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Modern Regression Analysis in R

This course will provide a set of foundational statistical modeling tools for data science. In particular, students will be introduced to methods, theory, and applications of linear statistical models, covering the topics of parameter estimation, residual diagnostics, goodness of fit, and various strategies for variable selection and model comparison. Attention will also be given to the misuse of statistical models and ethical implications of such misuse.

This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

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What's inside

Syllabus

Introduction to Statistical Models

In this module, we will introduce the basic conceptual framework for statistical modeling in general, and for linear regression models in particular.

Linear Regression Parameter Estimation

In this module, we will learn how to fit linear regression models with least squares. We will also study the properties of least squares, and describe some goodness of fit metrics for linear regression models.

Inference in Linear Regression

In this module, we will study the uses of linear regression modeling for justifying inferences from samples to populations.

Prediction and Explanation in Linear Regression Analysis

In this module, we will identify how models can predict future values, as well as construct interval estimates for those values. We will also explore the relationship between statistical modelling and causal explanations.

Regression Diagnostics

In this module, we will learn how to diagnose issues with the fit of a linear regression model. In particular, we will use formal tests and visualizations to decide whether a linear model is appropriate for the data at hand.

Model Selection and Multicollinearity

In this module, we will study methods for model selection and model improvement.. In particular, we will learn when and how to apply model selection techniques such as forward selection and backward selection, criterion-based methods, and will learn about the problem of multicollinearity (also called collinearity).

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

For those new to data science, the course offers a solid introduction to linear statistical models

Explores linear regression parameter estimation and examines its role in model fitting

Teaches linear regression modeling for making inferences from samples to populations

Provides a detailed study of regression diagnostics, which is essential for assessing the validity of statistical models

Covers model selection and multicollinearity, important topics for improving the accuracy and interpretability of statistical models

Students with a background in statistics or mathematics may find the introductory modules somewhat basic

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Reviews summary

Modern regression analysis course

Learners say that this Modern Regression Analysis course is well-received and challenging, but engaging and rewarding overall. Students praise the thorough explanations and well-thought-out assignments, but some have experienced issues with the autograded assignments. Despite this, many learners recommend this course for those seeking to enhance their understanding of regression analysis and its applications.

Challenging assignments that enhance learning.

"Something i did not like is the peer review assignments sometimes they took to much time to be reviewed."

"Assignments are very well thought."

"Highly recommended for those getting their feet wet in regression and for those already familiar with the techniques but need to brush up on the theoretical aspects of it."

Excellent explanations of complex topics.

"Professor Zaharatos explains the meth background needed in a excellent way."

"The instructor explained the material very well."

"Thorough review of simple linear regression and multiple linear regression with a good bit of well-explained theory."

Some issues with grading of assignments.

"The autograder for assignments is frustrating."

"Some minor problems with the autograded assignments but most of them are manageable after sometime."

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 Modern Regression Analysis in R with these activities:

Review basic statistics and linear algebra

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Refresh your memory on basic statistical concepts and linear algebra to provide a strong foundation for this course.

Browse courses on Statistics

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Review notes from previous courses or online resources
Practice solving basic statistical and linear algebra problems

Review 'Applied Linear Statistical Models' by Kutner, Nachtsheim, and Neter

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Expand your knowledge of linear statistical models through a comprehensive textbook, enhancing your theoretical understanding and practical application skills.

View Applied Linear Statistical Models 5ed (Pb 2013) on Amazon

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Read selected chapters relevant to course topics
Complete exercises and problems provided in the book
Summarize key concepts and findings in your own notes

Work through R tutorials for linear regression

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Develop an understanding of the practical aspects of implementing linear regression models using the R programming language.

Browse courses on Linear Regression

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Find online tutorials or courses on linear regression in R
Follow the instructions and complete exercises
Apply the learned techniques to analyze a dataset

Four other activities

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Solve practice problems on statistical modeling

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Reinforce your understanding of the concepts and techniques covered in the course through regular practice.

Browse courses on Statistical Modeling

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Find practice problems from textbooks, online resources, or assignments
Attempt to solve the problems independently
Check your solutions against provided answers or consult with peers or the instructor

Form a study group with classmates

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Collaborate with peers to discuss course concepts, solve problems, and reinforce your understanding through active engagement.

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Find classmates who are interested in forming a study group
Set regular meeting times and locations
Discuss course material, share perspectives, and solve problems together

Build a linear regression model for a real-world dataset

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Apply the knowledge gained in the course to a practical problem, enhancing your ability to solve real-world data analysis challenges.

Browse courses on Data Analysis

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Identify a suitable dataset
Clean and prepare the data
Build a linear regression model using statistical software
Evaluate the model's performance and interpret the results
Write a report summarizing your findings

Create a blog post or video explaining a linear regression concept

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Reinforce your understanding of linear regression by teaching it to others, solidifying your knowledge and developing effective communication skills.

Browse courses on Linear Regression

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Choose a specific linear regression concept to focus on
Research and gather information from reliable sources
Create a blog post or video that clearly explains the concept and provides examples
Share your content with others and engage in discussions

Career center

Learners who complete Modern Regression Analysis in R will develop knowledge and skills that may be useful to these careers:

Data Visualization Analyst

Data Visualization Analysts use data to create visual representations of data. This course can help you develop the skills you need to succeed in this role by providing you with a strong foundation in statistical modeling, including linear regression, inference, prediction, and model selection. These skills are essential for understanding and working with data, and they will give you a competitive advantage in the job market.

