Two way ANOVA
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
yes it is a good way to describe test scores answer
The questions on the four-way test are:Is it the truth?Is it fair to all concerned?Will it build good will and better friendships?Will it be beneficial to all concerned?
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.
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.
Not sure about an interactive hypothesis: are you sure you don't mean alternative hypothesis?
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.
Two way ANOVA
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
same as one way anova population variance equal among groups noramlly distributed independent samples
! 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. !
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
The null hypothesis for a 1-way ANOVA is that the means of each subset of data are the same.
A statistical test, such as t-test or ANOVA, is commonly used to compare dependent values in experiments to determine if there is a significant difference between them. These tests provide a statistical measure to determine the likelihood that any differences observed are not due to random chance.
What is the probability of a type I error? What does this mean?How would you use this same information but set it up in a way that allows you to conduct a t-test? An ANOVA?