We may earn an affiliate commission when you visit our partners.

Regression Modeling Fundamentals

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.

Enroll now

Or subscribe to Coursera Plus

And get unlimited access to Coursera

Two deals to help you save

Save money when you learn. Here are two deals and offers that may be relevant to this course.

All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.

Valid until July 14

Coursera Plus Annual

Work toward what's next for your career with access to 10,000+ programs. Discover emerging skills and save.

Valid until August 30

Google AI App Builder

Learn how to use Gemini API and API Studio with a three-course series from Google DeepMind

What's inside

Syllabus

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

In this module you learn about the course and the data you analyze in this course. Then you set up the data you need to do the practices in the course.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Focuses on industry-standard statistical analyses, including t tests, ANOVA, and linear regression

Provides a brief introduction to logistic regression

Emphasizes model selection, post-fitting, and predictive modeling

Offers a comprehensive overview of statistical analyses using SAS software

Instructors, Jordan Bakerman, are recognized for their expertise in statistical analyses

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.

Save

Reviews summary

Sas regression modeling fundamentals

According to students, this course provides a solid foundation in regression modeling using SAS/STAT software, particularly for beginners and those looking to apply concepts practically. Learners frequently commend the clarity of lectures and hands-on SAS demonstrations that reinforce understanding. While it excels at teaching linear regression and model diagnostics, some students note a brief introduction to logistic regression. Experienced users, however, may find the content too basic or slow-paced, suggesting it's best suited for those new to statistical modeling in SAS.

Course focuses exclusively on SAS, requiring learners to have access to it.

"It's a SAS course, so if you don't use SAS, it's not for you."

"It's useful if you are strictly looking for SAS-specific implementation. The focus is purely on the software."

"As a professional looking to brush up on my SAS skills, I found this course excellent."

"I appreciated the course's specificity in demonstrating SAS/STAT capabilities."

Focus on SAS/STAT implementation and real-world applicability.

"I loved the step-by-step SAS demonstrations. This course is a must for anyone wanting to apply regression effectively using SAS."

"It has directly improved my daily work."

"The course provides a strong foundation, especially for linear regression. I found the hands-on activities very practical."

"I learned how to use practical tools and strategies that I could apply immediately to my work."

Lectures are highly praised for clarity and breaking down complex topics.

"The lectures were incredibly clear, and the hands-on exercises reinforced every concept."

"The instructor's explanations were phenomenal, breaking down complex statistical concepts into easily digestible parts."

"I felt the course covered enough depth without being overwhelming. The practices are well-integrated."

"This course definitely clarified many concepts for me, particularly around ANOVA."

Highly recommended for those new to regression or SAS statistical functions.

"This course is truly a game-changer for anyone new to regression modeling with SAS."

"Excellent for beginners to intermediate users who want to use SAS for statistical modeling."

"Good starting point for SAS users."

"I found it to be a solid foundation for using SAS for statistical analysis."

Some find the introduction to logistic regression too brief or lacking depth.

"The logistic regression module was almost useless in its brevity."

"The logistic regression part was very brief, and I had to look up more resources to grasp it fully."

"Some parts felt a bit rushed, especially the logistic regression intro."

"I wished the logistic regression had more comprehensive coverage, as it was a key interest for me."

Content may be overly simplistic or redundant for experienced statisticians.

"Waste of time if you already know basic stats. The title is misleading. It should be 'SAS Syntax for Basic Regression'."

"Quite basic. I expected more from a SAS course... Disappointing for experienced users."

"The course felt a bit like a rehash of basic stats principles, mainly focused on showing SAS syntax."

"I was hoping for more advanced techniques or a deeper dive into theory. The content felt too basic for my needs."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Regression Modeling Fundamentals with these activities:

Revisit the basics of hypothesis testing

Show steps

Refreshes the essential concepts of hypothesis testing, providing a stronger foundation for the upcoming course.

Browse courses on Hypothesis Testing

Show steps

Review the concepts of null and alternative hypotheses.
Walk through examples of different types of hypothesis tests.

Review statistics concepts

Show steps

Reviewing statistics concepts will help you refresh your knowledge and prepare for the statistical techniques covered in this course.

Browse courses on Statistics

Show steps

Go over your notes or textbooks from previous statistics courses.
Review online resources or tutorials on statistics concepts.
Complete practice problems or exercises to reinforce your understanding.

Compile course materials

Show steps

Compiling your course materials will help you stay organized and have easy access to the resources you need throughout the course.

Show steps

Create a folder or digital notebook for the course.
Download or print lecture slides, notes, and assignments.
Organize and label your materials for easy retrieval.

Eight other activities

Expand to see all activities and additional details

Show all 11 activities

Participate in a study group

Show steps

Participating in a study group will provide you with an opportunity to discuss course material with peers, ask questions, and learn from different perspectives.

Show steps

Find a study group or create your own with classmates.
Meet regularly to discuss course material, review concepts, and work on assignments.
Contribute to the group by sharing your understanding and helping others.

