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An analysis of variance differs from a t test for independent means in that an analysis of variance?
Analysis of Variance (ANOVA) compares 3 or more means. The t-test would only compare 2 means.
When do you run a post-hoc test?
You run a post-hoc test after conducting an analysis of variance (ANOVA) and finding a significant result. A post-hoc test is used to determine which specific groups differ significantly from each other, as ANOVA only tells you that there is a difference somewhere but not which groups are different.
What are the criteria for duncan's multiple range test in statistics?
same as one way anova population variance equal among groups noramlly distributed independent samples
Does standard deviation effect the alpha level?
Yes.For some tests, such as the Fisher F-test, the test statistic is an estimate of the variance. If the alpha level was not affected, the test would be no use at all!Yes.For some tests, such as the Fisher F-test, the test statistic is an estimate of the variance. If the alpha level was not affected, the test would be no use at all!Yes.For some tests, such as the Fisher F-test, the test statistic is an estimate of the variance. If the alpha level was not affected, the test would be no use at all!Yes.For some tests, such as the Fisher F-test, the test statistic is an estimate of the variance. If the alpha level was not affected, the test would be no use at all!
What does it mean when the f-test value equals 1?
The F-test (when used in an Analysis of Variance Problem): F = Mean square between / Mean square within If F=1, Mean square within and Mean square between are almost equal.
What does test measuring?
Equal Variance
What does Hartley's test measure?
Equal Variance
Define error variance-?
The error in which a particular numbers are set apart is called error variance.
An analysis of variance differs from a t test for independent means in that an analysis of variance?
Analysis of Variance (ANOVA) compares 3 or more means. The t-test would only compare 2 means.
When do you run a post-hoc test?
You run a post-hoc test after conducting an analysis of variance (ANOVA) and finding a significant result. A post-hoc test is used to determine which specific groups differ significantly from each other, as ANOVA only tells you that there is a difference somewhere but not which groups are different.
Example of parametric test?
The Fisher F-test for Analysis of Variance (ANOVA).
What are the criteria for duncan's multiple range test in statistics?
same as one way anova population variance equal among groups noramlly distributed independent samples
Does standard deviation effect the alpha level?
Yes.For some tests, such as the Fisher F-test, the test statistic is an estimate of the variance. If the alpha level was not affected, the test would be no use at all!Yes.For some tests, such as the Fisher F-test, the test statistic is an estimate of the variance. If the alpha level was not affected, the test would be no use at all!Yes.For some tests, such as the Fisher F-test, the test statistic is an estimate of the variance. If the alpha level was not affected, the test would be no use at all!Yes.For some tests, such as the Fisher F-test, the test statistic is an estimate of the variance. If the alpha level was not affected, the test would be no use at all!
What does it mean when the f-test value equals 1?
The F-test (when used in an Analysis of Variance Problem): F = Mean square between / Mean square within If F=1, Mean square within and Mean square between are almost equal.
How can one test for heteroskedasticity in a statistical analysis?
One way to test for heteroskedasticity in a statistical analysis is to use the Breusch-Pagan test or the White test. These tests examine the relationship between the error terms and the independent variables in a regression model to determine if the variance of the errors is constant. If the test results show that the variance is not constant, it indicates the presence of heteroskedasticity.
Why is variance important when using statistics?
Variance is basically the raw material of statistics. If you don't have variance (differences in scores) you don't have much to work with or for that matter you don't have much to talk or think about. Consider a test where everyone gets the same score. What does that tell you? You might have some measurement problem, wherein the test is so easy everyone aces it. Still it might be so hard that everyone gets a zero. Now consider two tests. On each everyone gets the same score. That is on test one everyone gets a 15 and on the second test everyone gets a 10. That isn't telling you much is it? Now these are extreme cases, but in general, more variance is better and less variance isn't so good.
When do you use a z-value rejection region when testing a claim about a population mean?
If you know the population variance or if you have a very large sample then you could reliably use a Z test. Otherwise you should use a t test and use s^2 as an estimator for the population variance.
