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Regression Modeling Fundamentals

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Regression Modeling Fundamentals

Name: Regression Modeling Fundamentals
Rating: 4.660377358490566 (53 reviews)

This course is part of SAS Statistical Business Analyst Professional Certificate

Instructor: Jordan Bakerman

8,428 already enrolled

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

53 reviews

Intermediate level

Some related experience required

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

53 reviews

Intermediate level

Some related experience required

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

32 assignments

Taught in English

91%

of learners achieved a positive career outcome

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Build your Data Analysis expertise

This course is part of the SAS Statistical Business Analyst Professional Certificate

When you enroll in this course, you'll also be enrolled in this Professional Certificate.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate from SAS

There are 4 modules in this course

This introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t tests, ANOVA, and linear regression, and includes a brief introduction to logistic regression.

Module details

This module focuses on building regression models and selecting the best set of predictors using practical, data-driven methods in SAS. You’ll start by setting up the course environment, then move into key model selection approaches—including all-possible regressions, stepwise selection using significance levels, and selection using information criteria. Along the way, you’ll learn how to interpret p-values and parameter estimates, evaluate models with metrics like adjusted R-square and Mallows’ Cp, and apply these through demos and practice assignments.

What's included

13 videos7 readings3 assignments

13 videosTotal 41 minutes

Welcome and Meet the Instructor2 minutes
Demo: Exploring Ames Housing Data11 minutes
Overview1 minute
Scenario1 minute
Approaches to Selecting Models2 minutes
The All-Possible Regressions Approach to Model Building1 minute
The Stepwise Selection Approach to Model Building3 minutes
Interpreting p-Values and Parameter Estimates2 minutes
Demo: Performing Stepwise Regression Using PROC GLMSELECT8 minutes
Scenario1 minute
Information Criteria2 minutes
Adjusted R-Square and Mallows' Cp1 minute
Demo: Performing Model Selection Using PROC GLMSELECT6 minutes

7 readingsTotal 51 minutes

Learner Prerequisites1 minute
Access SAS Software for this Course10 minutes
Completing Demos and Practices10 minutes
Frequently Asked Questions10 minutes
Activity - Optional Stepwise Selection Method Code10 minutes
Information Criteria Penalty Components10 minutes
All-Possible Selection0 minutes

3 assignmentsTotal 80 minutes

Knowledge Check - Using PROC GLMSELECT for Stepwise Selection30 minutes
Knowledge Check -Using PROC GLMSELECT to Perform Other Model Selection Techniques30 minutes
Model Building and Effect Selection20 minutes

In this module you learn to verify the assumptions of the model and diagnose problems that you encounter in linear regression. You learn to examine residuals, identify outliers that are numerically distant from the bulk of the data, and identify influential observations that unduly affect the regression model. Finally, you learn to diagnose collinearity to avoid inflated standard errors and parameter instability in the model.

What's included

18 videos7 assignments

18 videosTotal 46 minutes

Overview1 minute
Scenario1 minute
Assumptions for Regression2 minutes
Verifying Assumptions Using Residual Plots3 minutes
Demo: Examining Residual Plots Using PROC REG5 minutes
Scenario1 minute
Identifying Influential Observations1 minute
Checking for Outliers with STUDENT Residuals1 minute
Checking for Influential Observations3 minutes
Detecting Influential Observations with DFBETAS1 minute
Demo: Looking for Influential Observations Using PROC GLMSELECT and PROC REG5 minutes
Demo: Examining the Influential Observations Using PROC PRINT7 minutes
Handling Influential Observations2 minutes
Scenario1 minute
Exploring Collinearity2 minutes
Visualizing Collinearity2 minutes
Demo: Calculating Collinearity Diagnostics Using PROC REG5 minutes
Using an Effective Modeling Cycle2 minutes

7 assignmentsTotal 105 minutes

Practice: Using PROC REG to Examine Residuals20 minutes
Question 5.015 minutes
Practice: Using PROC REG to Generate Potential Outliers20 minutes
Question 5.025 minutes
Question 5.035 minutes
Practice: Using PROC REG to Assess Collinearity20 minutes
Model Post-Fitting for Inference30 minutes

In this module you learn how to transition from inferential statistics to predictive modeling. Instead of using p-values, you learn about assessing models using honest assessment. After you choose the best performing model, you learn about ways to deploy the model to predict new data.