Follow online tutorials on SAS/STAT software

Show steps

Provides hands-on practice with the software used in the course, enhancing proficiency and confidence.

Browse courses on SAS/STAT Software

Show steps

Identify reputable online resources offering tutorials on SAS/STAT software.
Follow step-by-step instructions to perform various statistical analyses using SAS/STAT software.

Complete the SAS/STAT exercises

Show steps

Completing the exercises will help you reinforce the concepts you learn in each module and improve your understanding of SAS/STAT software.

Show steps

Work through the exercises at the end of each module.
Refer to the course materials for guidance as needed.

Complete practice exercises on t tests, ANOVA, and linear regression

Show steps

Reinforces understanding of these statistical techniques through repetitive practice, improving analytical skills.

Browse courses on T Tests

Show steps

Access online platforms or textbooks that provide practice exercises on t tests.
Solve a variety of practice problems.
Check solutions to assess understanding.

Develop a SAS code library

Show steps

Developing a code library will help you organize and reuse your SAS/STAT code, making it easier to complete assignments and projects.

Show steps

Create a new folder or repository for your code library.
Write and save individual SAS/STAT code snippets for each task or technique you learn in the course.
Categorize and organize your code snippets for easy retrieval.
Document your code clearly and add comments to explain its purpose and usage.

Build a linear regression model to predict sales

Show steps

Creating a linear regression model will help you understand how to apply the concepts you learn in this course to a real-world problem.

Browse courses on Linear Regression

Show steps

Gather data on sales and relevant factors.
Clean and prepare the data for analysis.
Build a linear regression model using SAS/STAT software.
Evaluate the model's performance and make adjustments as needed.
Use the model to make predictions about future sales.

Create a data analysis project using SAS/STAT software

Show steps

Applies knowledge gained in the course to a practical project, fostering a deeper understanding and ability to apply statistical techniques.

Browse courses on Data Analysis

Show steps

Choose a real-world dataset that is relevant to a specific industry or research question.
Use SAS/STAT software to clean and explore the data.
Apply statistical techniques learned in the course to analyze the data.
Create visualizations to present the results.
Write a report summarizing the findings.

Follow tutorials on logistic regression

Show steps

Following tutorials on logistic regression will provide you with additional practice and help you develop a deeper understanding of this technique.

Browse courses on Logistic Regression

Show steps

Identify online tutorials or resources that cover logistic regression.
Work through the tutorials, following the instructions and completing any exercises.
Apply what you learn to real-world datasets or projects.

Career center

Learners who complete Regression Modeling Fundamentals will develop knowledge and skills that may be useful to these careers:

Statistician

Statisticians use statistical methods to collect, analyze, interpret, and present data. Regression Modeling Fundamentals provides Statisticians with a strong foundation in using SAS to perform statistical analyses, such as t-tests, ANOVA, and linear regression. This course also provides an introduction to logistic regression, which is useful for analyzing binary outcomes. By taking this course, Statisticians can enhance their skills in statistical analysis and improve their ability to communicate research findings.

See salaries and explore the career path for Statistician

Quantitative Analyst

Quantitative Analysts use statistical models to analyze data and make predictions. Regression Modeling Fundamentals provides Quantitative Analysts with a strong foundation in using SAS to perform statistical analyses, such as t-tests, ANOVA, and linear regression. This course also provides an introduction to logistic regression, which is useful for analyzing binary outcomes. By taking this course, Quantitative Analysts can enhance their skills in statistical modeling and improve their ability to make informed decisions.

See salaries and explore the career path for Quantitative Analyst

Data Analyst

Data Analysts use statistical software like SAS to analyze data and identify trends. Regression Modeling Fundamentals helps Data Analysts build a foundation in using SAS to perform statistical analyses, such as t-tests, ANOVA, and linear regression. This course also provides an introduction to logistic regression, which is useful for analyzing binary outcomes. By taking this course, Data Analysts can enhance their skills in data analysis and improve their ability to draw meaningful insights from data.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists use statistical methods to analyze data and solve business problems. Regression Modeling Fundamentals provides Data Scientists with a strong foundation in using SAS to perform statistical analyses, such as t-tests, ANOVA, and linear regression. This course also provides an introduction to logistic regression, which is useful for analyzing binary outcomes. By taking this course, Data Scientists can enhance their skills in statistical analysis and improve their ability to solve complex business problems.

See salaries and explore the career path for Data Scientist

Actuary

Actuaries use statistical methods to assess risk and uncertainty. Regression Modeling Fundamentals provides Actuaries with a strong foundation in using SAS to perform statistical analyses, such as t-tests, ANOVA, and linear regression. This course also provides an introduction to logistic regression, which is useful for analyzing binary outcomes. By taking this course, Actuaries can enhance their skills in statistical analysis and improve their ability to assess risk and uncertainty.

