Checking Linearity

Our method for checking the first assumption, linearity of the data, is not a precise, quantitative test. Rather, we'll use visual inspection to check for linearity.

One quick way to test the linearity of the data is to create an x-y scatter plot and observe whether the data generally follows a straight line (either with positive or negative slope). Plotting the regression line through the data may help visualize this as well.

Using Minitab for the linearity check:

1. Bring up your data in a worksheet. We used the site.mtw file in class.

2. Select Graph-Scatterplot from the menu bar. Select the "With Regression" option when prompted for the type of scatterplot.

3. Put Annual Sales (the dependent variable) in the Y Variables column and Square Feet (the independent variable) in the X Variables column. Remember that the independent variable is the variable that you can control and which you think will be a predictor of the dependent variable. In other words, the annual sales is dependent on the size of the store (in square feet). It's not the other way around. The size of the store doesn't grow or shrink depending on the number of sales!

4. Don't change the default options and click OK. You should get a plot of your data with a regression line through it. (If you don't get the regression line, in step 3 click Data View, Regression Tab and make sure Linear is selected.)

To interpret the linearity of this graph, "eyeball" the way the points fall above and below the regression line and ask yourself: Are the data points relatively linear or is it curved or skewed in some way? In our case, the data is relatively linear and not curved, so we conclude that the assumption of linearity is valid.

A better way to visually assess the linearity is to plot the residuals versus the independent variable and look to see if the errors are distributed evenly above and below 0 along the entire length of the sample.

Plotting Residuals versus the Independent Variable with Minitab

1. Select Stat-Regression from the menu bar.

2. Put Annual Sales in the Response box and Square Feet in the Predictors box. In our scenario, we think that the number of square feet will be a predictor of the annual sales of the store. Notice that the predictors box is large. There can be more than one predictor - perhaps advertising, employee training, etc. Many things can influence the response variable - the annual sales. We'll get to that during multiple linear regression. Right now, for simple linear regression, we're just looking at a single predictor.

3. Click the Graphs button. Put Square Feet in the Residuals versus the variables box.

To interpret this graph, ask yourself: Do the residual points fall equally above and below 0 along the entire length of the horizontal axis? In our case, the residuals do more or less fall equally above and below 0, so we conclude that the data is linear and the assumption of linearity is valid. Note: We also see that the residuals are closer to 0 for lower values of x (square feet). That may become important later when we talk about equal variance of errors.

## Friday, February 29, 2008

### Lecture 8 - Residual Analysis - Checking Linearity

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