In this course, you’ll learn how and when to use linear regression models to make predictions. You’ll learn how to build linear regression models, how to interpret their output, and how to assess model accuracy. You’ll also explore the limitations of linear regression models when data isn’t linear. Finally, you’ll learn to use programming tools to fit and visualize many linear regression models at once.
Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser. At the end of the course, you’ll complete a project to practice your skills with a subset of condominium sales data from all five boroughs of New York City.
- Processing numerical and text data
- Interpreting linear regression model outputs
- Assessing model fit and accuracy
Linear Regression Modeling in R [6 lessons]
- Define correlation
- Identify relationships using scatterplots
- Select variables for linear regression
- Define coefficients
- Fit a linear regression model
- Interpret linear regression model outputs
- Expand your portfolio with a guided bivariate linear regression model project
- Overcome errors in the dataset that can influence modeling results
Projects in this course
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