Residuals Day 1

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

Tags

CCSS.HSS.ID.B.6

CCSS.HSS.ID.C.8

How do you calculate a residual?

• Observed - Predicted
• Predicted - Observed

Residuals are . . . 

• possible models not explored by the researcher
• variation in the response variable that is explained by the model
• the difference between the observed response and the values predicted by the model
• data collected from individuals that is not consistent with the rest of the group

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

• YES
• NO

Tags

CCSS.HSS.ID.A.3

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

• YES
• NO

Tags

CCSS.HSS.ID.B.6

Estimate the correlation coefficient.

• r = -0.8
• r = -0.5
• r = 0.5
• r = 0.8

Tags

CCSS.HSS.ID.B.6

CCSS.HSS.ID.C.8

Estimate the correlation coefficient for this scatterplot.

• r = 1.2
• r = 0.89
• r = 0
• r = -0.89

Tags

CCSS.HSS.ID.B.6

CCSS.HSS.ID.C.8

The correlation coefficient r is a value between

• 0 and 1
• 0 and 100
• -1 and 1
• -2 and 2

Tags

CCSS.HSS.ID.C.8

What is another name for the line of best fit?

Hint: There may be more than one answer.

• Linear regression

• Trend line

• Prediction line

• Fun line

Tags

CCSS.8.EE.B.5

Which indicates a causal relationship?

• A rooster crowing and the sun rising
• Driving more miles and using more gas
• Eating salty food and buying a drink
• Going to the beach and building a sand castle