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| The '''`test`''' command performs [[Statistics/WaldTest|Wald tests]]. | '''`-test-`''' performs [[Statistics/WaldTest|Wald tests]]. See also [[Stata/TestParm|-testparm-]]. |
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| The F statistic of the hypothesis is 3.47, corresponding to a significance level of about 0.07. In most settings, this would be considered insufficient to reject the null hypothesis: the factor is not significant. | The [[Statistics/FTest|F-statistic]] of the hypothesis is 3.47, corresponding to a significance level of about 0.07. In most settings, this would be considered insufficient to reject the null hypothesis: the factor is not significant. |
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| Note that the F distribution with 1 numerator degree of freedom is the t^2^ distribution, so the F statistic can be double-checked by squaring the previously-estimated t statistic on the corresponding coefficient. | Note that the [[Statistics/FDistribution|F distribution]] with 1 numerator degree of freedom is the [[Statistics/HotelingsTSquaredDistribution|t-squared distribution]], so the F-statistic can be double-checked by squaring the previously-estimated t-statistic on the corresponding coefficient. |
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| The F statistic of the joint hypotheses is 8.85, corresponding to a significance level very close to 0. This is a strong basis to reject the joint null hypotheses: the variable is significant. | The F-statistic of the joint hypotheses is 8.85, corresponding to a significance level very close to 0. This is a strong basis to reject the joint null hypotheses: the variable is significant. |
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| [[Stata/TestParm|testparm]] [[https://www.stata.com/manuals/rtest.pdf|Stata manual for test]] |
[[https://www.stata.com/manuals/rtest.pdf|Stata manual for -test-]] |
Stata test
-test- performs Wald tests.
See also -testparm-.
Contents
Usage
As a demonstration:
. use https://www.stata-press.com/data/r18/census3
(1980 Census data by state)
. regress brate medage c.medage#c.medage i.region
[snip]
. test 3.region=0
( 1) 3.region = 0
F( 1, 44) = 3.47
Prob > F = 0.0691The F-statistic of the hypothesis is 3.47, corresponding to a significance level of about 0.07. In most settings, this would be considered insufficient to reject the null hypothesis: the factor is not significant.
Note that the F distribution with 1 numerator degree of freedom is the t-squared distribution, so the F-statistic can be double-checked by squaring the previously-estimated t-statistic on the corresponding coefficient.
. test (2.region=0) (3.region=0) (4.region=0)
( 1) 2.region = 0
( 2) 3.region = 0
( 3) 4.region = 0
F( 3, 44) = 8.85
Prob > F = 0.0001The F-statistic of the joint hypotheses is 8.85, corresponding to a significance level very close to 0. This is a strong basis to reject the joint null hypotheses: the variable is significant.
Expressions
Expressions are interpreted by these patterns, where a and b represent sub-expressions and 1 represents a scalar value.
a: hypothesis that a coefficient is equal to 0
a = 1: hypothesis that a coefficient is equal to a scalar value
a = b: hypothesis that coefficients are equal to each other
Expressions can be delimited with parentheses.
Sub-expressions can be variable names, factor indicators, or linear combinations. For example:
x1: variable x1
2.a: factor indicator 2 of a
2*x1: linear combination of a variable
x1+x2: linear combination of variables
If a multiple-equation model has been run, use the [equation]variable syntax to specify the hypothesis. For example, test [y1]x1=[y3]x1.