Example 8: Multiple
This example is based on the data file Poverty.sta
that is included with your STATISTICA
program. Refer to Example
7 demonstrating simple regression analysis for a description of the
data file. See also the Multiple
for a discussion of these methods.
For this example, several possible correlates of poverty will be analyzed
and the relative degree to which each predicts the percent of families
below the poverty line in a county will be determined. Thus, we will treat
variable 3 (PT_POOR) as the dependent
or criterion variable, and the remaining variables will be treated as
continuous predictor variables.
Specifying the analysis.
Open the Poverty.sta data file
and start General Linear Models:
bar. Select the Home tab.
In the File group, click the
Open arrow and select Open
Examples to display the Open
a STATISTICA Data File dialog box. Open the data file, which is
located in the Datasets folder.
Then, select the Statistics tab.
In the Advanced/Multivariate
group, click Advanced Models
and from the menu, select General Linear
to display the General
Linear Models (GLM) Startup Panel.
menus. From the File menu,
select Open Examples to display
the Open a STATISTICA Data File
dialog box. Open the data file, which is located in the Datasets
folder. Then, from the Statistics
- Advanced Linear/Nonlinear Models
submenu, select General Linear Models
to display the General
Linear Models (GLM) Startup Panel.
Select Multiple regression
as the Type of analysis, Quick specs dialog as the Specification
method, and then click the OK
button to display the GLM Multiple Regression Quick Specs dialog
Click the Variables button
to display the standard variable
selection dialog box. Select PT_POOR
in the Dependent variable list
and the remaining variables as the Predictor
variables, and then click the OK
button to return to the GLM Multiple
Regression Quick Specs dialog box.
To view the syntax
program automatically generated from the specifications, click the Syntax editor button to display the GLM Analysis Syntax Editor.
= POP_CHNG N_EMPLD TAX_RATE PT_PHONE PT_RURAL AGE;
= POP_CHNG + N_EMPLD + TAX_RATE + PT_PHONE + PT_RURAL
The remainder of the specifications for this analysis can use the default
specifications, so click the OK (Run)
button in the GLM Analysis Syntax Editor
or the OK button in the GLM Multiple Regression Quick Specs dialog
box to perform the analysis. A warning dialog box will be displayed. For
information about this warning, see the GLM
Introductory Overview - Summary of Computations, specifically the
paragraph about matrix
ill conditioning. Click the OK
button in the warning.
Regression coefficients. When
Results dialog box is displayed, select the Summary tab, and then click the
Coefficients button. In order
to learn which of the independent
variables contribute most to the prediction of poverty, examine the
unstandardized regression (or B) coefficients and the standardized regression
(or Beta) coefficients.
The Beta coefficients are the
coefficients you would have obtained had you first standardized all of
your variables to a mean of 0 and a standard deviation of 1. Thus, the
magnitude of these Beta coefficients
allows you to compare the relative contribution of each independent variable
in the prediction of the dependent variable. As is evident in the spreadsheet
shown above, variables POP_CHNG,
PT_RURAL, and N_EMPLD
are the most important predictors of poverty; of those, only the first
two variables are statistically significant (their 95% confidence interval
limits do not include 0). The regression coefficient for POP_CHNG
is negative; the less the population increased, the greater the number
of families who lived below the poverty level in the respective county.
The regression weight for PT_RURAL
is positive; the greater the percent of rural population, the greater
the poverty level.
Significance of regressor effects.
On the Summary tab, click the
Univariate results button to
display a spreadsheet containing tests of significance.
As this spreadsheet shows, only the POP_CHNG
and PT_RURAL effects are statistically
significant, p < .05.
After fitting a regression equation, we should always examine the predicted
and residual scores. For example, extreme outliers
may seriously bias results and lead to erroneous conclusions.
In the GLM
Results dialog box, click the More
results button, located at the bottom of the dialog box. Select
1 tab to access the options for analysis of residuals.
Casewise plot of residuals.
Usually, we should examine the pattern of the raw or standardized residuals
to identify any extreme outliers. In the Resids
for default plots group box, select the Standardized
option button. Then, click the Case
no. & res. button to create a graph with a casewise plot of
The scale used for the vertical axis of the casewise plot is in terms
of sigma, that is, the standard
deviation of residuals. If one or several cases fall outside of the ±
3 times sigma limits, we should
probably exclude the respective cases (which is easily accomplished via
selection conditions) and run the analysis again to ensure that key results
were not biased by these outliers.
