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This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. This course also discusses selecting variables and interactions, recording categorical variables based on the smooth weight of evidence, assessing models, treating missing values and using efficiency techniques for massive data sets.

This course can help prepare you for the following certification exam(s): SAS Statistical Business Analysis Using SAS 9: Regression and Modeling.

Learn how to

• use logistic regression to model an individual’s behavior as a function of known inputs
• create effect plots and odds ratio plots using ODS Statistical Graphics
• handle missing data values
• tackle multicollinearity in your predictors
• assess model performance and compare models.Understanding Predictive
Modeling

### Course Curriculum

 Understanding Predictive Modeling Lesson Overview Understanding Predictive Modeling Lesson Overview Details 00:00:00 Predictive Modeling Fundamentals Introduction Predictive Modeling Fundamentals Introduction Details 00:00:00 Predictive Modeling Fudamentals Goals of Predictive Modeling Predictive Modeling Fudamentals Goals of Predictive Modeling Details 00:00:00 Predictive Modeling Fudamentals Terms of Elements in Predictive Modeling Predictive Modeling Fudamentals Terms of Elements in Predictive Modeling Details 00:00:00 Predictive Modeling Fudamentals Basic Steps of Predictive Modeling Predictive Modeling Fudamentals Basic Steps of Predictive Modeling Details 00:00:00 Predictive Modeling Fudamentals Applications of Predictive Modeling Predictive Modeling Fudamentals Applications of Predictive Modeling Details 00:00:00 Predictive Modeling Fudamentals Demonstartion Scenario Target Marketing for a Bank Predictive Modeling Fudamentals Demonstartion Scenario Target Marketing for a Bank Details 00:00:00 Predictive Modeling Fudamentals Examining the Code for Generating Descriptive Statistics and Frequency Tables Predictive Modeling Fudamentals Examining the Code for Generating Descriptive Statistics and Frequency Tables Details 00:00:00 Predictive Modeling challenges introduction Predictive Modeling challenges introduction Details 00:00:00 Predictive Modeling challenges Data challenges Predictive Modeling challenges Data challenges Details 00:00:00 Predictive Modeling challenges Analytical challenges Predictive Modeling challenges Analytical challenges Details 00:00:00 Predictive Modeling challenges Analytical challenges Separate Sampling Predictive Modeling challenges Analytical challenges Separate Sampling Details 00:00:00 Predictive Modeling challenges Avoiding the Optimism Bias_honest Assessment Predictive Modeling challenges Avoiding the Optimism Bias_honest Assessment Details 00:00:00 Predictive Modeling challenges Spitting the data Predictive Modeling challenges Spitting the data Details 00:00:00 Predictive Modeling challenges Spitting the data for Model Training and Assessment Predictive Modeling challenges Spitting the data for Model Training and Assessment Details 00:00:00 Fitting the Model Lesson OverView Details 00:00:00 Understanding the Logistic Regression Model Introduction Understanding the Logistic Regression Model Introduction Details 00:00:00 Understanding the Logistic Regression Model Understanding the Logistic Regression Model Details 00:00:00 Understanding the Logistic Regression ModelConstraining the Posterior Probablity Using the logit transformation Understanding the Logistic Regression ModelConstraining the Posterior Probablity Using the logit transformation Details 00:00:00 Understanding the Logistic Regression Model Understanding the Fitted Surface Understanding the Logistic Regression Model Understanding the Fitted Surface Details 00:00:00 Understanding the Logistic Regression Model nterpreting the model by calculating the Odds Ratio Understanding the Logistic Regression Model nterpreting the model by calculating the Odds Ratio Details 00:00:00 Understanding the Logistic Regression Model Understanding Logistic Discrimnation Understanding the Logistic Regression Model Understanding Logistic Discrimnation Details 00:00:00 Understanding the Logistic Regression Model Understanding Logistic Discrimnation Understanding the Logistic Regression Model Understanding Logistic Discrimnation Details 00:00:00 Understanding the Logistic Regression Model Estimating unknown Parameters using maximum likelihood Estmation Understanding the Logistic Regression Model Estimating unknown Parameters using maximum likelihood Estmation Details 00:00:00 Understanding the Logistic Regression