Linear Regression and Making Predictions

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

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

If x is temperature and y is coffee sales, use the following formula to determine the coffee sales on a 34° day.

y = -60x+6443

• $4403

• $4673

• $8483

• $6443

Tags

CCSS.HSF.LE.B.5

What letter do we use to represent correlation coefficient?

• c
• r
• p
• h

The chart below tracts starting salaries for health care workers since the year 2000. Using linear regression, find the equation of best fit. Round decimals to 3 places.

• y = 839.721x + 27541.812
• y = 27541.812x - 839.721
• y = 27541.812x + 839.721
• y = 839.721x - 27541.812

What is a best-fit line used for?

• To make predictions
• To make the graph look pretty
• To connect the points
• To make it look like you know what you are doing

Tags

CCSS.8.EE.B.5

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

• y = 163.41x+1.49

• y =- 163.41x+ 4.19

• y =- 4.19x+163.41

• y = 4.19x -163.41

Write the linear regression equation for the following data points. Round to the nearest tenth.

• $y=-0.83x+0.61$

• $y=-0.83x+0.63$

• $y=0.83x+-0.8$

• $a^2+b^2=c^2$

Tags

CCSS.8.EE.C.8C