Q: What happens if one of the assumptions for anova is violated?

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Null hypothesis of a one-way ANOVA is that the means are equal. Alternate hypothesis a one-way ANOVA is that at least one of the means are different.

Yes, in fact, that is one of ANOVA's chief uses.

Yes anova can and should be used to predict correlation between variable's in a single group. This is one of the primary and most common uses of such software.

No, F can never be smaller than 1; it can equal 1.

The F-test is designed to test if two population variances are equal. It compares the ratio of two variances. If the variances are equal, the ratio of the variances will be 1.The F-test provides the basis for ANOVA which can compare two or more groups.One-way (or one-factor) ANOVA: Tests the hypothesis that means from two or more samples are equal.Two-way (or two-factor) ANOVA: Simultaneously tests the hypothesis that the means of two variables from two or more groups are equal.

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The short answer is ANOVA is not one-tailed.

Null hypothesis of a one-way ANOVA is that the means are equal. Alternate hypothesis a one-way ANOVA is that at least one of the means are different.

One-Way ANOVA is used to test the comparison of 3 or more samples alleviating the risk of having a wrong answer in doing each test separately. ANOVA is an acronym for ANalysis Of VAriance

Independent variable is what you change in the experiment group. Dependent variable is what happens because of the independent variable. It has to be measurable in degrees, inches, or other such measurements.

Yes, in fact, that is one of ANOVA's chief uses.

They have violated the lease contract and can be held liable for damages.

Yes anova can and should be used to predict correlation between variable's in a single group. This is one of the primary and most common uses of such software.

The results of a one-way ANOVA can be considered reliable as long as the following as The results of a one-way ANOVA can be considered reliable as long as the following assumptions are met: * Response variable must be normally distributed (or approximately normally distributed). * Samples are independent. * Variances of populations are equal. * The sample is a Simple Random Sample (SRS). ANOVA is a relatively robust procedure with respect to violations of the normality assumption (Kirk, 1995) If data are ordinal, a non-parametric alternative to this test should be used - Kruskal-Wallis one-way analysis of variance. sumptions are met: * Response variable must be normally distributed (or approximately normally distributed). * Samples are independent. * Variances of populations are equal. * The sample is a Simple Random Sample (SRS). ANOVA is a relatively robust procedure with respect to violations of the normality assumption (Kirk, 1995) If data are ordinal, a non-parametric alternative to this test should be used - Kruskal-Wallis one-way analysis of variance

In a one-way ANOVA, the relate in an equation the total variation, , where i=1,2,…,a and j=1,2,…,n_i; the explained variation and the unexplained variation SST=SSA+SSE Degrees of freedom N-1 a-1 N-a

! ANOVA is generally computed for two or more QUANTITATIVE variables. If the quantitative variables are two or less in number, people prefer the t test (one sample t, paired t, or independent samples t) The Independent variable however is qualitative( for example, Girls and boys or Names of Schools.) It is the dependent variable that is Quantitative (for example, the ages - 2, 5 , 70, etc or weight or number of somethings). If you have 2 independent variables, you go for the two way ANOVA. Else, it's the one way ANOVA. !

No, F can never be smaller than 1; it can equal 1.

The F-test is designed to test if two population variances are equal. It compares the ratio of two variances. If the variances are equal, the ratio of the variances will be 1.The F-test provides the basis for ANOVA which can compare two or more groups.One-way (or one-factor) ANOVA: Tests the hypothesis that means from two or more samples are equal.Two-way (or two-factor) ANOVA: Simultaneously tests the hypothesis that the means of two variables from two or more groups are equal.