F test variance analysis is the relevant topic of statistics and students who have problems with their assignment work and have shortage of time then students can get online statistics assignment help.
The object of the f-test is to note whether the two independent approximations of population variance are different considerably, or whether the two samples may be observed as drawn from the usual populations having the equal variance. For moving out the test of significance, we determine the ratio F. F is defined as:
The calculated value of F is matched up to with the table value for u1 and u2 at 5% or 1% level of importance. If analyzed value of F is greater than the table value then the F ratio is measured significant and the null hypothesis is discarded. Alternatively, if the analyzed value of F is less than the table value the null hypothesis is accepted and it is incidental that both the samples have come from the population having equal variance.
Since F test is based on the ratio of two variances, it is also recognized as the variance ratio test. The ratio of two variances goes behind a distribution called the f distribution named after the famous statistician R.A. Fisher.
Suppositions in F-test: The F test is based on the following suppositions:
familiarity, the values in each group are normally distributed,
Homogeneity, the variance inside each group should be the same for all groups (σ12 = σ22 = ……..= σc2) this assumption is wanted with the intention of combining or pool the variance inside the groups into a single within sets source of variation.
Independency of error: It states that the fault (variation of each value around its own group mean) should be autonomous for all values.
Some of its main topics are:
Semi averages method
Trend origin shifting
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