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Regression Modeling Fundamentals

Name: Regression Modeling Fundamentals
Rating: 4.7 (52 reviews)

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

Course Cost

Free course

Intermediate

Skill Level

9 Hours

Self-paced lessons

This intermediate course teaches statistical analysis using SAS/STAT software, focusing on key regression modeling techniques and their practical applications. Students learn a comprehensive approach to building, validating, and applying statistical models for both inference and prediction. The curriculum begins with model building strategies, covering stepwise selection methods and information criteria for identifying optimal predictors. Students then explore model post-fitting procedures to verify assumptions, diagnose problems, and handle influential observations and collinearity in linear regression. The course transitions from inferential statistics to predictive modeling, teaching honest assessment methods and deployment techniques for scoring new data. Finally, students learn about categorical data analysis, including association tests and logistic regression for binary outcomes. Throughout the course, practical examples using real housing data provide hands-on experience with SAS tools such as PROC REG, PROC GLMSELECT, PROC LOGISTIC, and PROC PLM.

4.7

7,965 Enrolled

Jordan Bakerman

English

4.7

7,965 Enrolled

English

Jordan Bakerman

What you'll learn

Apply model selection techniques to identify optimal predictors

Verify regression assumptions and diagnose problems using residual plots

Identify outliers and influential observations in statistical models

Diagnose and address collinearity issues in regression analysis

Build predictive models and assess their performance

Deploy models to score new data using appropriate procedures

Analyze associations between categorical variables

Build and interpret logistic regression models for binary outcomes

Skills you'll gain

Data Analysis

Probability & Statistics

Regression

Statistical Programming

General Statistics

Linear Regression

Logistic Regression

SAS

Model Selection

Predictive Modeling

This course includes:

3.05 Hours PreRecorded video

33 assignments

Access on Mobile, Desktop, Tablet

FullTime access

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Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.

There are 5 modules in this course

This course provides a comprehensive introduction to regression modeling using SAS software. Students begin by exploring model building strategies and effect selection techniques, learning to use information criteria and stepwise methods to identify optimal predictors in regression models. The second module focuses on model validation and diagnostics, teaching students to verify assumptions, examine residuals, identify outliers and influential observations, and diagnose collinearity issues. The third module transitions from inferential statistics to predictive modeling, covering model assessment, selection, and deployment for scoring new data. The final module explores categorical data analysis, including association tests between variables and logistic regression modeling for binary outcomes. Throughout the course, students gain hands-on experience with SAS procedures like PROC REG, PROC GLMSELECT, PROC LOGISTIC, and PROC PLM, using real-world housing data to apply statistical concepts in practical scenarios.

Course Overview (Review from Introduction to Statistics: Hypothesis Testing)

Module 1 · 1 Hours to complete

Model Building and Effect Selection

Module 2 · 1 Hours to complete

In this module you explore several tools for model selection. These tools help limit the number of candidate models so that you can choose an appropriate model that's based on your expertise and research priorities.

Skills you'll gain

Model selection techniques

Stepwise regression

All-possible regressions

Information criteria

Parameter estimation

P-value interpretation

PROC GLMSELECT

Adjusted R-Square

Mallows' Cp

This course includes:

11 Videos

4 Assignments

3 Reading Materials

Model Post-Fitting for Inference

Module 3 · 2 Hours to complete

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.

Skills you'll gain

Regression assumptions

Residual analysis

Outlier detection

Influential observations

STUDENT residuals

DFBETAS

Collinearity diagnostics

PROC REG

Modeling cycle

This course includes:

18 Videos

7 Assignments

Model Building for Scoring and Prediction

Module 4 · 1 Hours to complete

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.

Skills you'll gain

Predictive modeling terminology

Model complexity

Model assessment

Model selection

Scoring methods

PROC GLMSELECT

PROC PLM

Data partitioning

This course includes:

11 Videos

4 Assignments

1 Reading Materials

Categorical Data Analysis

Module 5 · 4 Hours to complete

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.

Skills you'll gain

Categorical variables

Hypothesis testing

Pearson Chi-Square test

Odds ratios

Mantel-Haenszel Chi-Square test

Spearman correlation

Binary logistic regression

PROC LOGISTIC

PROC FREQ

Parameterization methods

Variable interactions

Predictions with PROC PLM

This course includes:

25 Videos

18 Assignments

Fee Structure

Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: SAS Statistical Business Analyst Professional Certificate

Reviews

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Faculties

These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.

4.7

Reviews

55,698

Students

Courses

Jordan Bakerman

Dr. Jordan Bakerman serves as an Analytical Training Consultant at SAS, where he specializes in bridging open-source and SAS analytics platforms. His Ph.D. research at North Carolina State University focused on leveraging social media data to forecast real-world events, including civil unrest and influenza rates. As the creator of the widely-used "SAS Programming for R Users" course, he developed an innovative cookbook-style approach to help R programmers transition efficiently to SAS. His teaching portfolio includes courses on statistical analysis, regression modeling, and API integration between SAS Viya and open-source platforms. Through his Coursera courses "Introduction to Statistical Analysis: Hypothesis Testing," "Regression Modeling Fundamentals," and "Using SAS Viya REST APIs with Python and R," he shares his expertise in statistical programming and analysis. His work focuses on making advanced statistical concepts accessible while helping professionals integrate open-source tools with SAS technologies.

Intermediate

Skill Level

9 Hours

Self-paced lessons

Course Cost

Free course

Completion

CERTIFICATE

Frequently asked Questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.

Regression Modeling Fundamentals

What you'll learn

Skills you'll gain

This course includes:

Get a Completion Certificate

Get a Completion Certificate

Top companies offer this course to their employees

There are 5 modules in this course

Course Overview (Review from Introduction to Statistics: Hypothesis Testing)

Skills you'll gain

This course includes:

Model Building and Effect Selection

Skills you'll gain

This course includes:

Model Post-Fitting for Inference

Skills you'll gain

This course includes:

Model Building for Scoring and Prediction

Skills you'll gain

This course includes:

Categorical Data Analysis

Skills you'll gain

This course includes:

Fee Structure

Reviews

Faculties

Frequently asked Questions

When will I have access to the lectures and assignments?

What will I get if I subscribe to this Certificate?

What is the refund policy?