Interpreting Scatter Plots and Predicting Unobserved Data

This lesson teaches how to interpret scatter plots by identifying the line of best fit. It explains the impact of outliers, demonstrates positive and negative associations using examples, and shows how to draw the line of best fit to make predictions. The lesson emphasizes the importance of the line of best fit in modeling relationships and predicting unobserved data.

What is a common mistake when identifying the line of best fit?

• Ignoring the slope of the line

• Allowing outliers to overly influence the line

• Using only the x-axis values

• Drawing the line through all data points

What does a positive association in a scatter plot indicate?

• The variables are unrelated

• As one variable increases, the other also increases

• There is no relationship between the variables

• As one variable increases, the other decreases

What is the main purpose of creating scatter plots?

• To make predictions about unobserved data

• To find the exact value of each data point

• To eliminate outliers

• To determine the average of the data

How can the line of best fit be used in a scatter plot with a negative association?

• To predict higher values for both variables

• To predict lower values for both variables

• To predict a decrease in one variable as the other increases

• To predict an increase in one variable as the other increases

What is another name for the line of best fit?

• Outlier line

• Data trend line

• Linear model

• Scatter line