Regression Modeling in Practice from Coursera

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Syllabus

Introduction to Regression

This session starts where the Data Analysis Tools course left off. This first set of videos provides you with some conceptual background about the major types of data you may work with, which will increase your competence in choosing the statistical analysis that’s most appropriate given the structure of your data, and in understanding the limitations of your data set. We also introduce you to the concept of confounding variables, which are variables that may be the reason for the association between your explanatory and response variable. Finally, you will gain experience in describing your data by writing about your sample, the study data collection procedures, and your measures and data management steps.

Basics of Linear Regression

In this session, we discuss more about the importance of testing for confounding, and provide examples of situations in which a confounding variable can explain the association between an explanatory and response variable. In addition, now that you have statistically tested the association between an explanatory variable and your response variable, you will test and interpret this association using basic linear regression analysis for a quantitative response variable. You will also learn about how the linear regression model can be used to predict your observed response variable. Finally, we will also discuss the statistical assumptions underlying the linear regression model, and show you some best practices for coding your explanatory variables Note that if your research question does not include one quantitative response variable, you can use one from your data set just to get some practice with the tool.

Multiple Regression

Multiple regression analysis is tool that allows you to expand on your research question, and conduct a more rigorous test of the association between your explanatory and response variable by adding additional quantitative and/or categorical explanatory variables to your linear regression model. In this session, you will apply and interpret a multiple regression analysis for a quantitative response variable, and will learn how to use confidence intervals to take into account error in estimating a population parameter. You will also learn how to account for nonlinear associations in a linear regression model. Finally, you will develop experience using regression diagnostic techniques to evaluate how well your multiple regression model predicts your observed response variable. Note that if you have not yet identified additional explanatory variables, you should choose at least one additional explanatory variable from your data set. When you go back to your codebooks, ask yourself a few questions like “What other variables might explain the association between my explanatory and response variable?”; “What other variables might explain more of the variability in my response variable?”, or even “What other explanatory variables might be interesting to explore?” Additional explanatory variables can be either quantitative, categorical, or both. Although you need only two explanatory variables to test a multiple regression model, we encourage you to identify more than one additional explanatory variable. Doing so will really allow you to experience the power of multiple regression analysis, and will increase your confidence in your ability to test and interpret more complex regression models. If your research question does not include one quantitative response variable, you can use the same quantitative response variable that you used in Module 2, or you may choose another one from your data set.

Logistic Regression

In this session, we will discuss some things that you should keep in mind as you continue to use data analysis in the future. We will also teach also you how to test a categorical explanatory variable with more than two categories in a multiple regression analysis. Finally, we introduce you to logistic regression analysis for a binary response variable with multiple explanatory variables. Logistic regression is simply another form of the linear regression model, so the basic idea is the same as a multiple regression analysis. But, unlike the multiple regression model, the logistic regression model is designed to test binary response variables. You will gain experience testing and interpreting a logistic regression model, including using odds ratios and confidence intervals to determine the magnitude of the association between your explanatory variables and response variable. You can use the same explanatory variables that you used to test your multiple regression model with a quantitative outcome, but your response variable needs to be binary (categorical with 2 categories). If you have a quantitative response variable, you will have to bin it into 2 categories. Alternatively, you can choose a different binary response variable from your data set that you can use to test a logistic regression model. If you have a categorical response variable with more than two categories, you will need to collapse it into two categories.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops foundational skills and deep expertise in regression analysis

Taught by Jen Rose and Lisa Dierker, who are recognized for their work in data analysis

Covers the basic assumptions, limitations, and statistical interpretation of regression analysis

Utilizes both SAS and Python, which are widely used tools for data analysis

Emphasizes the identification of confounding variables, which is crucial for understanding the true relationship between variables

Teaches techniques for evaluating the quality of regression models, allowing learners to assess the validity of their findings

Reviews summary

Regression modeling in practice well received

Learners say Regression Modeling in Practice is a well received course with engaging assignments and useful coding examples.

Students find the assignments to be interesting and helpful for their understanding of the material.

