What is dispersion analysis?
In research, it is sometimes necessary to analyze data comparing more than two samples or groups. The type of inference statistics test, dispersion analysis (Anova) allows for several samples to be examined at the same time for the purposes of determining whether there is an important relationship between them. The justification is identical to T-tests, only the dispersion analysis includes an independent variable of two or more samples. The differences between samples and difference in one sample are determined. Anova is based on four assumptions: measurement level, sampling method, population distribution and scattering homogeneity.
In order to determine whether the differences are significant, Ana is concerned with the differences between samples and inside the samples, which is referred to as a scattering. Anova can find out whether the scattering between the samples is greater compared to the scattering between the sample members. If it is found that this is true, the paccords are considered significant.
performing anova test involves accepting a certainCH prerequisites. The first is that the independent method of random sampling is used and the selection of sample members from one population does not affect the selection of members from later populations. The dependent variables are measured primarily at the level of interval ratio; However, it is possible to use a variance analysis on the order level measurement. It can be assumed that the population is normally distributed, although this is not verifiable and the population deviations are the same, which means that the populations are homogeneous.
Research hypothesis assumes that at least one diameter differs from others, but different means are not identified as larger or smaller. It is only assumed that there is a difference. Anaova tests for zero hypothesis, which means there is no difference between all average values, so A = B = C. This requires alpha settings with reference to the level of probability where the zero hypothesis will be rejected.
F-Pometer is a test statistics used specifically for dispersion analysis as shown in the score F where beginsthe area of rejection of the null hypothesis. The formula for F is developed by Ronald Fisher statistics and is as follows: F = Between the group scattering (MSB) divided by estimation of the scattering within the group (MSW), so F = MSB/MSW. Each of the scattering estimates consists of two parts - the sum of squares (SSB and SSW) and the degrees of freedom (DF). Using Statistical tables for biological, agricultural and medical research can be set up and on the basis of this and zero hypothesis by no difference can be rejected. It can be concluded that there is a significant difference between all groups, if so.