T3U12 Residual Plots Honors

A linear regression was fit to a set of data. Which residual plot shows that the model was a GOOD fit?

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Which residual plot shows a linear model that was a good fit for its data set?

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The residual plot for a linear model is shown in the image.

Which statement is true?

• The linear model is a good fit because the residual plot shows random scatter

• The linear model is a good fit because the residual plot shows non-random scatter

• The linear model is not a good fit because the residual plot shows non-random scatter

• The linear model is not a good fit because the residual plot shows random scatter

After performing analyses on a set of data, Mrs. Schoeneck examined the scatter plot of the residual values for each analysis. Which scatter plot indicated the best linear fit for the data?

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A linear model was used for a data set and produced the residual plot shown. Based on the residual plot, which statement is true about the model?

• The model is likely a good fit because the plot shows a random pattern

• The model is likely not a good fit because the plot shows a random pattern

• The model is likely not a good fit because the plot shows a non-random pattern

• The model is likely a good fit because the plot shows a non-random pattern

Answer explanation

The residual plot of a linear scatter plot will have RANDOM scatter.

They do NOT have any patterns or clusters of points

A residual plot compares the residual values (dependent variable) to the predicted values. Which of the following residual plots indicate that the regression model is a "good fit" to the data?

Select two residual plots.

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Answer explanation

The residual plot of a linear scatter plot will have RANDOM scatter.

They do NOT have any patterns or clusters of points

After performing a linear regression on a set of data, Jackie examined the residual plots. Which residual plot indicates that the linear model was a good fit?

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Answer explanation

GOOD residual plots have RANDOM scatter.

They do NOT have any patterns or clusters of points

Which correlation coefficient indicates the strongest relationship between the two variables in the data set?

Which of these scenarios most likely suggests causation?

• The number of social media posts made and the final grade in a class

• The time the car radio has been playing and the amount of gas left in the car

• The number of friends on social media and the number of friends in real life

• An increase in the width of the sides of a rectangle and an increase in the area of the rectangle

Which residual plots would indicate that the regression model used was a BAD fit?

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