# 10.2 - Stepwise Regression

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In this section, we learn about the stepwise regression procedure. While we will soon learn the finer details, the general idea behind the stepwise regression procedure is that we build our regression model from a set of candidate predictor variables by entering and removing predictors — in a stepwise manner — into our model until there is no justifiable reason to enter or remove any more.

Our hope is, of course, that we end up with a reasonable and useful regression model. There is one sure way of ending up with a model that is certain to be underspecified — and that's if the set of candidate predictor variables doesn't include all of the variables that actually predict the response. This leads us to a fundamental rule of the stepwise regression procedure — the list of candidate predictor variables must include all of the variables that actually predict the response. Otherwise, we are sure to end up with a regression model that is underspecified and therefore misleading.

## An example

Let's learn how the stepwise regression procedure works by considering a data set that concerns the hardening of cement. Sounds interesting, eh? In particular, the researchers were interested in learning how the composition of the cement affected the heat evolved during the hardening of the cement. Therefore, they measured and recorded the following data (cement.txt) on 13 batches of cement:

• Response y: heat evolved in calories during hardening of cement on a per gram basis
• Predictor x1: % of tricalcium aluminate
• Predictor x2: % of tricalcium silicate
• Predictor x3: % of tetracalcium alumino ferrite
• Predictor x4: % of dicalcium silicate

Now, if you study the scatter plot matrix of the data:

you can get a hunch of which predictors are good candidates for being the first to enter the stepwise model. It looks as if the strongest relationship exists between either y and x2 or between y and x4 — and therefore, perhaps either x2 or x4 should enter the stepwise model first. Did you notice what else is going on in this data set though? A strong correlation also exists between the predictors x2 and x4! How does this correlation among the predictor variables play out in the stepwise procedure? Let's see what happens when we use the stepwise regression method to find a model that is appropriate for these data.

Note. The number of predictors in this data set is not large. The stepwise procedure is typically used on much larger data sets, for which it is not feasible to attempt to fit all of the possible regression models. For the sake of illustration, the data set here is necessarily small, so that the largeness of the data set does not obscure the pedagogical point being made.

### The procedure

Again, before we learn the finer details, let me again provide a broad overview of the steps involved. First, we start with no predictors in our "stepwise model." Then, at each step along the way we either enter or remove a predictor based on the partial F-tests — that is, the t-tests for the slope parameters — that are obtained. We stop when no more predictors can be justifiably entered or removed from our stepwise model, thereby leading us to a "final model."

Now, let's make this process a bit more concrete. Here goes:

Starting the procedure. The first thing we need to do is set a significance level for deciding when to enter a predictor into the stepwise model. We'll call this the Alpha-to-Enter significance level and will denote it as αE. Of course, we also need to set a significance level for deciding when to remove a predictor from the stepwise model. We'll call this the Alpha-to-Remove significance level and will denote it as αR. That is, first:

• Specify an Alpha-to-Enter significance level. This will typically be greater than the usual 0.05 level so that it is not too difficult to enter predictors into the model. Many software packages — Minitab included — set this significance level by default to αE = 0.15.
• Specify an Alpha-to-Remove significance level. This will typically be greater than the usual 0.05 level so that it is not too easy to remove predictors from the model. Again, many software packages — Minitab included — set this significance level by default to αR = 0.15.

Step #1. Once we've specified the starting significance levels, then we:

1. Fit each of the one-predictor models — that is, regress y on x1, regress y on x2, ..., and regress y on xp-1.
2. Of those predictors whose t-test P-value is less than αE = 0.15, the first predictor put in the stepwise model is the predictor that has the smallest t-test P-value.
3. If no predictor has a t-test P-value less than αE = 0.15, stop.

Step #2. Then:

1. Suppose x1 had the smallest t-test P-value below αE = 0.15 and therefore was deemed the "best" single predictor arising from the the first step.
2. Now, fit each of the two-predictor models that include x1 as a predictor — that is, regress y on x1and x2, regress y on x1and x3, ..., and regress y on x1 and xp-1.
3. Of those predictors whose t-test P-value is less than αE = 0.15, the second predictor put in the stepwise model is the predictor that has the smallest t-test P-value.
4. If no predictor has a t-test P-value less than αE = 0.15, stop. The model with the one predictor obtained from the first step is your final model.
5. But, suppose instead that x2 was deemed the "best" second predictor and it is therefore entered into the stepwise model.
6. Now, since x1 was the first predictor in the model, step back and see if entering x2 into the stepwise model somehow affected the significance of the x1 predictor. That is, check the t-test P-value for testing β1 = 0. If the t-test P-value for β1 = 0 has become not significant — that is, the P-value is greater than αR = 0.15 — remove x1 from the stepwise model.