See salaries and explore the career path for Data Visualization Analyst

Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models. This course can help you develop the skills you need to succeed in this role by providing you with a strong foundation in statistical modeling, including linear regression, inference, prediction, and model selection. These skills are essential for understanding and working with data, and they will give you a competitive advantage in the job market.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

Business Analysts use data to help businesses make informed decisions. This course can help you develop the skills you need to succeed in this role by providing you with a strong foundation in statistical modeling, including linear regression, inference, prediction, and model selection. These skills are essential for understanding and working with data, and they will give you a competitive advantage in the job market.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to solve business problems. This course can help you develop the skills you need to succeed in this role by providing you with a strong foundation in statistical modeling, including linear regression, inference, prediction, and model selection. These skills are essential for understanding and working with data, and they will give you a competitive advantage in the job market.

See salaries and explore the career path for Operations Research Analyst

Statistician

Statisticians use mathematical and statistical techniques to collect, analyze, interpret, and present data. This course can help you develop the skills you need to succeed in this role by providing you with a strong foundation in statistical modeling, including linear regression, inference, prediction, and model selection. These skills are essential for understanding and working with data, and they will give you a competitive advantage in the job market.

See salaries and explore the career path for Statistician

Data Scientist

Data Scientists are responsible for collecting, analyzing, and interpreting data to help businesses make informed decisions. This course can help you develop the skills you need to succeed in this role by providing you with a solid foundation in statistical modeling, including linear regression, inference, prediction, and model selection. These skills are essential for understanding and working with data, and they will give you a competitive advantage in the job market.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts collect, clean, and analyze data to help businesses make informed decisions. This course can help you develop the skills you need to succeed in this role by providing you with a solid foundation in statistical modeling, including linear regression, inference, prediction, and model selection. These skills are essential for understanding and working with data, and they will give you a competitive advantage in the job market.

See salaries and explore the career path for Data Analyst

Financial Analyst

Financial Analysts use data to make investment decisions. This course can help you develop the skills you need to succeed in this role by providing you with a strong foundation in statistical modeling, including linear regression, inference, prediction, and model selection. These skills are essential for understanding and working with financial data, and they will give you a competitive advantage in the job market.

See salaries and explore the career path for Financial Analyst

Risk Analyst

Risk Analysts use statistical models to assess and manage risk. This course can help you develop the skills you need to succeed in this role by providing you with a strong foundation in statistical modeling, including linear regression, inference, prediction, and model selection. These skills are essential for understanding and working with data, and they will give you a competitive advantage in the job market.

See salaries and explore the career path for Risk Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course can help you develop the skills you need to succeed in this role by providing you with a strong foundation in statistical modeling, including linear regression, inference, prediction, and model selection. These skills are essential for understanding and working with financial data, and they will give you a competitive advantage in the job market.

See salaries and explore the career path for Quantitative Analyst

Actuary

Actuaries use mathematical and statistical models to assess and manage risk. This course can help you develop the skills you need to succeed in this role by providing you with a strong foundation in statistical modeling, including linear regression, inference, prediction, and model selection. These skills are essential for understanding and working with data, and they will give you a competitive advantage in the job market.

See salaries and explore the career path for Actuary

Epidemiologist

Epidemiologists use statistical models to study the causes of disease. This course can help you develop the skills you need to succeed in this role by providing you with a strong foundation in statistical modeling, including linear regression, inference, prediction, and model selection. These skills are essential for understanding and working with epidemiological data, and they will give you a competitive advantage in the job market.

See salaries and explore the career path for Epidemiologist

Biostatistician

Biostatisticians use statistical models to analyze data in the field of medicine. This course can help you develop the skills you need to succeed in this role by providing you with a strong foundation in statistical modeling, including linear regression, inference, prediction, and model selection. These skills are essential for understanding and working with medical data, and they will give you a competitive advantage in the job market.

See salaries and explore the career path for Biostatistician

Market Researcher

Market Researchers use data to understand consumer behavior. This course can help you develop the skills you need to succeed in this role by providing you with a solid foundation in statistical modeling, including linear regression, inference, prediction, and model selection. These skills are essential for understanding and working with data, and they will give you a competitive advantage in the job market.

See salaries and explore the career path for Market Researcher

Software Engineer

Software Engineers design, develop, and deploy software applications. This course may be useful for those who wish to specialize in data science or machine learning. The course will provide you with a strong foundation in statistical modeling, including linear regression, inference, prediction, and model selection. These skills are essential for understanding and working with data, and they will give you a competitive advantage in the job market.

See salaries and explore the career path for Software Engineer

Reading list

We've selected 15 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 Modern Regression Analysis in R.

An Introduction to Statistical Learning

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Provides a comprehensive introduction to statistical learning methods, including linear regression. It is written in a clear and accessible style and includes numerous examples and exercises.

Modern Regression Analysis in R

Three deals to help you save

What's inside

Syllabus

Good to know

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Reviews summary

Modern regression analysis course

Activities

Career center

Reading list

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