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Regression Modeling in Practice

This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you.

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What's inside

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.

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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

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Reviews summary

Practical regression modeling overview

According to learners, this course provides a solid foundation in core regression concepts, covering linear, multiple, and logistic models. Students find value in the focus on practical application, appreciating the opportunity to use either SAS or Python for hands-on work. While many feel the course builds useful skills, some note that it can be challenging and may require some prior statistical background. Technical setup for the software is also mentioned as a potential hurdle. Overall, it's considered a valuable course for understanding and applying regression.

Uses SAS or Python

"I chose the Python track and found the material relevant."

"It's good to have the option of learning with either SAS or Python."

"The course supports both major tools, which is a definite plus."

"I appreciated being able to work in the environment I preferred (SAS)."

Emphasizes real-world application

"I could apply the concepts immediately to my work, which was great."

"The exercises were practical and really helped reinforce the learning."

"Helped me understand how to use regression in real data analysis scenarios."

"The hands-on parts using the software made it very practical."

Covers fundamental regression types

"I feel this course gave me a solid foundation in linear and multiple regression."

"The explanation of logistic regression was clear and easy to grasp."

"It covered the basic concepts I needed to know about regression analysis."

"Gave me a good handle on interpreting different regression models."

Setting up software can be difficult

"Getting SAS OnDemand set up was frustrating for me initially."

"I had some trouble with the Python environment installation steps."

"Initial technical hurdles before starting the course material were challenging."

"Wish the instructions for software setup were a bit clearer."

Beneficial with stats background

"I struggled without a strong statistics foundation going in."

"Learners should definitely have some prior stats knowledge; it moves quickly."

"Felt like it assumed more background than I initially thought."

"Might be challenging for complete beginners to 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 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:

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

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

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

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.

See salaries and explore the career path for Market Researcher

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

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

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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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.

See salaries and explore the career path for Economist

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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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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Operations Research Analyst

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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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

Reading list

We've selected 20 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 in Practice.

Regression Modeling Strategies

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Comprehensive guide to regression modeling. It covers all aspects of regression analysis, from simple linear regression to complex nonlinear models. It valuable resource for anyone who wants to learn more about regression analysis or who needs to use it in their work.

Regression Modeling Strategies: With Applications...

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Regression Modeling Strategies: With Applications...

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Regression Modeling in Practice

What's inside

Syllabus

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Save this course

Reviews summary

Practical regression modeling overview

Activities

Career center

Reading list

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