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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.

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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.

Model Building and Effect Selection

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.

Model Post-Fitting for Inference

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.

Model Building for Scoring and Prediction

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.

Categorical Data Analysis

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.

Good to know

Know what's good

, what to watch for

, 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

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Reviews summary

Regression modeling with sas

According to students, Regression Modeling Fundamentals is a helpful course if learners have already taken the prior course in the Specialization. Reviews say Jordan Bakerman is a great instructor, but acknowledge that learners should have a solid grounding in statistics. The course covers linear regression and logistic regression, and it also discusses SAS procedures for statistical analysis.

Covers using SAS for regression modelling.

"This was another good course from SAS and Coursera."

Learned about odds ratios, association, etc.

"Learned a lot about the fundamentals and use of linear regression and logistic regression, odds ratios, association, etc."

Provides step-by-step processes.

"Great explanations and step by step processes"

Course is well-received.

"good learning platform"

Have the prior course background in the Specialization.

"Must have taken the prior Course. In the Specialization."

Jordan Bakerman is great.

"Thanks so much to our instructor, Jordan Bakerman for teaching this course!"

Some presentations are hurried.

"I felt that some of the presentations were hurried and quite dense and I had to review some of the videos several times"

Not for those without statistics background.

"Definitely not for someone without a solid background in statistics."

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

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

Browse courses on Hypothesis Testing

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

Review statistics concepts

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

Browse courses on Statistics

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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

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

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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.

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Participate in a study group

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

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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

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

Browse courses on SAS/STAT Software

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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

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

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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

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

Browse courses on T Tests

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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

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

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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

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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

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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

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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

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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

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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

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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

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

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

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

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

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

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

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

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

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

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.

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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.

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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

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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

What's inside

Syllabus

Good to know

Save this course

Reviews summary

Regression modeling with sas

Activities

Career center

Reading list

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