Step #3. Then:

1. Suppose both x1 and x2 made it into the two-predictor stepwise model and remained there.
2. Now, fit each of the three-predictor models that include x1 and x2 as predictors — that is, regress y on x1, x2, and x3, regress y on x1, x2, and x4, ..., and regress y on x1, x2, and xp-1.
3. Of those predictors whose t-test P-value is less than αE = 0.15, the third predictor put in the stepwise model is the predictor that has the smallest t-test P-value.
4. If no predictor has a t-test P-value less than αE = 0.15, stop. The model containing the two predictors obtained from the second step is your final model.
5. But, suppose instead that x3 was deemed the "best" third predictor and it is therefore entered into the stepwise model.
6. Now, since x1 and x2 were the first predictors in the model, step back and see if entering x3 into the stepwise model somehow affected the significance of the x1 and x2 predictors. That is, check the t-test P-values for testing β1 = 0 and β2 = 0. If the t-test P-value for either β1 = 0 or β2 = 0 has become not significant — that is, the P-value is greater than αR = 0.15 — remove the predictor from the stepwise model.

Stopping the procedure. Continue the steps as described above until adding an additional predictor does not yield a t-test P-value below αE = 0.15.

Whew! Let's return to our cement data example so we can try out the stepwise procedure as described above.

### The example again

To start our stepwise regression procedure, let's set our Alpha-to-Enter significance level at αE = 0.15, and let's set our Alpha-to-Remove significance level at αR = 0.15. Now, regressing y on x1, regressing y on x2, regressing y on x3, and regressing y on x4, we obtain:

Each of the predictors is a candidate to be entered into the stepwise model because each t-test P-value is less than αE = 0.15. The predictors x2 and x4 tie for having the smallest t-test P-value — it is 0.001 in each case. But note the tie is an artifact of Minitab rounding to three decimal places. The t-statistic for x4 is larger in absolute value than the t-statistic for x2—4.77 versus 4.69—and therefore the P-value for x4 must be smaller. As a result of the first step, we enter x4 into our stepwise model.

Now, following step #2, we fit each of the two-predictor models that include x4 as a predictor — that is, we regress y on x4 and x1, regress y on x4 and x2, and regress y on x4 and x3, obtaining:

The predictor x2 is not eligible for entry into the stepwise model because its t-test P-value (0.687) is greater than αE = 0.15. The predictors x1 and x3 are candidates because each t-test P-value is less than αE = 0.15. The predictors x1 and x3 tie for having the smallest t-test P-value—it is < 0.001 in each case. But, again the tie is an artifact of Minitab rounding to three decimal places. The t-statistic for x1 is larger in absolute value than the t-statistic for x3—10.40 versus 6.35—and therefore the P-value for x1 must be smaller. As a result of the second step, we enter x1 into our stepwise model.

Now, since x4 was the first predictor in the model, we must step back and see if entering x1 into the stepwise model affected the significance of the x4 predictor. It did not—the t-test P-value for testing β1 = 0 is less than 0.001, and thus smaller than αR = 0.15. Therefore, we proceed to the third step with both x1 and x4 as predictors in our stepwise model.

Now, following step #3, we fit each of the three-predictor models that include x1 and x4 as predictors — that is, we regress y on x4, x1, and x2; and we regress y on x4, x1, and x3, obtaining:

Both of the remaining predictors—x2 and x3—are candidates to be entered into the stepwise model because each t-test P-value is less than αE = 0.15. The predictor x2 has the smallest t-test P-value (0.052). Therefore, as a result of the third step, we enter x2 into our stepwise model.

