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. Remember that you need to use the .sav extension and
that you need to end the command with a period. In the **regression**
command, the **statistic**s subcommand must come before the **dependent**
subcommand. You can shorten **dependent** to **dep**. 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.
Here, we have specified **ci**, which is short for confidence intervals.
These are very useful for interpreting the output, as we will see. There
are four tables given in the output. SPSS has provided some superscripts
(a, b, etc.) to assist you in understanding 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** – R-Square is the proportion
of variance in the dependent variable (**science**) which can be predicted from the
independent variables (**math,** **female**, **socst** and **read**). This value
indicates that 48.9% of the variance in science scores can be predicted from the
variables **math,** **female**, **socst** and **read**. Note that 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.
R-Square is also called the coefficient of determination.

**e**. **Adjusted R-square** – As
predictors are added to the model, each predictor will explain some of the
variance in the dependent variable simply due to chance. One could continue to
add predictors to the model which would continue to improve the ability of the
predictors to explain the dependent variable, although some of this increase in
R-square would be simply due to chance variation in that particular sample. The
adjusted R-square attempts to yield a more honest value to estimate the
R-squared for the population. The value of R-square was .489, while the value
of Adjusted R-square was .479 Adjusted R-squared is computed using the formula
1 – ((1 – Rsq)(N – 1 )/ (N – k – 1)). From this formula, you can see that
when the number of observations is small and the number of predictors is large,
there will be a much greater difference between R-square and adjusted R-square
(because the ratio of (N – 1) / (N – k – 1) will be much greater than 1). By contrast,
when the number of observations is very large compared to the number of
predictors, the value of R-square and adjusted R-square will be much closer
because the ratio of (N – 1)/(N – k – 1) will approach 1.

f. **Std. Error of the Estimate** – The standard error of the estimate, also called the root
mean square error, is the standard
deviation of the error term, and is the square root of the Mean Square Residual
(or Error).

## 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. This is the source of variance, Regression, Residual and Total. 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, sometimes called Error). Note that the Sums of Squares for the Regression and Residual add up to the Total, reflecting the fact that the Total is partitioned into Regression and Residual variance.

e. **Sum of Squares** – These are the Sum of Squares associated with the three sources of variance,
Total, Model and Residual. These can be computed in many ways.
Conceptually, these formulas can be expressed as:
SSTotal The total variability around the
mean. S(Y – Ybar)^{2}.
SSResidual The sum of squared errors in prediction.
S(Y – Ypredicted)^{2}.
SSRegression The improvement in prediction by using
the predicted value of Y over just using the mean of Y. Hence, this would
be the squared differences between the predicted value of Y and the mean of Y,
S(Ypredicted – Ybar)^{2}. Another
way to think of this is the SSRegression is SSTotal – SSResidual. Note that the
SSTotal = SSRegression + SSResidual. Note that SSRegression /
SSTotal is equal to .489, the value of R-Square. This is because R-Square is the
proportion of the variance explained by the independent variables, hence can be computed
by SSRegression / SSTotal.

f. **df** – These are the
degrees of freedom associated with the sources of variance. The total
variance has N-1 degrees of freedom. In this case, there were N=200
students, so the DF
for total is 199. The model degrees of freedom corresponds to the number
of predictors minus 1 (K-1). You may think this would be 4-1 (since there were
4
independent variables in the model, **math**, **female**, **socst** and **read**).
But, the intercept is automatically included in the model (unless you explicitly omit the
intercept). Including the intercept, there are 5 predictors, so the model has
5-1=4
degrees of freedom. The Residual degrees of freedom is the DF total minus the DF
model, 199 – 4 is 195.

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

9543.72074 / 4 = 2385.93019. For the Residual, 9963.77926 / 195 =

51.0963039. These are computed so you can compute the F ratio, dividing the Mean Square Regression by the Mean Square Residual to test the significance of the predictors in the model.

h. **F** and **Sig.** – The F-value is the Mean
Square Regression (2385.93019) divided by the Mean Square Residual (51.0963039), yielding
F=46.69. The p-value associated with this F value is very small (0.0000).
These values are used to answer the question “Do the independent variables
reliably predict the dependent variable?”. The p-value is compared to your
alpha level (typically 0.05) and, if smaller, you can conclude “Yes, the
independent variables reliably predict the dependent variable”. You could say
that the group of variables **math**, and **female**, **socst** and **read** can be used to
reliably predict **science** (the dependent variable). If the p-value were greater than
0.05, you would say that the group of independent variables does not show a
statistically significant relationship with the dependent variable, or that the group of
independent variables does not reliably predict the dependent variable. Note that
this is an overall significance test assessing whether the group of independent
variables when used together reliably predict the dependent variable, and does
not address the ability of any of the particular independent variables to
predict the dependent variable. The ability of each individual independent
variable to predict the dependent variable is addressed in the table below where
each of the individual variables are listed.

