About this Course

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Intermediate Level
Approx. 17 hours to complete
English

Skills you will gain

OversamplingLogistic RegressionPredictive Modellingregression
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 17 hours to complete
English

Offered by

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SAS

Syllabus - What you will learn from this course

Week
1

Week 1

1 hour to complete

Course Overview and Logistics

1 hour to complete
1 video (Total 1 min), 6 readings
1 video
6 readings
What You Learn in This Course5m
Learner Prerequisites1m
Using Forums and Getting Help10m
Access SAS Software for this Course10m
Set Up Data for This Course (REQUIRED) 30m
About the Demos and Practices in this Course10m
2 hours to complete

Understanding Predictive Modeling

2 hours to complete
15 videos (Total 34 min), 1 reading, 6 quizzes
15 videos
Introduction19s
Goals of Predictive Modeling1m
Terms for Elements in Predictive Modeling49s
Basic Steps of Predictive Modeling2m
Applications of Predictive Modeling1m
Demonstration Scenario: Target Marketing for a Bank1m
Demo: Examining the Code for Generating Descriptive Statistics and Frequency Tables2m
Introduction21s
Data Challenges6m
Analytical Challenges2m
Separate Sampling1m
Avoiding the Optimism Bias: Honest Assessment2m
Splitting the Data for Model Training and Assessment3m
Demo: Splitting the Data5m
1 reading
Summary10m
6 practice exercises
Practice: Exploring the Bank Data for the Target Marketing Project20m
Practice: Exploring the Veterans' Organization Data Used in the Practices20m
Question 1.015m
Question 1.025m
Question 1.035m
Practice: Splitting the Data20m
Week
2

Week 2

2 hours to complete

Fitting the Model

2 hours to complete
18 videos (Total 54 min), 1 reading, 4 quizzes
18 videos
Introduction22s
Understanding the Logistic Regression Model2m
Constraining the Posterior Probability Using the Logit Transformation1m
Understanding the Fitted Surface1m
Interpreting the Model by Calculating the Odds Ratio3m
Understanding Logistic Discrimination1m
Estimating Unknown Parameters Using Maximum Likelihood Estimation2m
Interpreting Concordant, Discordant, and Tied Pairs1m
Using PROC LOGISTIC to Fit Logistic Regression Models24s
Demo: Fitting a Basic Logistic Regression Model, Part 18m
Demo: Fitting a Basic Logistic Regression Model, Part 212m
Scoring New Cases26s
Demo: Scoring New Cases7m
Introduction16s
Understanding the Effect of Oversampling53s
Understanding the Offset1m
Demo: Correcting for Oversampling6m
1 reading
Summary10m
4 practice exercises
Question 2.015m
Question 2.025m
Practice: Fitting a Logistic Regression Model20m
Fitting the Model Review30m
Week
3

Week 3

3 hours to complete

Preparing the Input Variables, Part 1

3 hours to complete
26 videos (Total 76 min)
26 videos
Introduction22s
Reasons for Missing Data2m
Complete Case Analysis1m
Methods for Imputing Missing Values2m
Missing Value Imputation with Missing Value Indicator Variables3m
Demo: Imputing Missing Values4m
Cluster Imputation1m
Introduction25s
Problems Caused by Categorical Inputs4m
Solutions to Problems Caused by Categorical Inputs39s
Linking to Other Data Sets56s
Collapsing Categories by Thresholding53s
Collapsing Categories by Using Greenacre's Method3m
Demo: Collapsing the Levels of a Nominal Input, Part 16m
Demo: Collapsing the Levels of a Nominal Input, Part 210m
Replacing Categorical Levels by Using Smoothed Weight-of-Evidence Coding2m
Demo: Computing the Smoothed Weight of Evidence4m
Introduction20s
Problem of Redundancy2m
Variable Clustering Method1m
Understanding Principal Components5m
Divisive Clustering3m
PROC VARCLUS Syntax1m
Selecting a Representative Variable from Each Cluster1m
Demo: Reducing Redundancy by Clustering Variables8m
9 practice exercises
Question 3.015m
Practice: Imputing Missing Values20m
Question 3.025m
Question 3.035m
Question 3.045m
Practice: Collapsing the Levels of a Nominal Input20m
Practice: Computing the Smoothed Weight of Evidence20m
Question 3.055m
Practice: Reducing Redundancy by Clustering Variables20m
Week
4

Week 4

4 hours to complete

Preparing the Input Variables, Part 2

4 hours to complete
23 videos (Total 92 min), 1 reading, 12 quizzes
23 videos
Detecting Nonlinear Relationships4m
Demo: Performing Variable Screening, Part 15m
Demo: Performing Variable Screening, Part 24m
Univariate Binning and Smoothing2m
Demo: Creating Empirical Logit Plots10m
Remedies for Nonlinear Relationships2m
Demo: Accommodating a Nonlinear Relationship, Part 16m
Demo: Accommodating a Nonlinear Relationship, Part 27m
Introduction26s
Specifying a Subset Selection Method in PROC LOGISTIC1m
Best-Subsets Selection54s
Stepwise Selection2m
Backward Elimination1m
Scalability of the Subset Selection Methods in PROC LOGISTIC2m
Detecting Interactions2m
BIC-based Significance Level2m
Demo: Detecting Interactions7m
Demo: Using Backward Elimination to Subset the Variables4m
Demo: Displaying Odds Ratios for Variables Involved in Interactions3m
Demo: Creating an Interaction Plot3m
Demo: Using the Best-Subsets Selection Method3m
Demo: Using Fit Statistics to Select a Model9m
1 reading
Summary of Preparing the Input Variables, Parts 1 and 210m
12 practice exercises
Question 3.065m
Practice: Performing Variable Screening20m
Practice: Creating Empirical Logit Plots20m
Question 3.075m
Question 3.085m
Question 3.095m
Practice: Using Forward Selection to Detect Interactions20m
Question 3.105m
Practice: Using Backward Elimination to Subset the Variables20m
Question 3.115m
Practice: Using Fit Statistics to Select a Model20m
Preparing the Input Variables Review30m

About the SAS Statistical Business Analyst Professional Certificate

SAS Statistical Business Analyst

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