Regression
Find lines of best fit and make predictions from data
lessons (8)
Introduction to Regression
Learn how to find the best-fitting line through data points and make predictions using linear regression.
Linear Regression
Learn how to find the line of best fit for data and use it to make predictions.
Scatter Plots
Learn how to make a scatter plot from a table and describe the pattern: form, direction, strength, clusters, gaps and outliers.
Line of Best Fit
Learn to draw a line of best fit by eye on a scatter plot, find its equation from two points and explain what its slope and intercept mean.
Making Predictions from Data
Use a regression line to predict values, work backwards to a target, and judge when a prediction can be trusted.
Residuals
Learn what a residual is, how to compute residuals in a table, and how a residual plot shows whether a line fits the data.
Coefficient of Determination (R²)
Learn what R² tells you about a regression line, how to calculate it from r or from sums of squares, and why a high R² does not prove causation.
Regression Word Problems
Solve complete regression problems: interpret slope and intercept in context, predict, spot extrapolation, compute residuals and read r and R².
Regression analysis finds relationships between variables. Linear regression fits a line to data points, allowing predictions. Understanding regression helps in science, economics, and data analysis.
What Students Will Learn
- Calculate the line of best fit
- Interpret slope and y-intercept
- Make predictions using regression
- Evaluate model fit with r-squared
- Understand residuals
Frequently Asked Questions
What is the line of best fit?
The line that minimizes the sum of squared distances from each point to the line. Also called the regression line.
What does r-squared tell us?
R-squared (0 to 1) shows what fraction of variation in y is explained by x. Higher is better fit.
What are residuals?
The difference between actual y-values and predicted y-values. We want residuals to be small and random.