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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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