Mahalanobis distances. Most
statistics textbooks devote some discussion to the issue of outliers
and residuals concerning the dependent
variable. However, the role of outliers in the predictor variables
is often overlooked. On the predictor variable side, we have a list of
variables that participate with different weights (the regression coefficients)
in the prediction of the dependent variable. We can think of the independent
variables as defining a multidimensional space in which each observation
can be located. For example, if we had two independent variables with
equal regression coefficients, we could construct a scatterplot
of those two variables, and place each observation in that plot. We could
then plot one point for the mean on both variables and compute the distances
of each observation from this mean (now called the centroid) in the two-dimensional
space; this is the conceptual idea behind the computation of the Mahalanobis
distance. Now, look at those distances to identify extreme cases on
the predictor variable side.
Select the Residuals 2 tab. In the
X (var/pred/res) list, select Mah.
Dis. Next, click the Histogram
of selected X (variable, predicted, or residual value) button to
display a histogram of the distribution of Mahalanobis distances.
It appears that there is one outlier case on Mahalanobis distances.
To identify this case, select the Residuals 1 tab.
In the Sort obs by drop-down
list, select Mahalanobis distance,
and then click the Predicted and residuals
button to create the Observed, Predicted,
and Residual Values spreadsheet.
Note that Shelby county (in
the first line) appears somewhat extreme as compared to the other counties
in the spreadsheet. If we look at the raw data, we will find that, indeed,
Shelby county is by far the largest
county in the data file with many more persons employed in agriculture
(variable N_EMPLD). Probably,
it would have been wise to express those numbers in percentages rather
than in absolute numbers, and in that case, the Mahalanobis distance of
Shelby county from the other
counties in the sample would probably not have been as large. As it stands,
however, Shelby county is clearly
Deleted residuals. Another very
important statistic that makes it possible for us to evaluate the seriousness
of the outlier problem is the deleted
residual. This is the standardized residual for the respective case
that we would obtain if the case were excluded from the analysis. Remember
that the multiple regression procedure fits a regression surface to express
the relationship between the dependent and predictor variables. If one
case is clearly an outlier (as is Shelby
county in this data), there is a tendency for the regression surface to
be "pulled" by this outlier so as to account for it as much
as possible. As a result, if the respective case were excluded, a completely
different surface (and B coefficients) would emerge. Therefore, if the
deleted residual is grossly different from the standardized residual,
we have reason to believe that the regression analysis is seriously biased
by the respective case.
In this example, the deleted residual for Shelby
county is an outlier that seriously affects the analysis. We can plot
the residuals against the deleted residuals: in the Resids
for default plots group box, select the Raw
option button. Then, click the Res.
& del. res. button, which will produce a scatterplot
of these values.
The scatterplot clearly shows the outlier; to label the outlier (Shelby), click the toolbar
button to display the Brushing 2D dialog box, and then Label the respective point.
Normal probability plots. There
are many additional graphs available from the Residuals
tabs. Most of them are more or less straightforward in their interpretation;
however, the normal
probability plots will be commented on here.
As previously mentioned, multiple linear regression assumes linear relationships
between the variables in the equation, and the normal
distribution of residuals. If these assumptions are violated, your
final conclusion may not be accurate. The normal probability plot of residuals
will give you an indication of whether or not gross violations of the
assumptions have occurred. In the Probab.
plot of resids group box, click the Normal
button to produce this plot.
This plot is constructed as follows: First the standardized residuals
are rank ordered. From these ranks, z values can be computed (i.e., standard
values of the normal
distribution) based on the assumption that the data come from a normal
distribution. These z values are plotted on the y-axis in the plot.
If the observed residuals (plotted on the x-axis) are normally distributed,
all values should fall onto a straight line in the plot; in this plot,
all points follow the line very closely. If the residuals are not normally
distributed, they will deviate from the line. Outliers
may also become evident in this plot.
If there is a general lack
of fit and the data seem to form a clear pattern (e.g., an S shape)
around the line, the dependent
variable may have to be transformed in some way (e.g., a log transformation
to "pull in" the tail of the distribution, etc.). A discussion
of such techniques is beyond the scope of this example (Neter, Wasserman,
and Kutner, 1985, pp. 134-141, present an excellent discussion of transformations
as remedies for non-normality and non-linearity); however, too often researchers
simply accept their data at face value without checking for the appropriateness
of their assumptions, leading to erroneous conclusions. For that reason,
one design goal of the GLM module is to make residual
(graphical) analysis as easy and accessible as possible.
See also GLM - Index.