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 key characteristic of a line of best fit?

• It always passes through the origin.

• It reflects the pattern of the data.

• It is always horizontal.

• It only applies to univariate data.

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

• Find nine on the y-axis and track horizontally.

• Find nine on the x-axis and track vertically to the line of best fit.

• Use the average weight of all data points.

• Find the midpoint of the scatterplot.

If a baby weighs 12 pounds, how old can you predict the baby to be?

• One month old

• Nine months old

• Three months old

• Six months old

What is the estimated study time for a student to achieve at least 65%?

• 10 minutes

• 25 minutes

• 5 minutes

• 17 minutes

If a student misses 10 days of school, what grade can they expect?

• About 90%

• About 70%

• About 38%

• About 50%

Why might predictions from a scatterplot with a strong association be more accurate?

• The data points are close to the line of best fit.

• The data points are far from the line of best fit.

• The line of best fit is always horizontal.

• The scatterplot has fewer data points.

What is a common misunderstanding about predictions using a line of best fit?

• Predictions are only for positive associations.

• Predictions are exact values.

• Predictions are always above 50%.

• Predictions are estimates.