Data Analysis · Level 4

4.1 Explaining Variation

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

Simple Linear Regression Practice 1

Simple Linear Regression Practice 2

Least Squares

Practice Least Squares

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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Data Analysis · Level 4

4.2 Case Study: Maximizing Electric Car Value

This is the Case Study for Explaining Variation. 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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