CW/HW: 2-variable stats review

• This residual graph represents a linear fit.
• This graph represents a nonlinear fit.
• This residual graph represents no fit.

• This residual plot represents a linear fit.
• This residual plot represents a nonlinear fit.
• This residual plot represents no fit.

What should a residual plot look like?

• Distinct Pattern
• Curve
• Random

Tags

CCSS.HSS.ID.B.6B

As study time _______________, errors _______________.

• increases ; decrease
• increases ; increase
• decreases; decrease
• increases ; stays the same

Tags

CCSS.HSS.ID.B.5

Shown is a residual plot. How many positive residuals are there?

• 5

• 1

• 3

• 9

Tags

CCSS.HSS.ID.B.6B

What is the correlation?

• Negative correlation

• No correlation

• Positive correlation

Tags

CCSS.HSS.ID.B.5

Which is TRUE?
I.  Random scatter in the residuals indicates a linear model.
II.  If two variables are very strongly associated, then the correlation between them will be near 1 or -1.
III.  Changing the units of measurement for x or y changes the correlation coefficient.

• I only
• II only
• I and II only
• I, II and III

Tags

CCSS.HSS.ID.B.6B

Estimate the correlation coefficient for this scatterplot.

• r = 0.56

• r = -0.56

• r = 0.92

• r = -0.92

Tags

CCSS.HSS.ID.C.8

Which of the following statements are supported by the scatterplot?
I.   There is a positive association between height and volume.
II.  There is an outlier in the plot. 
III.  As the height of a cherry tree increases, the volume of useable lumber it yields increases.

• I only
• II only
• I and II only
• I, II, and III

Tags

CCSS.8.SP.A.1

A person travels by car.  They record their miles driven in a data table.  Calculate the linear regression equation of this data.

• y = 61.93x - 1.79
• y = -1.79x + 61.93
• y = 0.016x + 0.03
• y = 0.03x + 0.016