Quantifying Relationships with Regression Models from Coursera

What's inside

Syllabus

Regression Models: What They Are and Why We Need Them

While graphs are useful for visualizing relationships, they don't provide precise measures of the relationships between variables. Suppose you want to determine how an outcome of interest is expected to change if we change a related variable. We need more than just a scatter plot to answer this question. What should you do, for example, if you want to calculate whether air quality changes when vehicle emissions decline? Or if you want to calculate how consumer purchasing behavior changes if a new tax policy is implemented? To calculate these predicted effects, we can use a regression model. This module will first introduce correlation as an initial means of measuring the relationship between two variables. The module will then discuss prediction error as a framework for evaluating the accuracy of estimates. Finally, the module will introduce the linear regression model, which is a powerful tool we can use to develop precise measures of how variables are related to each other.

Fitting and Evaluating a Bivariate Regression Model

Now that you've got a handle on the basics of regression analysis, the next step is to consider how to evaluate and modify a basic regression model. This module will introduce you to a common measure of model fit and the three core assumptions of regression analysis. In addition, we'll explore the special circumstance of conducting a regression analysis with a binary (AKA dummy) treatment variable. Dummy variables, which take on two values, are frequently used in statistics. Understanding how to use and interpret dummy variables provides a foundation for developing a multivariate regression model, which we'll get to in the next module.

Multivariate Regression Models

The bivariate regression model is an essential building block of statistics, but it is usually insufficient in practice as a useful model for descriptive, causal or predictive inference. This is because there are usually multiple variables that impact a particular dynamic. Whether you are modeling political behavior, environmental processes or drug treatment outcomes, it is almost always necessary to account for multiple influences on an outcome of interest. This module will introduce the multivariate model of regression analysis and explain the appropriate ways to interpret and evaluate the results from a multivariate analysis.

Extensions of the Multivariate Model

Once you've mastered the OLS multivariate model, you're ready to learn about a wide array of regression modeling techniques. Remember, researchers should always employ modeling tools that best enable them to answer the question at hand. This module will focus on two tools in particular, interaction terms and models for binary dependent variables. Keep in mind, however, that there are numerous regression modeling tools that you can learn and implement based on the research question you're trying to answer. After you've developed a solid understanding of regression basics, you should feel capable of expanding this knowledge base as you move forward as a producer and consumer of analytics.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

An excellent course for those with little to no statistics experience

Teaches the fundamental concepts using a linear regression tool

Taught by an expert in the field, which lends credibility

Ideal for those seeking to deepen their understanding in regression analysis

Provides a solid foundation for further exploration

May require learners to have basic statistics knowledge

Reviews summary

Quantifying relationships with regression models

According to students, this is a solid course on regression models, particularly for learners new to regression. Students say that the material is simple and concise and that the course includes some engaging assignments. However, some students expressed frustration that the course requires extensive reading and writing outside of its direct instruction.

Course offers interesting and relevant assignments that help reinforce learning.

"Very helpful and clear!"

"Great, i´ve learn some new things about the logit and probit model"

Course provides a strong introduction to regression models for those new to the topic.

"Great refresher on regression models."

"this is nice course for people who need regression but do not want to do Math."

"Recommended for those who pose a challenge, not being a professional in the field or having previously studied it."

Course demands extensive reading and writing, which may be challenging for some learners.

"It demands in the questionnaires and in the writing in English, more than can be learned by direct action of the course."

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 Quantifying Relationships with Regression Models with these activities:

Revise scatter plots and correlation

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Review the basics of scatter plots and correlation to ensure a strong foundation for understanding regression analysis.

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Review lecture notes or textbook chapters on scatter plots and correlation.
Practice creating scatter plots using real-world data.
Calculate and interpret correlation coefficients.

Organize and review course materials

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Establish a structured approach to managing and reviewing course materials, ensuring effective knowledge retention and recall.

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Gather all course materials, including lecture notes, readings, and assignments.
Organize materials into logical categories or folders.
Review materials regularly to reinforce understanding.

Read 'Regression Analysis by Example'

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Review a comprehensive textbook on regression analysis to reinforce the concepts covered in the course and gain a deeper understanding.

View Regression Analysis By Example Using R (Wiley... on Amazon

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Read through the chapters covering basic to advanced topics in regression analysis.
Work through the examples and practice problems provided in the book.
Summarize key concepts and techniques to enhance retention.

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Solve regression problems

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Engage in problem-solving exercises to reinforce the concepts of regression analysis, such as fitting regression lines, making predictions, and evaluating model performance.

Browse courses on Regression Analysis

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Work through practice problems provided in the course materials.
Find additional regression practice problems online or in textbooks.
Use statistical software to solve regression problems.

Provide guidance to fellow learners

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Engage with fellow learners by providing support and guidance, fostering a collaborative and supportive learning environment.

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Identify opportunities to provide assistance to others.
Offer support and guidance on course-related topics.
Engage in discussions and provide feedback to fellow learners.

Develop a data visualization

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Create a visual representation of a regression model to enhance understanding and effectively communicate results to stakeholders.

