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**Statistical Thinking for Industrial Problem Solving**

In this video, we see how to use the Effect Summary table for variable selection using the Impurity data.

We’ll start by fitting a full model, with interactions, using Fit Model.

First, we’ll select **Impurity** as the Y.

Then, we’ll select **Temp** through **Shift**, and select **Macros**, and then **Factorial to Degree**. This adds all main effects and two-way interactions as model effects.

We’ll use the default personality and click **Run**.

The Effect Summary table shows the terms in the model, in ascending order of *p*-value.

We can use the Effect Summary table to slowly remove nonsignificant terms from the model, one at a time, starting from the bottom.

We can’t remove **Reaction Time** because it is involved in two-way interactions that are still in the model.

As we slowly remove terms, the *p*-values for the terms in the model all update, along with all the statistical output. We’ll use a *p*-value threshold of 0.05. Our final reduced model has all five main effects and three interactions.

**Catalyst Conc** and **Temp** are the most significant, followed by the interaction between **Catalyst Conc** and **Reaction Time**.

**Reactor** is significant, indicating that there is a difference in impurity between the three reactors. There is also a significant interaction between **Reactor** and **Shift**, indicating that the reactors perform differently, relative to **Impurity**, on the different shifts.

Let’s explore this model using the Prediction Profiler. Ignoring the other factors, lower values of **Temp** and **Catalyst Conc** have lower impurity. At these values, if we change **Reaction Time,** we don’t see much of a change in **Impurity**. And **Shift 1** has the lowest value of **Impurity** on Reactor 2, and **Shift 2** has the lowest value of **Impurity** on Reactor 1.