In a hypothesis test for ANOVA, you are interested in the significance of the difference between (samples, population variances, sample variances, population means). You assume that you have (unbiased, isolated, independent, dependent) (unequal, stratified, minimal, random) samples from populations that are (exponentially, unequally, normally, equally) distributed with (random, independent, equal, unequal) variances. The test is designed to be used with (nominal, interval-ratio, ordinal, numerical) level dependent variables.
What is the null hypothesis in an ANOVA?
a. H0: Not all population means are equal.
b. H0: All population means are different.
c. H0: All population means are equal.
d. H0: Some of the population means are equal.
What is the alternative hypothesis in an ANOVA?
a. H1: At least one of the population means is different.
b. H1: All population means are equal.
c. H1: All population means are different.
d. H1: Some of the population means are equal.
The F-test statistic is formed by taking the (product, sum, ratio) of two separate estimates of (correlation, variance, standard deviation, mean), where the estimate in the numerator is derived from the (sum of the variables, overall average, variation between categories, variation within categories) and the estimate in the denominator is derived from the (sum of the variables, overall average, variation between categories, variation within categories). The sampling distribution is the (p, z, F, t) distribution with (N - k, N - 1, k - 1, N) degrees of freedom within categories and (N - k, N - 1, k - 1, N) degrees of freedom between categories.
Once you compute the F(obtained) statistic for your data, you compare its value with F(obtained, dependent, alternative, critical) determined by the given alpha level and the degrees of freedom. If the test statistic is in the critical region, you (confirm, reject, support, reevaluate) the null hypothesis and conclude that there (is/is not) a significant difference between the means.