Now, since x1 and x4 were the first predictors in the model, we must step back and see if entering x2 into the stepwise model affected the significance of the x1 and x4 predictors. Indeed, it did—the t-test P-value for testing β4 = 0 is 0.205, which is greater than αR = 0.15. Therefore, we remove the predictor x4 from the stepwise model, leaving us with the predictors x1 and x2 in our stepwise model:

Now, we proceed fitting each of the three-predictor models that include x1 and x2 as predictors — that is, we regress y on x1, x2, and x3; and we regress y on x1, x2, and x4, obtaining:

Neither of the remaining predictors—x3 and x4—are eligible for entry into our stepwise model, because each t-test P-value—0.209 and 0.205, respectively—is greater than αE = 0.15. That is, we stop our stepwise regression procedure. Our final regression model, based on the stepwise procedure contains only the predictors x1 and x2:

Whew! That took a lot of work! The good news is that most statistical software—including Minitab—provides a stepwise regression procedure that does all of the dirty work for us. For example in Minitab v17, select Stat > Regression > Regression > Fit Regression Model, click the Stepwise button in the resulting Regression Dialog, select Stepwise for Method and select Include details for each step under Display the table of model selection details. Here's what the Minitab stepwise regression output looks like for our cement data example:

Minitab tells us that :

• a stepwise regression procedure was conducted on the response y and four predictors x1, x2, x3, and x4
• the Alpha-to-Enter significance level was set at αE = 0.15 and the Alpha-to-Remove significance level was set at αR = 0.15

The remaining portion of the output contains the results of the various steps of Minitab's stepwise regression procedure. One thing to keep in mind is that Minitab numbers the steps a little differently than described above. Minitab considers a step any addition or removal of a predictor from the stepwise model, whereas our steps—step #3, for example—considers the addition of one predictor and the removal of another as one step.

The results of each of Mintab's steps are reported in a column labeled by the step number. It took Minitab 4 steps before the procedure was stopped. Here's what the output tells us:

• Just as our work above showed, as a result of Minitab's first step, the predictor x4 is entered into the stepwise model. Minitab tells us that the estimated intercept ("Constant") b0 = 117.57 and the estimated slope b4 = -0.738. The P-value for testing β4 = 0 is 0.001. The estimate S, which equals the square root of MSE, is 8.96. The R2-value is 67.45% and the adjusted R2-value is 64.50%. Mallows' Cp-statistic, which we learn about in the next section, is 138.73. The output also includes a predicted R2-value, which we'll come back to in Section 10.5.
• As a result of Minitab's second step, the predictor x1 is entered into the stepwise model already containing the predictor x4. Minitab tells us that the estimated intercept b0 = 103.10, the estimated slope b4 = -0.614, and the estimated slope b1 = 1.44. The P-value for testing β4 = 0 is < 0.001. The P-value for testing β1 = 0 is < 0.001. The estimate S is 2.73. The R2-value is 97.25% and the adjusted R2-value is 96.70%. Mallows' Cp-statistic is 5.5.
• As a result of Minitab's third step, the predictor x2 is entered into the stepwise model already containing the predictors x1 and x4. Minitab tells us that the estimated intercept b0 = 71.6, the estimated slope b4 = -0.237, the estimated slope b1 = 1.452, and the estimated slope b2 = 0.416. The P-value for testing β4 = 0 is 0.205. The P-value for testing β1 = 0 is < 0.001. The P-value for testing β2 = 0 is 0.052. The estimate S is 2.31. The R2-value is 98.23% and the adjusted R2-value is 97.64%. Mallows' Cp-statistic is 3.02.
• As a result of Minitab's fourth and final step, the predictor x4 is removed from the stepwise model containing the predictors x1, x2, and x4, leaving us with the final model containing only the predictors x1 and x2. Minitab tells us that the estimated intercept b0 = 52.58, the estimated slope b1 = 1.468, and the estimated slope b2 = 0.6623. The P-value for testing β1 = 0 is < 0.001. The P-value for testing β2 = 0 is < 0.001. The estimate S is 2.41. The R2-value is 97.87% and the adjusted R2-value is 97.44%. Mallows' Cp-statistic is 2.68.

Does the stepwise regression procedure lead us to the "best" model? No, not at all! Nothing occurs in the stepwise regression procedure to guarantee that we have found the optimal model. Case in point! Suppose we defined the best model to be the model with the largest adjusted R2-value. Then, here, we would prefer the model containing the three predictors x1, x2, and x4, because its adjusted R2-value is 97.64%, which is higher than the adjusted R2-value of 97.44% for the final stepwise model containing just the two predictors x1 and x2.

Again, nothing occurs in the stepwise regression procedure to guarantee that we have found the optimal model. This, and other cautions of the stepwise regression procedure, are delineated in the next section.

