linear regression

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

Tags

CCSS.8.EE.C.8C

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

• 5,000
• 2,620
• 17,000
• 20,200

Tags

CCSS.HSF-LE.A.1B

The scatter plot shows the relationship between the number of chapters and the total number of pages for several books.  Use the trend line to predict how many chapters would be in a book with 180 pages. 

• 12 chapters
• 15 chapters
• 18 chapters
• 21 chapters

Tags

CCSS.HSA.CED.A.2

CCSS.HSS.ID.B.6

CCSS.HSS.ID.C.7

If Joe worked 30 hours, how much is he expected to earn?

• $600
• $200
• $1000
• $800

Tags

CCSS.HSF-LE.A.1B

Which of the following pairs of variables would be expected to have a negative association?

• GPA and height

• Number of chores and weekly allowance

• Number of hours spent studying and test grade

• Number of miles driven and amount of gas remaining in gas tank

• Age and IQ

Tags

CCSS.HSS.ID.B.6

CCSS.HSS.ID.C.9

Meg has a set of bivariate numerical data with a correlation coefficient of r=0.83. Elise has a data set with a correlation coefficient of r=-0.83. What can you conclude about the two sets of data?

• The scatterplots for these two data sets both display a strong linear pattern.

• In Meg’s data, 83% of the data points are closely related.

• In Elise’s data, 83% of the variability in y can be explained by the linear association with x.

• Meg’s data is more linear than Elise’s data.

• Nothing can be concluded about the two data sets without looking scatterplots of the data.

Tags

CCSS.HSF.LE.B.5

Two variables are exponentially related. The least squares regression lines for transformed data is found to be ln y=-0.041+0.0035x. What is the predicted value for y when x=16.83?

• 2.49

• 1.018

• 12.98

• 16.74

• 54.15

Tags

CCSS.HSF.LE.A.2

Is this a positive, negative, or no relationship?

• positive relationship

• negative relationship

• no relationship

Tags

CCSS.HSS.ID.B.6

CCSS.HSS.ID.C.8