Regression
Leverage the power of correlation and regression to understand relationships and make predictions with data.
Correlation
Correlation Extremes
Simple Linear Regression
Calculating Error
Regression and Prediction
Nonlinear Relationships
Simpson's Paradox
Use Multiple Variables
Understand Coefficients
Optimize Coefficients
Assess the Model
Choose Variables
Avoid Overfitting
Interpret the Model
Use Categorical Variables
Explore Interactions
Transform Variables
EV Trends
Exploring EV Correlations
Exploring Data Subpopulations
Battery Electric Vehicles
Plug-in Hybrid Electric Vehicles
Changing the Shape of the Data
Up next
Clustering & Classification
Detect and predict groups in your data to understand differences and make predictions.
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