### Cautions!

Here are some things to keep in mind concerning the stepwise regression procedure:

• The final model is not guaranteed to be optimal in any specified sense.
• The procedure yields a single final model, although there are often several equally good models.
• Stepwise regression does not take into account a researcher's knowledge about the predictors. It may be necessary to force the procedure to include important predictors.
• One should not over-interpret the order in which predictors are entered into the model.
• One should not jump to the conclusion that all the important predictor variables for predicting y have been identified, or that all the unimportant predictor variables have been eliminated. It is, of course, possible that we may have committed a Type I or Type II error along the way.
• Many t-tests for testing βk = 0 are conducted in a stepwise regression procedure. The probability is therefore high that we included some unimportant predictors or excluded some important predictors.

It's for all of these reasons that one should be careful not to overuse or overstate the results of any stepwise regression procedure.

### More examples

Let's close up our discussion of stepwise regression by taking a quick look at two more examples.

Example #1. Are a person's brain size and body size predictive of his or her intelligence? Interested in this question, some researchers (Willerman, et al, 1991) collected the following data (iqsize.txt) on a sample of n = 38 college students:

• Response (y): Performance IQ scores (PIQ) from the revised Wechsler Adult Intelligence Scale. This variable served as the investigator's measure of the individual's intelligence.
• Potential predictor (x1): Brain size based on the count obtained from MRI scans (given as count/10,000).
• Potential predictor (x2): Height in inches.
• Potential predictor (x3): Weight in pounds.

A matrix plot of the resulting data looks like:

Using Minitab to perform the stepwise regression procedure, we obtain:

The output tells us:

• The first predictor entered into the stepwise model is Brain. Minitab tells us that the estimated intercept is 4.7 and the estimated slope for Brain is 1.177. The P-value for testing βBrain = 0 is 0.019. The estimate S is 21.2, the R2-value is 14.27%, the adjusted R2-value is 11.89%, and Mallows' Cp-statistic is 7.34.
• The second and final predictor entered into the stepwise model is Height. Minitab tells us that the estimated intercept is 111.3, the estimated slope for Brain is 2.061, and the estimated slope for Height is -2.730. The P-value for testing βBrain = 0 is 0.001. The P-value for testing βHeight = 0 is 0.009. The estimate S is 19.5, the R2-value is 29.49%, the adjusted R2-value is 25.46%, and Mallows' Cp-statistic is 2.00.
• At no step is a predictor removed from the stepwise model.
• When αE = αR = 0.15, the final stepwise regression model contains the predictors Brain and Height.

Example #2. Some researchers observed the following data (bloodpress.txt) on 20 individuals with high blood pressure:

• blood pressure (y = BP, in mm Hg)
• age (x1 = Age, in years)
• weight (x2 = Weight, in kg)
• body surface area (x3 = BSA, in sq m)
• duration of hypertension (x4 = Dur, in years)
• basal pulse (x5 = Pulse, in beats per minute)
• stress index (x6 = Stress)

The researchers were interested in determining if a relationship exists between blood pressure and age, weight, body surface area, duration, pulse rate and/or stress level.

The matrix plot of BP, Age, Weight, and BSA looks like:

and the matrix plot of BP, Dur, Pulse, and Stress looks like:

Using Minitab to perform the stepwise regression procedure, we obtain:

When αE = αR = 0.15, the final stepwise regression model contains the predictors Weight, Age, and BSA.

### PRACTICE PROBLEMS: Stepwise regression

Brain size and body size. Imagine that you do not have automated stepwise regression software at your disposal, and conduct the stepwise regression procedure on the iqsize.txt data set. Setting Alpha-to-Remove and Alpha-to-Enter at 0.15, verify the final model obtained above by Minitab.

That is:

1. First, fit each of the three possible simple linear regression models. That is, regress PIQ on Brain, regress PIQ on Height, and regress PIQ on Weight. (See Minitab Help: Performing a basic regression analyis). The first predictor that should be entered into the stepwise model is the predictor with the smallest P-value (or equivalently the largest t-statistic in absolute value) for testing βk = 0, providing the P-value is smaller than 0.15. What is the first predictor that should be entered into the stepwise model?
2. Now, fit each of the possible two-predictor multiple linear regression models which include the first predictor identified above and each of the remaining two predictors. (See Minitab Help: Performing a basic regression analyis). Which predictor should be entered into the model next?
3. Continue the stepwise regression procedure until you can not justify entering or removing any more predictors. What is the final model identified by your stepwise regression procedure?