Lines of Best, Correlation Coefficients and Residuals

How do you calculate a residual?

• Actual value - Predicted value

• Predicted value - Actual value

• Actual value + Predicted value

• Predicted value + Actual value

Tags

CCSS.HSF-LE.A.1B

Which phrase best describes the relationship between x and y?

• strong negative correlation

• strong positive correlation

• weak negative correlation

• weak positive correlation

If a linear model is applied to these data, which statement best describes the correlation coefficient?

• It is close to −1.

• It is close to 1.

• It is close to 0.

• It is close to 0.5.

A positive residual means...

• that the predicted value is too big

• that the predicted value is too small

• that the x value is too big

• that the x value is too small

Tags

CCSS.HSF.LE.B.5

The equation of the least squares regression 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

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

Tags

CCSS.8.EE.C.8C

The linear regression equation is y = 61.93x - 1.79.  Use the equation to predict how far this person will travel after 10 hours of driving.

• 10 miles

• 617.5 miles

• 0.19 miles

• 500 miles

The linear regression equation is y = 61.93x - 1.79.  According to the model, the slope can be interpreted as:

• After 0 hours of driving, they went 61.93 miles.

• They drove at a speed of 61.93 miles per hour.

• They drove 61.93 miles total.

• They drove 61.93 hours total.

Tags

CCSS.HSF.LE.B.5

A restaurant sells pizza for the prices in the data table.  Calculate the linear regression equation of the data.

• y = 1.5x + 12

• y = 12x + 1.5

• y = 0.67x - 8

• y = -8x + 0.67

Tags

CCSS.8.EE.C.8C

Find the equation of the line of best fit for the given data

• y = 4.19 + 163.41x

• y = 4.19 - 163.41x

• y =163.41 - 4.19x

• y = -163.41+ 4.19x