Suppose that you want to run a regression model and to test the statistical
significance of a group of variables.  For example, let’s say that you want
to predict students’ writing score from their reading, math and science
scores.  The data set with these variables in it can be downloaded by
following this link:
hsb2.sav
.

The SPSS syntax for this would be:

regression
 /dependent = write
 /method = enter read math science.

Variables Entered/Removed(b)

Model	Variables Entered	Variables Removed	Method
1	science score, reading score, math score(a)	.	Enter
a All requested variables entered.
b Dependent Variable: writing score

Model Summary

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.684(a)	.467	.459	6.97111
a Predictors: (Constant), science score, reading score, math score

ANOVA(b)

Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	8353.990	3	2784.663	57.302	.000(a)
	Residual	9524.885	196	48.596
	Total	17878.875	199
a Predictors: (Constant), science score, reading score, math score
b Dependent Variable: writing score

Coefficients(a)

		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
Model		B	Std. Error	Beta
1	(Constant)	13.192	3.069		4.299	.000
	reading score	.236	.069	.255	3.410	.001
	math score	.319	.076	.316	4.222	.000
	science score	.202	.069	.211	2.918	.004
a Dependent Variable: writing score

Now let’s suppose that you wanted to test the combined effect of math and
science on writing.  The SPSS syntax for doing that is below.  Note that the variables listed in the
method = test()
subcommand are not listed on the method = enter subcommand.  In other
words, the independent variables are listed only once.  Also note that,
unlike other SPSS subcommands, you can have multiple method =
subcommands within the regression command.

regression
 /dependent = write
 /method = enter read
 /method = test(math science).

Variables Entered/Removed(b)

Model	Variables Entered	Variables Removed	Method
1	reading score(a)	.	Enter
2	science score, math score	.	Test
a All requested variables entered.
b Dependent Variable: writing score

Model Summary

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.597(a)	.356	.353	7.62487
2	.684(b)	.467	.459	6.97111
a Predictors: (Constant), reading score
b Predictors: (Constant), reading score, science score, math score

ANOVA(d)

Model			Sum of Squares	df	Mean Square	F	Sig.	R Square Change
1	Regression		6367.421	1	6367.421	109.521	.000(a)
	Residual		11511.454	198	58.139
	Total		17878.875	199
2	Subset Tests	math score, science score	1986.569	2	993.284	20.439	.000(b)	.111
	Regression		8353.990	3	2784.663	57.302	.000(c)
	Residual		9524.885	196	48.596
	Total		17878.875	199
a Predictors: (Constant), reading score
b Tested against the full model.
c Predictors in the Full Model: (Constant), reading score, science score, math score.
d Dependent Variable: writing score

Coefficients(a)

		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
Model		B	Std. Error	Beta
1	(Constant)	23.959	2.806		8.539	.000
	reading score	.552	.053	.597	10.465	.000
2	(Constant)	13.192	3.069		4.299	.000
	reading score	.236	.069	.255	3.410	.001
	math score	.319	.076	.316	4.222	.000
	science score	.202	.069	.211	2.918	.004
a Dependent Variable: writing score

Excluded Variables(b)

		Beta In	t	Sig.	Partial Correlation	Collinearity Statistics
Model						Tolerance
1	math score	.396(a)	5.583	.000	.370	.561
	science score	.322(a)	4.609	.000	.312	.603
a Predictors in the Model: (Constant), reading score
b Dependent Variable: writing score

If you wanted to test all three variables together, the syntax would be:

regression
 /dependent = write
 /method = test(read math science).

Variables Entered/Removed(a)

Model	Variables Entered	Variables Removed	Method
1	science score, reading score, math score	.	Test
a Dependent Variable: writing score

Model Summary

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.684(a)	.467	.459	6.97111
a Predictors: (Constant), science score, reading score, math score

ANOVA(c)

Model			Sum of Squares	df	Mean Square	F	Sig.	R Square Change
1	Subset Tests	reading score, math score, science score	8353.990	3	2784.663	57.302	.000(a)	.467
	Regression		8353.990	3	2784.663	57.302	.000(b)
	Residual		9524.885	196	48.596
	Total		17878.875	199
a Tested against the full model.
b Predictors in the Full Model: (Constant), science score, reading score, math score.
c Dependent Variable: writing score

Coefficients(a)

		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
Model		B	Std. Error	Beta
1	(Constant)	13.192	3.069		4.299	.000
	reading score	.236	.069	.255	3.410	.001
	math score	.319	.076	.316	4.222	.000
	science score	.202	.069	.211	2.918	.004
a Dependent Variable: writing score

You will notice that the output from the first example with the three
independent variables on the method = enter subcommand and the output
from this example with the three independent variables on the method =
test() subcommand are virtually identical.  The only difference between
them is the line in the ANOVA table that gives the test of the subset, which in
this case is all of the variables.  The point of this example is that you
can put all of the independent variables in the regression on the method =
test() subcommand and not use a method = enter subcommand if you like.