Database Reference
In-Depth Information
3)
What are linear regression coefficients? What does 'weight' mean?
4)
What is the linear regression mathematical formula, and how is it arranged?
5)
How are linear regression results interpreted?
Extra thought question:
6)
If you have an attribute that you want to use in a linear regression model, but it contains
text data, such as the make or model of a car, what could you do in order to be able to use
that attribute in your model?
EXERCISE
In the Chapter 4 exercise, you compiled your own data set about professional athletes. For this
exercise, we will enhance this data set and then build a linear regression model on it. Complete the
following steps:
1)
Open the data set you compiled for the Chapter 4 exercise. If you did not do that exercise,
please turn back to Chapter 4 and complete steps 1 - 4.
2)
Split your data set's observations in two: a training portion and a scoring portion. Be sure
that you have at least 20 observations in your training data set, and at least 10 in your
scoring data set. More would be better, so if you only have 30 observations total, perhaps
it would be good to take some time to look up ten or so more athletes to add to your
scoring data set. Also, we are going to try to predict each athlete's salary, so if Salary is not
one of your attributes, look it up for each athlete in your training data set (don't look it up
for the scoring data set athletes, we're going to try to predict these). Also, if there are other
attributes that you don't have, but that you think would be great predictors of salary, look
these up, and add them to both your training and scoring data sets. These might be things
like points per game, defensive statistics, etc. Be sure your attributes are numeric.
