This page shows an example regression analysis with footnotes explaining the
output. These data (hsb2)
were collected on 200 high schools students and are scores on various tests,
including science, math, reading and social studies (**socst**). The
variable **female** is a dichotomous variable coded 1 if the student was
female and 0 if male.

In the syntax below, the **get file** command is used to load the data
into SPSS. In quotes, you need to specify where the data file is located
on your computer. In the **regression**
command, the **statistic**s subcommand must come before the **dependent**
subcommand. You list the
independent variables after the equals sign on the **method** subcommand.
The **statistics** subcommand is not needed to run the regression, but on it
we can specify options that we would like to have included in the output.

Please note that SPSS sometimes includes footnotes as part of the output. We have left those intact and have started ours with the next letter of the alphabet.

get file "c:\data\hsb2.sav". regression /statistics coeff outs r anova ci /dependent science /method = enter math female socst read.

## Variables in the model

c. **Model** – SPSS allows you to specify multiple models in a
single **regression** command. This tells you the number of the model
being reported.

d. **Variables Entered** – SPSS allows you to enter variables into a
regression in blocks, and it allows stepwise regression. Hence, you need
to know which variables were entered into the current regression. If you
did not block your independent variables or use stepwise regression, this column
should list all of the independent variables that you specified.

e. **Variables Removed** – This column listed the variables that were
removed from the current regression. Usually, this column will be empty
unless you did a stepwise regression.

f. **Method** – This column tells you the method that SPSS used
to run the regression. “Enter” means that each independent variable was
entered in usual fashion. If you did a stepwise regression, the entry in
this column would tell you that.

## Overall Model Fit

b. **Model** – SPSS allows you to specify multiple models in a
single **regression** command. This tells you the number of the model
being reported.

c. **R** – R is the square root of R-Squared and is the
correlation between the observed and predicted values of dependent variable.

**d**. **R-Square** – This is the
proportion of variance in the dependent variable (**science**) which can be
explained by the independent variables (**math,** **female**, **socst**
and **read**). This is an overall measure of the strength of association and
does not reflect the extent to which any particular independent variable is
associated with the dependent variable.

**e**. **Adjusted R-square** – This is
an adjustment of the R-squared that penalizes the addition of extraneous
predictors to the model. Adjusted R-squared is computed using the formula 1 –
((1 – Rsq)((N – 1) /( N – k – 1)) where k is the number of predictors.

f. **Std. Error of the Estimate** – This is also referred to as the root mean
squared error. It is the standard deviation of the error term and the
square root of the Mean Square for the Residuals in the ANOVA table (see below).

## Anova Table

c. **Model** – SPSS allows you to specify multiple models in a
single **regression** command. This tells you the number of the model
being reported.

d. **Regression, Residual, Total** – Looking at the breakdown of variance
in the outcome variable, these are the categories we will examine: Regression,
Residual, and Total. The Total variance is partitioned into the variance which
can be explained by the independent variables (Model) and the variance which is
not explained by the independent variables (Error).

e. **Sum of Squares** – These are the Sum of Squares associated with the
three sources of variance, Total, Model and Residual. The Total variance is
partitioned into the variance which can be explained by the independent
variables (Regression) and the variance which is not explained by the
independent variables (Residual).

f. **df** – These are the degrees of freedom associated with the sources
of variance. The total variance has N-1 degrees of freedom. The Regression
degrees of freedom corresponds to the number of coefficients estimated minus 1.
Including the intercept, there are 5 coefficients, so the model has 5-1=4
degrees of freedom. The Error degrees of freedom is the DF total minus the DF
model, 199 – 4 =195.

g. **Mean Square** – These are the Mean Squares, the Sum of Squares divided
by their respective DF.

h. **F** and **Sig.** – This is the F-statistic the p-value associated
with it. The F-statistic is the Mean Square (Regression) divided by the Mean
Square (Residual): 2385.93/51.096 = 46.695. The p-value is compared to some
alpha level in testing the null hypothesis that all of the model coefficients
are 0.

## Parameter Estimates

b. **Model** – SPSS allows you to specify multiple models in a
single **regression** command. This tells you the number of the model
being reported.

**c**. This column shows the predictor variables
(**constant, math,** **female**, **socst**, **read**).
The first variable (**constant**) represents the
constant, also referred to in textbooks as the Y intercept, the height of the
regression line when it crosses the Y axis. In other words, this is the
predicted value of **science** when all other variables are 0.

**d**. **B** – These are the values
for the regression equation for predicting the dependent variable from the
independent variable. The regression equation is presented in many different
ways, for example:

**Ypredicted = b0 + b1*x1 + b2*x2 + b3*x3 + b4*x4**

The column of estimates provides the values for b0, b1, b2, b3 and b4 for this equation.

**math** – The coefficient for **math** is .389. So for every unit
increase in **math**, a 0.39 unit increase in **science** is predicted,
holding all other variables constant.
**female** – For every unit increase in **female**, we expect a -2.010
unit decrease in the **science** score, holding all other variables constant.
Because **female** is coded 0/1 (0=male, 1=female), the
interpretation is easy: for females, the predicted science score would be
2 points lower than for males.
**socst** – The coefficient for **socst** is .050. So for every unit
increase in **socst**, we expect an approximately .05 point increase in the
science score, holding all other variables constant.
**read** – The coefficient for **read** is .335. So for every unit
increase in **read**, we expect a .34 point increase in the science score.

**e**. **Std. Error** – These are
the standard errors associated with the coefficients.

f. **Beta** – These are the standardized coefficients. These are the
coefficients that you would obtain if you standardized all of the variables in
the regression, including the dependent and all of the independent variables,
and ran the regression. By standardizing the variables before running the
regression, you have put all of the variables on the same scale, and you can
compare the magnitude of the coefficients to see which one has more of an
effect. You will also notice that the larger betas are associated with the
larger t-values and lower p-values.

**g**. **t** and **Sig**. – These
are the t-statistics and their associated 2-tailed p-values used in testing
whether a given coefficient is significantly different from zero. Using an alpha
of 0.05:
The coefficient for **math** (0.389) is significantly different from 0 because its
p-value is 0.000, which is smaller than 0.05.
The coefficient for **female** (-2.010) is not significantly different from 0 because its
p-value is 0.051, which is larger than 0.05.
The coefficient for **socst** (0.0498443) is not statistically
significantly different from 0 because its p-value is definitely larger than
0.05.
The coefficient for **read** (**0.3352998)
is **statistically **significant because
its p-value of 0.000 is less than .05.
The intercept is significantly different from 0 at the 0.05 alpha level.
**

**h**. **95% Confidence Limit for B
Lower Bound **and** Upper Bound** – **These
are the 95% confidence intervals for the coefficients. The confidence intervals
are related to the p-values such that the coefficient will not be statistically
significant if the confidence interval includes 0. These confidence intervals
can help you to put the estimate from the coefficient into perspective by seeing
how much the value could vary.**