Course overview
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.
Key skills
- Processing numerical and text data
- Interpreting linear regression model outputs
- Assessing model fit and accuracy
Course outline
Linear Regression Modeling in R [6 lessons]
Introduction to Modeling 1h
Lesson Objectives- Define predictive modeling
- Define prediction and inference
- Define errors and residuals
Bivariate Relationships - Correlation and Scatterplots 1h
Lesson Objectives- Define correlation
- Identify relationships using scatterplots
- Select variables for linear regression
Estimating the Coefficients and Fitting Linear Models 1h
Lesson Objectives- Define coefficients
- Fit a linear regression model
- Interpret linear regression model outputs
Assessing the Accuracy of the Model 1h
Lesson Objectives- Assess the quality of coefficient estimates
- Compute linear regression model quality of fit
- Interpret linear regression model quality of fit
Fitting Many Linear Models 1h
Lesson Objectives- Generate tidy linear models with the broom package
- Fit linear regression models
- Visualize linear regression models.
Guided Project: Predicting Condominium Sale Prices 1h
Lesson Objectives- 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
Predicting Condominium Sale Prices
For this project, you’ll assume the role of a data analyst to predict condominium sale prices in New York City boroughs based on property size, using linear regression modeling in R.
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