Topic 4 DQ 1
Please Respond to the following post with a paragraph, add citations and references.
The term analysis of variance is often referred to as ANOVA. It is a hypothesis-testing technique used to test the equality of two or more population means by examining the variances of samples. ANOVA can help to determine whether the differences in the samples are due to random error or systematic treatment effects that are usually due to a mean in one or more of the groups.
ANOVA can be used to compare the equality of three or more means, however when the means from two samples are compared using ANOVA it is equivalent to using a t-test to compare the means of independent samples. ANOVA assumptions are all populations involved follow a normal distribution; they have the same variance and are randomly selected and independent of each other.
A great example would be comparing educational levels from students in rural areas on the West coast. See the example below to determine how the ANOVA is completed.
- The mean is calculated for each of your groups. Using the example of educational levels in rural areas on the West coast and calculate means for each population group.
- The overall mean is then calculated for the groups combined.
- Within each group, the total deviation of each individual’s score from the group mean is calculated. This is called within group variation.
- Next, the deviation of each group mean from the overall mean is calculated. This is call between group variation.
- Finally, an F statistic is calculated, which is the ratio of between group variation to the within group variation.
If the between group variation is significantly greater than the within group variation, then it is likely that there is a statistically significant difference between the groups. Using statistical software will tell you if the F statistic is significant (Thought Company, 2017).
Reference
Thought Company, (2017). Analysis of variance (ANOVA) Retrieved https://www.thoughtco.com/analysis-of-variance-ano…
October 15, 2018