See salaries and explore the career path for Actuary

Epidemiologist

Epidemiologists use statistical methods to study the distribution and determinants of health-related states or events in specified populations. Regression Modeling Fundamentals provides Epidemiologists with a strong foundation in using SAS to perform statistical analyses, such as t-tests, ANOVA, and linear regression. This course also provides an introduction to logistic regression, which is useful for analyzing binary outcomes. By taking this course, Epidemiologists can enhance their skills in statistical analysis and improve their ability to study the distribution and determinants of health-related states or events in specified populations.

See salaries and explore the career path for Epidemiologist

Biostatistician

Biostatisticians use statistical methods to design and analyze studies in the health sciences. Regression Modeling Fundamentals provides Biostatisticians with a strong foundation in using SAS to perform statistical analyses, such as t-tests, ANOVA, and linear regression. This course also provides an introduction to logistic regression, which is useful for analyzing binary outcomes. By taking this course, Biostatisticians can enhance their skills in statistical analysis and improve their ability to design and analyze studies in the health sciences.

See salaries and explore the career path for Biostatistician

Operations Research Analyst

Operations Research Analysts use statistical methods to solve complex business problems. Regression Modeling Fundamentals provides Operations Research Analysts with a strong foundation in using SAS to perform statistical analyses, such as t-tests, ANOVA, and linear regression. This course also provides an introduction to logistic regression, which is useful for analyzing binary outcomes. By taking this course, Operations Research Analysts can enhance their skills in statistical analysis and improve their ability to solve complex business problems.

See salaries and explore the career path for Operations Research Analyst

Risk Manager

Risk Managers use statistical methods to assess and manage risk. Regression Modeling Fundamentals provides Risk Managers with a strong foundation in using SAS to perform statistical analyses, such as t-tests, ANOVA, and linear regression. This course also provides an introduction to logistic regression, which is useful for analyzing binary outcomes. By taking this course, Risk Managers can enhance their skills in statistical analysis and improve their ability to assess and manage risk.

See salaries and explore the career path for Risk Manager

Financial Analyst

Financial Analysts use statistical models to analyze financial data and make investment decisions. Regression Modeling Fundamentals provides Financial Analysts with a strong foundation in using SAS to perform statistical analyses, such as t-tests, ANOVA, and linear regression. This course also provides an introduction to logistic regression, which is useful for analyzing binary outcomes. By taking this course, Financial Analysts can enhance their skills in statistical modeling and improve their ability to make informed investment decisions.

See salaries and explore the career path for Financial Analyst

Machine Learning Engineer

Machine Learning Engineers use statistical models to build predictive models. Regression Modeling Fundamentals provides Machine Learning Engineers with a strong foundation in using SAS to perform statistical analyses, such as t-tests, ANOVA, and linear regression. This course also provides an introduction to logistic regression, which is useful for analyzing binary outcomes. By taking this course, Machine Learning Engineers can enhance their skills in statistical modeling and improve their ability to build accurate predictive models.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

Business Analysts use data to make informed decisions about business strategies. Regression Modeling Fundamentals provides Business Analysts with a strong foundation in using SAS to perform statistical analyses, such as t-tests, ANOVA, and linear regression. This course also provides an introduction to logistic regression, which is useful for analyzing binary outcomes. By taking this course, Business Analysts can enhance their skills in data analysis and improve their ability to make data-driven decisions.

See salaries and explore the career path for Business Analyst

Market Researcher

Market Researchers use statistical methods to collect and analyze data about consumer behavior. Regression Modeling Fundamentals provides Market Researchers with a strong foundation in using SAS to perform statistical analyses, such as t-tests, ANOVA, and linear regression. This course also provides an introduction to logistic regression, which is useful for analyzing binary outcomes. By taking this course, Market Researchers can enhance their skills in statistical analysis and improve their ability to understand consumer behavior.

See salaries and explore the career path for Market Researcher

Data Engineer

Data Engineers design and build systems to collect, store, and process data. Regression Modeling Fundamentals provides Data Engineers with a strong foundation in using SAS to perform statistical analyses, such as t-tests, ANOVA, and linear regression. This course also provides an introduction to logistic regression, which is useful for analyzing binary outcomes. By taking this course, Data Engineers can enhance their skills in statistical analysis and improve their ability to design and build systems to collect, store, and process data.

See salaries and explore the career path for Data Engineer

Software Engineer

Software Engineers design and develop software applications. Regression Modeling Fundamentals may be useful for Software Engineers who want to develop data-driven applications. This course provides a strong foundation in using SAS to perform statistical analyses, such as t-tests, ANOVA, and linear regression. By taking this course, Software Engineers can enhance their skills in statistical analysis and improve their ability to develop data-driven applications.

See salaries and explore the career path for Software Engineer

Reading list

We've selected 13 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Regression Modeling Fundamentals.

Applied Linear Statistical Models

Save

Provides a comprehensive introduction to linear regression models, including both theory and applications. It valuable resource for anyone who wants to learn more about this topic.

Regression Modeling Fundamentals

Two deals to help you save

What's inside

Syllabus

Traffic lights

Save this course

Reviews summary

Sas regression modeling fundamentals

Activities

Career center

Reading list

Share

Similar courses