Linear Regression Test F18

Describe the correlation in the graph shown.

• Strong Negative
• Strong Positive
• Weak Negative
• Weak Positive

Tags

CCSS.HSF-LE.A.1B

Describe the correlation in the graph shown.

• Strong Negative
• Strong Positive
• Weak Negative
• No Correlation

Tags

CCSS.HSF-LE.A.1B

Describe the correlation in the graph shown.

• Strong Negative
• Strong Positive
• Weak Negative
• Weak Positive

Tags

CCSS.HSF-LE.A.1B

The table shown is comparing a person's foot length to their height in cm. Calculate a line of best fit.

• f(x) = 25.05x + 5.57
• f(x) = 5.57x + 25.05
• f(x) = -5.57x + 25.05
• f(x) = 5.57x - 25.05

Tags

CCSS.HSA.CED.A.2

CCSS.HSS.ID.B.6

CCSS.HSS.ID.C.7

The table shown is comparing a person's foot length to their height in cm. What is the relationship between foot length and height?

• strong; r = .99
• strong; r = .97
• weak; r = .38
• weak; r = .58

Tags

CCSS.HSS.ID.B.6

CCSS.HSS.ID.C.8

The linear regression equation for the data is  y = 1.5x + 12.  Interpret the slope of the equation.

• The slope is (0, 1.5).  It means that a pizza with 0 toppings will cost $1.50.
• The slope is 1.5.  It means that it costs $1.50 per topping.
• There are 1.5 toppings.
• There are 1.5 pizzas.

Tags

CCSS.HSF.LE.B.5

The linear regression equation for the data is  y = 1.5x + 12.  Use the equation to predict the cost of a pizza that has 8 toppings.

• $8
• $12
• $24
• $28

Tags

CCSS.8.EE.C.8C

Shown is a residual plot. Would a linear regression model of the data be most appropriate?

• YES
• NO

How do you calculate a residual?

• Observed - Predicted
• Predicted - Observed
• find r²  in your TI-84
• find r in your Ti-84

The equation of the best-fit line is
y(hat) = 1.3 + 0.73x.  What is the residual for the point (4, 7)?

• 2.78
• 3.00
• 4.00
• 4.22

Tags

CCSS.8.EE.C.8C