What's included

11 videos1 reading4 assignments

11 videosTotal 27 minutes

Overview2 minutes
Scenario0 minutes
Predictive Modeling Terminology2 minutes
Model Complexity1 minute
Building a Predictive Model3 minutes
Model Assessment and Selection2 minutes
Demo: Building a Predictive Model Using PROC GLMSELECT11 minutes
Scenario0 minutes
Preparing for Scoring1 minute
Methods of Scoring1 minute
Demo: Scoring Data Using PROC PLM4 minutes

1 readingTotal 10 minutes

Partitioning a Data Set Using PROC GLMSELECT10 minutes

4 assignmentsTotal 75 minutes

Question 6.015 minutes
Practice: Building a Predictive Model Using PROC GLMSELECT20 minutes
Practice: Scoring Using the SCORE Statement in PROC GLMSELECT20 minutes
Model Building for Scoring and Prediction30 minutes

In this module you look for associations between predictors and a binary response using hypothesis tests. Then you build a logistic regression model and learn about how to characterize the relationship between the response and predictors. Finally, you learn how to use logistic regression to build a model, or classifier, to predict unknown cases.

What's included

25 videos18 assignments

25 videosTotal 73 minutes

Overview2 minutes
Scenario1 minute
Associations between Categorical Variables2 minutes
Demo: Examining the Distribution of Categorical Variables Using PROC FREQ and PROC UNIVARIATE6 minutes
Scenario1 minute
The Pearson Chi-Square Test3 minutes
Odds Ratios4 minutes
Demo: Performing a Pearson Chi-Square Test of Association Using PROC FREQ5 minutes
Scenario0 minutes
The Mantel-Haenszel Chi-Square Test1 minute
The Spearman Correlation Statistic1 minute
Demo: Detecting Ordinal Associations Using PROC FREQ2 minutes
Scenario1 minute
Modeling a Binary Response4 minutes
Demo: Fitting a Binary Logistic Regression Model Using PROC LOGISTIC7 minutes
Interpreting the Odds Ratio3 minutes
Comparing Pairs to Assess the Fit of a Logistic Regression Model5 minutes
Scenario1 minute
Specifying a Parameterization Method5 minutes
Demo: Fitting a Multiple Logistic Regression Model with Categorical Predictors Using PROC LOGISTIC7 minutes
Scenario1 minute
Interactions between Variables2 minutes
Demo: Fitting a Multiple Logistic Regression Model with Interactions Using PROC LOGISTIC4 minutes
Demo: Fitting a Multiple Logistic Regression Model with All Odds Ratios Using PROC LOGISTIC3 minutes
Demo: Generating Predictions Using PROC PLM2 minutes

18 assignmentsTotal 190 minutes

Question 7.015 minutes
Question 7.025 minutes
Practice: Using PROC FREQ to Examine Distributions20 minutes
Question 7.035 minutes
Question 7.045 minutes
Question 7.055 minutes
Question 7.065 minutes
Practice: Using PROC FREQ to Perform Tests and Measures of Association20 minutes
Question 7.075 minutes
Question 7.085 minutes
Practice: Using PROC LOGISTIC to Perform a Binary Logistic Regression Analysis20 minutes
Question 7.095 minutes
Question 7.105 minutes
Practice: Using PROC LOGISTIC to Perform a Multiple Logistic Regression Analysis with Categorical Variables20 minutes
Question 7.115 minutes
Question 7.125 minutes
Practice: Using PROC LOGISTIC to Perform Backward Elimination and PROC PLM to Generate Predictions20 minutes
Categorical Data Analysis30 minutes