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Linear Regression Modeling for Health Data

Philip S. Boonstra and Bhramar Mukherjee

This course provides learners with a first look at the world of statistical modeling. It begins with a high-level overview of different philosophies on the question of 'what is a statistical model' and introduces learners to the core ideas of traditional statistical inference and reasoning. Learners will get their first look at the ever-popular t-test and delve further into linear regression. They will also learn how to fit and interpret regression models for a continuous outcome with multiple predictors. All concepts taught in this course will be covered with multiple modalities: slide-based lectures, guided coding practice with the instructor, and independent but structured exercises.

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What's inside

Syllabus

Principles of Statistical Modeling

This module gives you a first look at the world of statistical modeling. It begins with a high-level overview of different philosophies on the question of 'what is a statistical model' and introduces you to the core ideas of traditional statistical inference and reasoning. At the end of the module, you will have an introductory understanding of important terms such as 'sample-to-population' (STOP) principle, sampling variation, and measures of statistical uncertainty. You will also get your first look at the ever popular t-test.

Simple Linear Regression

This module takes you beyond t-test into linear regression. By the end of the module, you will understand how linear regression is a generalization of the t-test.

Multiple Linear Regression

A key reason that linear regression is so powerful is that it allows to adjust for multiple predictors at the same time. In Module 3, you will learn how to fit regression models for multiple predictors. You will see how to interpret the resulting model and how to use it to answer different questions about your data.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Covers core ideas of statistical inference and reasoning, helping learners build a foundation in understanding statistical concepts

Introduces learners to the widely used t-test, providing a practical application of statistical inference

Progresses beyond t-test into linear regression, expanding learners' understanding of statistical modeling techniques

Empowers learners to fit and interpret regression models with multiple predictors, enhancing their ability to analyze complex datasets

Provides a blend of learning modalities, including slide-based lectures, coding practice, and exercises, catering to diverse learning styles

Taught by instructors Philip S. Boonstra and Bhramar Mukherjee, both recognized for their expertise in the field of statistics

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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 Linear Regression Modeling for Health Data with these activities:

Review basic probability and statistics concepts

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Refreshes essential foundational knowledge for success in this course

Browse courses on Probability

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Review notes or textbooks on probability and statistics
Complete practice problems to test understanding

Watch online tutorials on statistical modeling concepts

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Provides additional support and reinforcement of course material

Browse courses on Statistical Modeling

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Identify relevant online tutorials
Watch the tutorials attentively
Take notes and summarize key points

Form a study group with classmates to discuss course material and practice problems

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Fosters collaboration, promotes active learning, and provides support from peers

Browse courses on Statistical Modeling

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Find classmates who are interested in forming a study group
Establish regular meeting times and locations
Prepare for each meeting by reviewing the material
Actively participate in discussions and ask questions

Five other activities

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Read 'Introduction to Statistical Modeling' by John Fox

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Provides a comprehensive introduction to statistical modeling, addressing two core questions: what is a statistical model and what is the process of statistical modeling?

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Read chapters 1 and 2
Complete the exercises at the end of each chapter
Summarize the key concepts in your own words

Complete the practice problems in the course textbook

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Provides an opportunity to apply the concepts learned in the course and reinforce understanding

Browse courses on t-Test

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Identify the relevant practice problems
Attempt to solve the problems independently
Check your solutions against the provided answer key

Create a presentation on a statistical modeling topic

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Encourages a deeper understanding of the material through active recall and synthesis

Browse courses on Statistical Modeling

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Choose a topic
Research the topic
Create a presentation outline
Develop the presentation slides
Practice presenting

Develop a predictive model using a real-world dataset

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Provides practical experience in applying statistical modeling to real-world problems

Browse courses on Statistical Modeling

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Identify a suitable dataset
Clean and prepare the data
Build and evaluate a predictive model
Interpret the results and make predictions

Contribute to an open-source statistical modeling project

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Provides hands-on experience in working with real-world statistical modeling code

Browse courses on Statistical Modeling

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Identify a suitable open-source project
Read the project documentation
Contribute improvements or new features
Submit a pull request

Career center

Learners who complete Linear Regression Modeling for Health Data will develop knowledge and skills that may be useful to these careers:

Statistician

Statisticians use their knowledge of statistics to design surveys, collect and analyze data, and interpret the results. They work in a variety of industries, including healthcare, finance, and marketing. The skills you learn in this course, such as how to fit and interpret regression models, will be essential for success in this role. Additionally, the course provides a strong foundation in statistical principles, which will be helpful for understanding more advanced statistical techniques.

