The F-test is the ratio of the mean squared error between these two groups, and ANOVA isolates within-group and between-group variance.
When the standard deviation is unknown and the sample size is small, the T-test is used as a univariate hypothesis test. The F-test is a statistical test that examines if the variances of two normal populations are equal.
A researcher use the F-test to do a test for the equivalence of the two population variances. The F-test is used when a researcher wishes to see if two independent samples chosen from a normal population with the same variability are comparable.
Because variances are usually positive, F's numerator and denominator must be positive as well. As a result, F must always be a positive number. (If your ANOVA results in a negative F, double-check your calculations.)
ANOVA is used to compare three or more groups. The t-test is less likely to make a mistake. ANOVA carries a higher risk of error. The mean and standard deviation of a sample of students from classes A and B who have taken a mathematics course may differ.
great resource to learn data science in hindi. but in this particular video lecture there is a mistake....actually mutually exclusive event can never be independent event.
it really amazing to study....and easily understand difficult concepts...i hope you make more video on like power bi and nueral network model....its really helpful....thank you for these
please provide course notes
Prakash Suresh Lokhande
Great. please provide pdf notes
MD Mishkat Ahsan
plzz provide pdf notes also
Mujhe ye link Sanjeev Sir k ek vedio se mili.. & i am very happy to watch this vedio..
& my all doughts are clear..