Building Statistical Models in R: Linear Regression from Coursera

By the end of this 2-hour long project, you will understand how to build and interpret the result of simple linear regression models in R. Also, you will learn how to perform model assessments and check for assumptions using diagnostic plots. By extension, you will learn how to build and interpret the result of a multiple linear regression model.

Note that you do not need to be a data scientist to be successful in this guided project; just a familiarity with basic statistics and R suffice for this project. If you are not familiar with R and want to learn the basics, start with my previous guided project titled “Getting Started with R”. So, taking this project will give the needed requisite to complete this project on Building Statistical Models in R: Linear Regression. However, if you are comfortable using R, please join me on this wonderful and exciting ride! Let’s get our hands dirty!

What's inside

Syllabus

Project Overview

Welcome to this project-based course Building Statistical Models in R: Linear Regression. This is a hands-on project that introduces beginners to the world of statistical modeling. In this project, you will learn the basics of building statistical models in R. We will start this hands-on project by exploring the dataset and creating visualizations for the dataset. By the end of this 2-hour long project, you will understand how to build and interpret the result of simple linear regression models in R. Also, you will learn how to perform model assessments and check for assumptions using diagnostic plots. By extension, you will learn how to build and interpret the result of a multiple linear regression model. Note that you do not need to be a data scientist to be successful in this guided project; just a familiarity with basic statistics and R suffice for this project. If you are not familiar with R and want to learn the basics, start with my previous guided project titled “Getting Started with R”. So, taking this project will give the needed requisite to complete this project on Building Statistical Models in R: Linear Regression. However, if you are comfortable using R, please join me on this wonderful and exciting ride! Let’s get our hands dirty!

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches statistical modeling, a widely used skill in industry, research, and academia

Introduces students with basic statistics and R knowledge to statistical modeling

Suitable for beginners seeking an introduction to statistical modeling using R

May require some familiarity with basic statistics and R for a smooth learning experience

Reviews summary

Intermediate statistics w/r

According to students, Building Statistical Models in R: Linear Regression teaches intermediate level statistical skills using the programming language R. Learners say the course is well-structured and engaging, but may require additional knowledge and experience with R before taking this course.

This course covers more advanced statistical concepts.

"Awesome sauce! There's a bit to learn about skills relevant to data analytics and even more pertaining to stats. "

Learners should be familiar with R before taking this course.

"This class assumes students have at least fundamental working knowledge of R, which for me is what tripped me up."

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 Building Statistical Models in R: Linear Regression with these activities:

Review and organize your notes, assignments, and quizzes

Show steps

Enhance your retention and understanding by organizing and reviewing your course materials.

Browse courses on Organization

Show steps

Gather all of your notes, assignments, and quizzes.
Review your materials and identify any areas where you need additional clarification.
Create a system for organizing your materials, such as using folders or a binder.
Review your materials regularly, especially before exams.
Consider using flashcards or other study aids to help you remember key concepts.

Organize a study group with peers to discuss linear regression concepts

Show steps

Foster collaboration and reinforce your understanding by discussing linear regression concepts with peers.

Browse courses on Linear Regression

Show steps

Gather a group of peers who are also taking or interested in linear regression.
Decide on a regular meeting schedule and location.
Take turns presenting different aspects of linear regression to the group.
Discuss and critique each other's presentations.
Work together on practice problems and projects.

Follow R programming tutorials for data science

Show steps

Review the fundamentals of R programming and reinforce your understanding of its applications in data science.

Browse courses on R Programming

Show steps

Identify relevant R programming tutorials online.
Complete the tutorials at your own pace, taking notes and practicing the concepts.
Create a small data science project to demonstrate your understanding.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Practice solving linear regression problems

Show steps

Sharpen your problem-solving skills in linear regression, improving your understanding of its applications.

Browse courses on Linear Regression

Show steps

Find practice problems online or in textbooks.
Solve the problems step-by-step, checking your solutions against the provided answers.
Repeat until you feel confident in your understanding.

Write a blog post explaining linear regression concepts

Show steps

Solidify your understanding of linear regression by explaining its concepts to others, enhancing your ability to recall and retain information.

Browse courses on Linear Regression

Show steps

Research and gather information about linear regression.
Organize your thoughts and create an outline for your blog post.
Write the blog post, explaining the concepts in a clear and concise manner.
Proofread and edit your blog post before publishing.
Share your blog post on social media or other online platforms.

