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.
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plz provides all notes
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please provide notes also in pdf
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Shashi Kumar
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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.
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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
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