Making Predictions from Bivariate Measurement Data with a Line of Best Fit

This lesson teaches how to make predictions from bivariate measurement data by interpreting a line of best fit. It covers the concepts of positive and negative associations, as well as strong and weak associations. Through examples, it demonstrates predicting a baby's weight, estimating study time for grades, and predicting grades based on school absences. The lesson emphasizes that predictions are estimates, not exact answers, and explains the impact of data point associations on prediction accuracy.

What is a line of best fit and what does it represent in a scatterplot?

How can you predict a baby's weight at nine months using a scatterplot?

Explain how to estimate the study time needed for a student to achieve at least a 65%.

What is a common misunderstanding when making predictions from data?

What prediction can be made if a student studies for 15 minutes?

What does it mean if a line of best fit has a strong association?

How does the association strength affect the accuracy of predictions in scatterplots?