Attend a workshop on advanced linear regression techniques

Show steps

Expand your knowledge of linear regression by delving into advanced techniques, broadening your understanding and increasing your expertise.

Browse courses on Linear Regression

Show steps

Search for workshops on advanced linear regression techniques.
Register and attend the workshop.
Participate actively in the workshop, asking questions and taking notes.
Apply the techniques learned in the workshop to your own research or projects.

Participate in a data science competition involving linear regression

Show steps

Challenge yourself and test your skills in linear regression through participation in a data science competition.

Browse courses on Linear Regression

Show steps

Find a data science competition that involves linear regression.
Download the data and familiarize yourself with the problem.
Apply your linear regression skills to build a model and generate predictions.
Submit your predictions to the competition.
Analyze the results and learn from your experience.

Career center

Learners who complete Building Statistical Models in R: Linear Regression will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers design and build machine learning models to solve complex problems. This course provides a foundation in statistical modeling, which is essential for understanding and building machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Statistician

Statisticians collect, analyze, interpret, and present data to inform decision-making. This course is a perfect fit for aspiring Statisticians, teaching the basics of building statistical models in R, which is an essential skill for this role.

See salaries and explore the career path for Statistician

Biostatistician

Biostatisticians apply statistical methods to data in the field of healthcare and medicine. Statistical modeling is a key skill for Biostatisticians, and this course can help them build a strong foundation in the subject.

See salaries and explore the career path for Biostatistician

Actuary

Actuaries use mathematical and statistical techniques to assess risk and uncertainty in various fields, such as insurance and finance. Statistical modeling is an essential skill for Actuaries, and this course can provide a solid foundation in the basics.

See salaries and explore the career path for Actuary

Epidemiologist

Epidemiologists use statistical models to study the causes and spread of diseases. This course provides a foundation in statistical modeling that is essential for Epidemiologists to understand and analyze disease data.

See salaries and explore the career path for Epidemiologist

Data Scientist

Data Scientists use data to solve complex problems and gain insights into complex issues. This course provides a strong foundation in statistical modeling, which is a fundamental skill for Data Scientists.

See salaries and explore the career path for Data Scientist

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course can help Quantitative Analysts build a foundation in statistical modeling, which is essential for this role.

See salaries and explore the career path for Quantitative Analyst

Market Researcher

Market Researchers design and conduct surveys and other research studies to collect and analyze data about consumer behavior. This course provides a foundation in statistical modeling, which is used extensively in market research.

See salaries and explore the career path for Market Researcher

Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to improve the efficiency and effectiveness of organizations. This course can provide Operations Research Analysts with the skills needed to build and analyze statistical models for decision-making.

See salaries and explore the career path for Operations Research Analyst

Marketing Analyst

Marketing Analysts are responsible for analyzing market research data to understand customer behavior and preferences. This course can provide Marketing Analysts with the skills needed to analyze data and develop marketing campaigns that are informed by statistical models.

See salaries and explore the career path for Marketing Analyst

Financial Analyst

Financial Analysts use financial data to make investment decisions and provide advice to clients. This course can help Financial Analysts build a foundation in statistical modeling, which is useful for analyzing financial data and making informed investment decisions.

See salaries and explore the career path for Financial Analyst

Business Analyst

Business Analysts use data to identify and solve business problems. This course can help Business Analysts build a foundation in statistical modeling, which is used to analyze data and make recommendations for business decisions.

See salaries and explore the career path for Business Analyst

Economist

Economists use statistical models to analyze economic data and make predictions about economic trends. This course may be useful for aspiring Economists, providing a foundation in statistical modeling that can be used to analyze economic data.

See salaries and explore the career path for Economist

Data Analyst

A Data Analyst uses programming and data analysis techniques to examine and interpret data, often presenting their findings for data-driven decision-making. This course may be useful for Data Analysts, providing a foundation in statistical modeling that can be used to make informed decisions based on data.

See salaries and explore the career path for Data Analyst

Software Engineer

Software Engineers design, develop, and maintain software systems. While not a direct fit, a foundation in statistical modeling can be useful for Software Engineers who work on data-intensive applications.

See salaries and explore the career path for Software Engineer