Residuals Mini-Quiz 10th

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

Tags

CCSS.HSS.ID.B.6B

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

Select two residual plots.

• 
• 
• 
• 
• 

Answer explanation

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

They do NOT have any patterns or clusters of points

Tags

CCSS.HSS.ID.B.6B

The scatterplot shows the relationship between two variables, x and y.

Which description best describes the relationship shown in the scatter plot?

• The relationship is linear with a positive correlation

• The relationship is non-linear with a positive correlation

• The relationship is linear with a negative correlation

• The relationship is non-linear with a negative correlation

Tags

CCSS.8.F.A.3

The table below shows the relationship between total fat grams and total calories in a selection of fast food sandwiches.

Using the set of data given above, a linear regression model was calculated, where y represents the amount of Calories and x represents total fat (in grams)

y = 13.1x + 145.17

What is true when x = 30?

Tags

CCSS.HSS.ID.B.6B

The table below shows the relationship between total fat grams and total calories in a selection of fast food sandwiches.

Using the set of data given above, a linear regression model was calculated, where y represents the amount of calories and x represents total fat (in grams)

y = 13.1x + 145.17

When x = 45, what is the predicted value? Choose the option that is rounded to the nearest hundredth correctly

• 734.67

• 735

• 734.7

• 734.66

• 734.68

Tags

CCSS.HSF.LE.B.5

The table below shows the relationship between total fat grams and total calories in a selection of fast food sandwiches.

Using the set of data given above, a linear regression model was calculated, where y represents the amount of calories and x represents total fat (in grams)

y = 13.1x + 145.17

When x = 31, the _______ value is 560

• observed

• predicted

• residual

• partial

Tags

CCSS.HSS.ID.B.6B

The table below shows the relationship between total fat grams and total calories in a selection of fast food sandwiches.

Using the set of data given above, a linear regression model was calculated, where y represents the amount of calories and x represents total fat (in grams)

y = 13.1x + 145.17

What is the residual when x = 21?

• -0.27

• 0.27

• 420

• 420.27

Tags

CCSS.HSS.ID.B.6B

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

Tags

CCSS.HSS.ID.B.5

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

Tags

CCSS.HSS.ID.C.9

An experiment was conducted to examine the relationship between two variables. The correlation coefficient r was determined to be -0.9442.

Which statement best describes the relationship between the two variables in the experiment?

• Strong negative correlation; one can conclude causation

• Weak negative correlation; one can conclude causation

• Weak negative correlation; one can not conclude causation

• Strong negative correlation; one can not conclude causation

Tags

CCSS.HSS.ID.C.9