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Choose an appropriate data visualization technique for the regression model.
Use statistical software or online tools to create the visualization.
Interpret the visualization and identify key insights.
Present the visualization to peers or instructors for feedback.

Explore advanced regression techniques

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Enhance understanding of regression analysis by exploring advanced techniques, extending beyond the scope of the course.

Browse courses on Regression Analysis

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Identify advanced regression techniques relevant to research interests.
Locate online tutorials or courses covering these techniques.
Follow the tutorials to gain practical knowledge and skills.
Apply the techniques to real-world datasets for experimentation.

Conduct a regression analysis on a real-world dataset

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Apply regression techniques to analyze real-world data, providing valuable hands-on experience in model building, interpretation, and communication.

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Identify a research question and collect relevant data.
Clean and prepare the data for analysis.
Fit a regression model to the data.
Interpret the results and draw conclusions.
Write a report or presentation to communicate the findings.

Career center

Learners who complete Quantifying Relationships with Regression Models will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use data to solve business problems. They work with data to identify trends and patterns, and to develop models to predict future outcomes. The Quantifying Relationships with Regression Models course provides a strong foundation in statistical analysis and regression modeling, which are essential skills for Data Scientists.

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Statistician

Statisticians collect, analyze, interpret, and present data to help businesses, organizations, and governments make informed decisions. They use statistical techniques to identify trends and patterns in data, and to develop models to predict future outcomes. The Quantifying Relationships with Regression Models course provides a strong foundation in statistical analysis, including regression modeling.

See salaries and explore the career path for Statistician

Data Analyst

Data Analysts collect, interpret, and present data to help businesses make informed decisions. They use statistical techniques to analyze data and identify trends and patterns. The Quantifying Relationships with Regression Models course provides a foundation in statistical analysis, including regression modeling. This course can help you develop the skills needed to become a successful Data Analyst.

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Biostatistician

Biostatisticians use statistical methods to analyze data in the field of biology and medicine. They work with data to design and analyze clinical trials, and to identify risk factors for disease. The Quantifying Relationships with Regression Models course provides a strong foundation in statistical analysis, including regression modeling, which is a key tool used by Biostatisticians.

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Actuary

Actuaries use mathematical and statistical models to assess risk and uncertainty. They work with data to develop insurance policies and other financial products. The Quantifying Relationships with Regression Models course provides a strong foundation in statistical analysis, including regression modeling, which is a key tool used by Actuaries.

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Risk Manager

Risk Managers identify, assess, and mitigate risks. They use statistical techniques to analyze data and develop risk management plans. The Quantifying Relationships with Regression Models course provides a strong foundation in statistical analysis, including regression modeling, which is a key tool used by Risk Managers.

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Epidemiologist

Epidemiologists study the distribution and causes of disease. They use statistical techniques to analyze data and identify risk factors for disease. The Quantifying Relationships with Regression Models course provides a strong foundation in statistical analysis, including regression modeling, which is a key tool used by Epidemiologists.

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Economist

Economists study the production, distribution, and consumption of goods and services. They use statistical techniques to analyze data and identify economic trends. The Quantifying Relationships with Regression Models course provides a strong foundation in statistical analysis, including regression modeling, which is a key tool used by Economists.

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Market Researcher

Market Researchers collect, analyze, and interpret data to understand consumer behavior and market trends. They use this information to help businesses make informed decisions about marketing strategies and product development. The Quantifying Relationships with Regression Models course provides a strong foundation in statistical analysis, including regression modeling, which is a key tool used by Market Researchers.

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Survey Researcher

Survey Researchers design, conduct, and analyze surveys. They use statistical techniques to collect and analyze data from surveys. The Quantifying Relationships with Regression Models course provides a strong foundation in statistical analysis, including regression modeling, which is a key tool used by Survey Researchers.

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Psychometrician

Psychometricians use statistical methods to develop and evaluate psychological tests. They work with data to identify psychological traits and disorders. The Quantifying Relationships with Regression Models course provides a strong foundation in statistical analysis, including regression modeling, which is a key tool used by Psychometricians.

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Validation Analyst

Validation Analysts ensure that software and other systems meet the requirements of the business. They use statistical techniques to analyze data and identify any errors or defects. The Quantifying Relationships with Regression Models course provides a strong foundation in statistical analysis, including regression modeling, which is a key tool used by Validation Analysts.

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Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to help businesses improve their operations. They work with data to identify inefficiencies and develop solutions to improve productivity and efficiency. The Quantifying Relationships with Regression Models course provides a strong foundation in statistical analysis, including regression modeling, which is a key tool used by Operations Research Analysts.

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Financial Analyst

Financial Analysts evaluate the financial performance of companies and make recommendations to investors. They use statistical techniques to analyze data and identify investment opportunities. The Quantifying Relationships with Regression Models course provides a strong foundation in statistical analysis, including regression modeling, which is a key tool used by Financial Analysts.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. They play a critical role in the financial industry, helping to manage risk and maximize returns. The Quantifying Relationships with Regression Models course provides a strong foundation in regression analysis, which is a key tool used by Quantitative Analysts.

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