Model Interpretting Concordant,Discordant nad Tied pairs Understanding the Logistic Regression Model Interpretting Concordant,Discordant nad Tied pairs Details 00:00:00 Understanding the Logistic Regression Model Using Proc logistic to fit Logistic regression Models Understanding the Logistic Regression Model Using Proc logistic to fit Logistic regression Models Details 00:00:00 Understanding the Logistic Regression Model Fitting a Basic logistic regression model,Part 2 Understanding the Logistic Regression Model Fitting a Basic logistic regression model,Part 2 Details 00:00:00 Understanding the Logistic Regression Model Scoring New Cases Understanding the Logistic Regression Model Scoring New Cases Details 00:00:00 Understanding the Logistic Regression Model Scoring New Cases Understanding the Logistic Regression Model Scoring New Cases Details 00:00:00 Correcting For Sampling Introduction Correcting For Sampling Introduction Details 00:00:00 Correcting For Sampling Understanding the effect of Oversampling Correcting For Sampling Understanding the effect of Oversampling Details 00:00:00 Correcting For Sampling Understanding the Offset Correcting For Sampling Understanding the Offset Details 00:00:00 Correcting For Sampling Correcting the Ovesampling Correcting For Sampling Correcting the Ovesampling Details 00:00:00 Preparing the input variables Preparing the input variables Details 00:00:00 Handling Missing Values Handling Missing Values Details 00:00:00 Handling Missing Values Reason of Missing Data Handling Missing Values Reason of Missing Data Details 00:00:00 Handling Missing Values Complete Case Analysis Handling Missing Values Complete Case Analysis Details 00:00:00 Handling Missing Values Methods for Inputing Missing Values Handling Missing Values Methods for Inputing Missing Values Details 00:00:00 Handling Missing Values Missing value impputaion with missing value indicator variables Handling Missing Values Missing value impputaion with missing value indicator variables Details 00:00:00 Handling Missing Values Imputing missing values Handling Missing Values Imputing missing values Details 00:00:00 Handling Missing Values Cluster Imputation Handling Missing Values Cluster Imputation Details 00:00:00 Working with Categorical Inputs Working with Categorical Inputs Details 00:00:00 Working with Categorical Inputs Problems caused by Catagorical Inputs Working with Categorical Inputs Problems caused by Catagorical Inputs Details 00:00:00 Working with Categorical Inputs Solutions to problems caused by Catagorical Inputs Working with Categorical Inputs Solutions to problems caused by Catagorical Inputs Details 00:00:00 Working with Categorical Inputs Linking to other Data Sets Working with Categorical Inputs Linking to other Data Sets Details 00:00:00 Working with Categorical Inputs Collapsing catagories by threshholding Working with Categorical Inputs Collapsing catagories by threshholding Details 00:00:00 Working with Categorical Inputs Collapsing catagories by usin g Greenacre’s Method Working with Categorical Inputs Collapsing catagories by usin g Greenacre’s Method Details 00:00:00 Working with Categorical Inputs Collapsing the levels of a Nominal input Part 1 Working with Categorical Inputs Collapsing the levels of a Nominal input Part 1 Details 00:00:00 Working with Categorical Inputs Collapsing the levels of a Nominal input Part 2 Working with Categorical Inputs Collapsing the levels of a Nominal input Part 2 Details 00:00:00 Working with Categorical Inputs Replacing Categorical Levels by using Smoothed Weight-of-Evidence Coding Working with Categorical Inputs Collapsing the levels of a Nominal input Part 2 Details 00:00:00 Working with Categorical Inputs Computing the Smoothed Weight-of-Evidence Working with Categorical Inputs Computing the Smoothed Weight-of-Evidence Details 00:00:00 Reducing Renundancy by Clustering variables Reducing Renundancy by Clustering variables Details 00:00:00 Reducing Renundancy by Clustering variables Problem of RedundancyNot Yet Rated Reducing Renundancy by Clustering variables Problem of RedundancyNot Yet Rated Details 00:00:00 Reducing Renundancy by Clustering variables Variable Clustering Method Reducing Renundancy by Clustering variables Variable Clustering Method Details 00:00:00 Reducing Renundancy by Clustering variables Understanding principal Components Reducing Renundancy by Clustering variables Understanding principal Components Details 00:00:00

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• 3 months
• 25 SEATS
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