See salaries and explore the career path for Statistician

Data Analyst

Data Analysts collect, clean, and analyze data to help businesses make informed decisions. They use a variety of statistical techniques, including regression analysis, to identify trends and patterns in data. The skills you learn in this course will be essential for success in this role. Additionally, the course provides a strong foundation in statistical principles, which will be helpful for understanding more advanced data analysis techniques.

See salaries and explore the career path for Data Analyst

Biostatistician

Biostatisticians apply statistical methods to solve problems in the health sciences. They work with researchers to design clinical trials, analyze data, and interpret results. The skills you learn in this course will be essential for success in this role. Additionally, the course provides a strong foundation in statistical principles, which will be helpful for understanding more advanced biostatistical techniques.

See salaries and explore the career path for Biostatistician

Epidemiologist

Epidemiologists study the distribution and causes of disease in populations. They use a variety of statistical techniques, including regression analysis, to identify risk factors for disease and develop prevention strategies. The skills you learn in this course will be essential for success in this role. Additionally, the course provides a strong foundation in statistical principles, which will be helpful for understanding more advanced epidemiological techniques.

See salaries and explore the career path for Epidemiologist

Market Researcher

Market Researchers collect and analyze data about consumers and markets. They use a variety of statistical techniques, including regression analysis, to identify trends and patterns in data. The skills you learn in this course will be essential for success in this role. Additionally, the course provides a strong foundation in statistical principles, which will be helpful for understanding more advanced market research techniques.

See salaries and explore the career path for Market Researcher

Actuary

Actuaries use statistical methods to assess risk and uncertainty. They work in a variety of industries, including insurance, finance, and healthcare. The skills you learn in this course will be essential for success in this role. Additionally, the course provides a strong foundation in statistical principles, which will be helpful for understanding more advanced actuarial techniques.

See salaries and explore the career path for Actuary

Operations Research Analyst

Operations Research Analysts use statistical methods to improve the efficiency of organizations. They work in a variety of industries, including manufacturing, transportation, and healthcare. The skills you learn in this course will be essential for success in this role. Additionally, the course provides a strong foundation in statistical principles, which will be helpful for understanding more advanced operations research techniques.

See salaries and explore the career path for Operations Research Analyst

Financial Analyst

Financial Analysts use statistical methods to analyze financial data and make investment recommendations. They work in a variety of industries, including investment banking, asset management, and financial planning. The skills you learn in this course will be essential for success in this role. Additionally, the course provides a strong foundation in statistical principles, which will be helpful for understanding more advanced financial analysis techniques.

See salaries and explore the career path for Financial Analyst

Data Scientist

Data Scientists use statistical methods to analyze data and solve business problems. They work in a variety of industries, including technology, healthcare, and finance. The skills you learn in this course will be essential for success in this role. Additionally, the course provides a strong foundation in statistical principles, which will be helpful for understanding more advanced data science techniques.

See salaries and explore the career path for Data Scientist

Computer Programmer

Computer Programmers write and maintain computer code. While not directly related to statistics, the skills you learn in this course, such as problem-solving and critical thinking, will be helpful for success in this role.

See salaries and explore the career path for Computer Programmer

Software Engineer

Software Engineers design, develop, and test software applications. While not directly related to statistics, the skills you learn in this course, such as problem-solving and critical thinking, will be helpful for success in this role.

See salaries and explore the career path for Software Engineer

Database Administrator

Database Administrators manage and maintain databases. While not directly related to statistics, the skills you learn in this course, such as problem-solving and critical thinking, will be helpful for success in this role.

See salaries and explore the career path for Database Administrator

Web Developer

Web Developers design and develop websites. While not directly related to statistics, the skills you learn in this course, such as problem-solving and critical thinking, will be helpful for success in this role.

See salaries and explore the career path for Web Developer

Network Administrator

Network Administrators manage and maintain computer networks. While not directly related to statistics, the skills you learn in this course, such as problem-solving and critical thinking, will be helpful for success in this role.

See salaries and explore the career path for Network Administrator

Systems Analyst

Systems Analysts design and implement computer systems. While not directly related to statistics, the skills you learn in this course, such as problem-solving and critical thinking, will be helpful for success in this role.

See salaries and explore the career path for Systems Analyst

Reading list

We've selected nine 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 Linear Regression Modeling for Health Data.