"It needs hard work and a lot of practicing"

"Great explanation of stat and useful coding examples"

Some learners experienced technical issues with the course, including non-working example programs, assignment evaluations requiring multiple blog entries, and low sound quality in videos.

"example programs not working (data sets located at different path)"

"assignment evaluations require multiple blog entries"

"sound in some videos is too low"

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 in Practice with these activities:

Review fundamental data analysis concepts.

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Refresh your understanding of the basic principles of data analysis, including data types, data collection methods, and data exploration techniques, to strengthen your foundation for this course.

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Revisit your lecture notes or textbook materials from a previous introductory data analysis course.
Complete online tutorials or exercises that cover the basics of data analysis.

Read and summarize "Applied Regression Analysis and Generalized Linear Models" by John Fox.

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Solidify your understanding of regression analysis by reading a comprehensive book on the topic. This will provide you with a deeper theoretical foundation and practical insights to complement the course content.

View A Mathematical Primer for Social Statistics on Amazon

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Obtain a copy of the book "Applied Regression Analysis and Generalized Linear Models" by John Fox.
Read and take notes on the relevant chapters that align with the course topics.
Summarize key concepts, equations, and examples from the book in your own words.
Identify areas where the book provides additional insights or補足 to the course materials.

Attend a study group to discuss the assumptions of regression analysis.

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Enhance your comprehension of the assumptions underlying regression analysis through peer discussions. Active participation in study groups can clarify concepts and provide diverse perspectives.

Browse courses on Statistical Modeling

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Join or form a study group with fellow learners.
Prepare questions and discussion points related to regression analysis assumptions.
Actively participate in the study group, sharing insights and engaging in discussions.
Summarize key takeaways and areas where further understanding is needed.

Four other activities

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Practice linear regression analysis using Python or SAS.

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Reinforce your understanding of linear regression analysis by working through practice problems and datasets. This will enhance your ability to interpret regression coefficients and evaluate the quality of regression models.

Browse courses on Linear Regression

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Find online resources or textbooks with practice exercises on linear regression.
Install the necessary software (Python or SAS) and libraries.
Load and explore a dataset suitable for linear regression analysis.
Build linear regression models using the chosen software.
Interpret the regression coefficients and evaluate the goodness of fit of the models.

Explore online tutorials on logistic regression analysis for binary outcomes.

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Expand your knowledge of logistic regression analysis by accessing online tutorials. This will enhance your ability to analyze and interpret data involving binary outcomes.

Browse courses on Logistic Regression

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Search for reputable online platforms or resources offering tutorials on logistic regression.
Choose a tutorial that aligns with your learning style and the concepts you need to reinforce.
Follow the tutorial, taking notes and working through the examples provided.
Apply the concepts learned to practice problems or real-world datasets.

Develop a presentation on a real-world application of multiple regression.

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Deepen your understanding of multiple regression by exploring its practical applications. This activity will challenge you to identify a real-world scenario where multiple regression can be used and effectively communicate your findings.

Browse courses on Multiple Regression

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Identify a business or research question that can be addressed using multiple regression.
Gather and prepare a dataset that includes relevant variables.
Build a multiple regression model and interpret the results.
Create a presentation that clearly explains the problem, approach, and insights gained from the analysis.

Develop a data visualization dashboard to showcase the results of a multiple regression analysis.

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Enhance your ability to communicate data insights effectively by creating an interactive dashboard. This project will challenge you to present the results of a multiple regression analysis in a visually appealing and informative manner.

Browse courses on Data Visualization

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Choose a dataset and perform a multiple regression analysis to identify significant relationships.
Select appropriate data visualization techniques to represent the key findings.
Use a dashboarding tool to create an interactive visualization that allows users to explore the data and insights.
Add annotations, context, and storytelling elements to guide users through the dashboard.

Career center

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

Statistician

Statisticians collect, analyze, interpret, and present data. They use statistical methods to develop models and make predictions. Regression Modeling in Practice provides a comprehensive overview of regression analysis, including linear regression, multiple regression, and logistic regression. This course helps build a foundation for Statisticians who need to use regression models in their research or work.

