter 5 Quiz Feedback Details 00:00:00
6.4a Using the Neural Network Tool
6.4a Using the Neural Network Tool Details 00:00:00
7.5d Determining the Average Value of a Target Variable
7.5d Determining the Average Value of a Target Variable Details 00:00:00
7.5f Specifying Decision Processing Information and Running the Model Comparison Node
7.5f Specifying Decision Processing Information and Running the Model Comparison Node Details 00:00:00
7.5m Refitting the Models Using Profit Optimization
7.5m Refitting the Models Using Profit Optimization Details 00:00:00
7.6b Practice Run the Model Comparison Node and View the Results
7.6b Practice Run the Model Comparison Node and View the Results Details 00:00:00
7.6b Practice Run the Model Comparison Node and View the Results Solution
7.6b Practice Run the Model Comparison Node and View the Results Solution Details 00:00:00

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