Page 101 - Statistics II for Dummies

Chapter 5: Multiple Regression with Two X Variables 85

Stepping through the analysis

Your job in conducting a multiple regression analysis is to do the following
(the computer can help you do steps three through six):

1. Come up with a list of possible x variables that may be helpful in
estimating y.
2. Collect data on the y variable and your x variables from step one.
3. Check the relationships between each x variable and y (using
scatterplots and correlations), and use the results to eliminate those x
variables that aren’t strongly related to y.
4. Look at possible relationships between the x variables to make sure
you aren’t being redundant (in statistical terms, you’re trying to avoid
the problem of multicolinearity).
If two x variables relate to y the same way, you don’t need both in the
model.

5. Use those x variables (from step four) in a multiple regression analysis
to find the best-fitting model for your data.
6. Use the best-fitting model (from step five) to predict y for given
x-values by plugging those x-values into the model.

I outline each of these steps in the sections to follow.

Looking at x’s and y’s

The first step of a multiple regression analysis comes way before the number
crunching on the computer; it occurs even before the data is collected. Step
one is where you sit down and think about what variables may be useful in
predicting your response variable y. This step will likely take more time than
any other step, except maybe the data-collection process. Deciding which x
variables may be candidates for consideration in your model is a deal-breaking
step, because you can’t go back and collect more data after the analysis is
over.
Always check to be sure that your response variable, y, and at least one of the
x variables are quantitative. For example, if y isn’t quantitative but at least one
x is, a logistic regression model may be in order (see Chapter 8).

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