## 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. These are
called unstandardized coefficients because they are measured in their natural
units. As such, the coefficients cannot be compared with one another to
determine which one is more influential in the model, because they can be
measured on different scales. For example, how can you compare the values
for gender with the values for reading scores? The regression
equation can be presented in many different ways, for example:

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

The column of estimates (coefficients or parameter estimates, from here on labeled coefficients) provides the values for b0, b1, b2, b3 and b4 for this equation. Expressed in terms of the variables used in this example, the regression equation is

** sciencePredicted = 12.325 + **

.389*math + -2.010*female+.050*socst+.335*read

These estimates tell you about the
relationship between the independent variables and the dependent variable.
These estimates tell the amount of increase in science scores that would be predicted
by a 1 unit increase in the predictor. Note: For the independent variables
which are not significant, the coefficients are not significantly different from
0, which should be taken into account when interpreting the coefficients. (See
the columns with the t-value and p-value about testing whether the coefficients
are significant).
**math** – The coefficient (parameter estimate) is

.389. So, for every unit (i.e., point, since this is the metric in
which the tests are measured)
increase in **math**, a .389 unit increase in **science** is predicted,
holding all other variables constant. (It does not matter at what value you hold
the other variables constant, because it is a linear model.) Or, for
every increase of one point on the **math** test, your science score is predicted to be
higher by .389 points. This is significantly different from 0.
**female** – For every unit increase in **female**, there is a

-2.010 unit decrease in
the predicted **science** score, holding all other variables constant. Since **female** is coded 0/1 (0=male,
1=female) the interpretation can be put more simply. For females the predicted
science score would be 2 points lower than for males. The variable **
female** is technically not statistically significantly different from 0,
because the p-value is greater than .05. However, .051 is so close to .05
that some researchers would still consider it to be statistically significant.
**socst** – The coefficient for **socst** is .050.
This means that for a 1-unit increase in the social studies score, we expect an
approximately .05 point increase in the science score. This is not
statistically significant; in other words, .050 is not different from 0.
**read** – The coefficient for **read** is .335.
Hence, for every unit increase in reading score we expect a .335 point increase
in the science score. This is statistically significant.

**e**. **Std. Error** – These are the standard
errors associated with the coefficients. The standard error is used for testing
whether the parameter is significantly different from 0 by dividing the
parameter estimate by the standard error to obtain a t-value (see the column
with t-values and p-values). The standard errors can also be used to form a
confidence interval for the parameter, as shown in the last two columns of this
table.

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.

**g**. **t** and **Sig**. – These columns provide the
t-value and 2 tailed p-value used in testing the null hypothesis that the
coefficient/parameter is 0. If you use a 2 tailed test, then you would compare
each p-value to your preselected value of alpha. Coefficients having p-values
less than alpha are statistically significant. For example, if you chose alpha to be 0.05,
coefficients having a p-value of 0.05 or less would be statistically significant
(i.e., you can reject the null hypothesis and say that the coefficient is
significantly different from 0). If you use a 1 tailed test (i.e., you predict
that the parameter will go in a particular direction), then you can divide the p-value by
2 before comparing it to your preselected alpha level. With a 2-tailed
test and alpha of 0.05, you should not reject the null hypothesis that the coefficient
for **female** is equal to 0, because p-value = 0.051 > 0.05. The coefficient of
-2.009765 is not significantly different
from 0. However, if you hypothesized specifically that males had higher scores than females (a 1-tailed test) and used an alpha of 0.05, the p-value
of .0255
is less than 0.05 and the coefficient for **female** would be significant at
the 0.05 level. In this case, we could say that the **female** coefficient is signfiicantly greater than 0. Neither a 1-tailed nor 2-tailed test would be significant at alpha of 0.01.

The constant is significantly different from 0 at the 0.05 alpha level. However, having a significant intercept is seldom interesting.

The coefficient for **math** (.389) is statistically significantly different from 0 using alpha
of 0.05 because its p-value is 0.000, which is smaller than 0.05.

The coefficient for **female** (-2.01) is not statictically
significant at the 0.05 level since the p-value is greater than .05.

The coefficient for **socst** (.05) is not statistically significantly different from 0 because
its p-value is definitely larger than 0.05.

The coefficient for **read** (.335) is statistically significant because its
p-value of 0.000 is less than .05.

**h**. **[95% Conf. Interval]** – 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 at alpha = .05 if the 95% confidence
interval includes zero. These confidence intervals
can help you to put the estimate
from the coefficient into perspective by seeing how much the value could vary.