See salaries and explore the career path for Statistician

Data Scientist

Data Scientists build and improve analytical models that support decision-making. They analyze and interpret data to identify patterns and trends. Regression Modeling in Practice teaches the fundamentals of regression analysis, a statistical technique used to predict outcomes based on one or more independent variables. This course provides a solid foundation for Data Scientists who need to understand and apply regression models in their work.

See salaries and explore the career path for Data Scientist

Quantitative Analyst

Quantitative Analysts (Quants) use mathematical and statistical models to analyze financial data and make investment decisions. Regression Modeling in Practice teaches the basics of regression analysis, a technique used to predict outcomes based on one or more independent variables. This course provides a valuable foundation for Quants who need to understand and apply regression models in their work.

See salaries and explore the career path for Quantitative Analyst

Data Analyst

Data Analysts collect, clean, and analyze data to help businesses make informed decisions. They use statistical techniques to identify trends and patterns in data. Regression Modeling in Practice provides a comprehensive overview of regression analysis, a statistical technique used to predict outcomes based on one or more independent variables. This course provides a strong foundation for Data Analysts who need to use regression models in their work.

See salaries and explore the career path for Data Analyst

Financial Analyst

Financial Analysts use financial data to make investment recommendations and assess the financial health of companies. They use statistical techniques to analyze financial data and make predictions. Regression Modeling in Practice provides a strong foundation for Financial Analysts who need to use regression models in their work.

See salaries and explore the career path for Financial Analyst

Business Analyst

Business Analysts use data to identify problems and opportunities within businesses. They develop and implement solutions to improve efficiency and profitability. Regression Modeling in Practice teaches the basics of regression analysis, a technique used to predict outcomes based on one or more independent variables. This course provides a valuable foundation for Business Analysts who need to use regression models in their work.

See salaries and explore the career path for Business Analyst

Market Researcher

Market Researchers gather and analyze data about consumer behavior and market trends. They use this information to help businesses make informed decisions about product development, marketing, and sales. Regression Modeling in Practice provides a strong foundation for Market Researchers who need to use regression models to analyze market data and make predictions.

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Consultant

Consultants provide expert advice to businesses on a variety of topics, including strategy, operations, and marketing. They use data to analyze problems and develop solutions. Regression Modeling in Practice provides a valuable foundation for Consultants who need to use regression models in their work.

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Actuary

Actuaries use mathematics and statistics to assess risk and uncertainty. They develop and implement solutions to mitigate risk and protect financial interests. Regression Modeling in Practice provides a valuable foundation for Actuaries who need to use regression models in their work.

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Economist

Economists study the production, distribution, and consumption of goods and services. They use data to analyze economic trends and make predictions. Regression Modeling in Practice provides a strong foundation for Economists who need to use regression models in their research or work.

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Epidemiologist

Epidemiologists study the distribution and determinants of disease in populations. They use data to identify risk factors for disease and develop strategies to prevent and control outbreaks. Regression Modeling in Practice may be useful for Epidemiologists who need to use regression models in their research.

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Biostatistician

Biostatisticians apply statistical methods to solve problems in biology and medicine. They use data to design and analyze clinical trials, evaluate the effectiveness of new treatments, and identify risk factors for disease. Regression Modeling in Practice may be useful for Biostatisticians who need to use regression models in their research.

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Software Engineer

Software Engineers design, develop, and maintain software applications. They use data to identify problems and develop solutions. Regression Modeling in Practice may be useful for Software Engineers who need to use regression models in their work, particularly in the area of predictive analytics.

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Operations Research Analyst

Operations Research Analysts use mathematical and statistical techniques to solve problems in business and industry. They develop and implement solutions to improve efficiency and profitability. Regression Modeling in Practice may be useful for Operations Research Analysts who need to use regression models in their work.

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Teacher

Teachers plan and deliver instruction to students in a variety of settings. They use data to assess student learning and develop effective teaching strategies. Regression Modeling in Practice may be useful for Teachers who want to use regression models to analyze student data and improve their teaching.

See salaries and explore the career path for Teacher