## 4.1 Building Regression Models

Leverage the power of correlation and regression to understand relationships and make predictions with data.

Correlation

Practice Correlation

Correlation Extremes

Practice Correlation Extremes

Simple Linear Regression

Practice Simple Linear Regression

Calculating Error

Practice Calculating Error

Regression and Prediction

Nonlinear Relationships

Simpson's Paradox

### Course description

This course introduces correlation and regression, which are used to quantify the strength of the relationship between variables and to compute the slope and intercept of the regression line. It explores two applications of these methods, using correlated measurements to make informed guesses for measurements that are not available, and making predictions for future events. Datasets used in these lessons include weights and other measurements from penguins and a time series of annual average temperatures. Later lesson explore nonlinear relationships and Simpson's paradox.

### Topics covered

- Correlation
- Regression
- Mean squared error
- Mean absolute error

### Prerequisites and next steps

Exploring Data Visually Introduction to Probability

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### 4.2 Case Study: Maximizing Electric Car Value

This is the Case Study for Building Regression Models. In it, you will use correlation and regression to dive into data on electric vehicles to discover what a buyer gets for their money.

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