Compute the equation of the linear regression that best fits

To do:

Use your skills from Chapter 6 - 9 to create the better linear regression line to predict a person's height. You'll need to do two linear regressions then determine which equation is "better" than the other. (Making a regression line is also called "building a model" because our whole goal is to find a line that is a really good estimate of the patter of data points. Just like modeling clay is shaped to look like an object, the regression model is shaped to the pattern of the data.)

Turn in:

Once you've determine the better linear regression, explain the following:

• Compute the equation of the linear regression that best fits the data. Show your equation here. (Don't show you work here, use technology and then give the equation.) Which variable did you use to predict height: arm span or forearm length?
• For both models: Compute and show us the correlation between the two variables. Interpret the correlation in a sentence.
• For both models:Compute and show us the coefficient of determination, and interpret it in a sentence.
• How did you decide which variable (armspan or forearm length) would be a better predictor? Cite at least two reasons. (r? r2? a scatter plot? s?)
• You don't have to turn in ALL of your work and calculations. You'll do more work "on paper", but then turn in your answers here.
• Using good notation is part of your grade. Use r2 and not r^2. Use yˆ and not y^. (Use the equation editor and look at the 7th tab for the empty box that has the hat above it.)

In a statistics class last spring the students measured their height, their arm span (finger tip to finger tip), and the length of their forearm (elbow to finger tip). All distances were measured in inches.

We collected data to answer this question: which is a better predictor of someone's height, their arm span or their forearm length? In other words, will someone's forearm length or their arm span more accurately predict their height?

Listed below are the data that were collected.