Linear Regression

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

The table shown is comparing a person's foot length to their height in cm. Calculate a line of best fit.

• f(x) = 25.05x + 5.57
• f(x) = 5.57x + 25.05
• f(x) = -5.57x + 25.05
• f(x) = 5.57x - 25.05

What is the y-intercept in the following equation:  y= -5 - 4x

• -4
• -4x
• 5
• -5

Tags

CCSS.8.EE.B.6

CCSS.8.F.A.3

What is the slope in the following equation:  y=6 - 2/3x

• 2/3
• 6
• -6
• -2/3

Tags

CCSS.8.EE.B.6

CCSS.8.F.A.3

What does "b" represent in y=a + bx?

• y-intercept
• slope
• macaroni
• nothing

Tags

CCSS.8.EE.B.6

CCSS.8.F.A.3

What is the line of best fit? (Ignore the x = 30, y = ? point.)

• f(x) = 1.82 + 5x
• cannot be determined
• f(x) = 5.02 + 1.82x
• f(x) = 4.2 + 1.1x

Tags

CCSS.8.EE.B.5

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
  

Tags

CCSS.8.EE.C.8C

The table shown is comparing a person's foot length to their height in cm. Calculate a line of best fit.

• f(x) = 5.57 + 25.05x
• f(x) = 25.05 + 5.57x
• f(x) = 25.05 - 5.57x
• f(x) = - 25.05 + 5.57x

What is least-squares 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

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.HSF.LE.B.5