msc zoology 1st sem student can help by this ppt

1. Synopsis
Introduction
defination
Formula and methods
examples
Merit and demerit of F test
Conclusion
References

2. Introduction
F test is a statistical test that
is used in hypothesis testing
to check whether the
variances of two populations
or two samples are equal or
not. In an f test, the data
follows an f distribution. This
test uses the f statistic to
compare two variances by
dividing them

3. Defination
“F test is the ratio
of two
independent chi-
square variance
divided by their
respective degree
of freedom”

4. Formula

5. Methods
• State the null hypothesis
with the alternate
hypothesis.
• Calculate the F-value, using
the formula.
• Find the F Statistic which is
the critical value for this
test. ...
• Finally, support or reject the
Null Hypothesis.

6. Examples of F-test
• Example 1: A research team wants
to study the effects of a new drug
on insomnia. 8 tests were
conducted with a variance of 600
initially. After 7 months 6 tests
were conducted with a variance of
400. At a significance level of 0.05
was there any improvement in the
results after 7 months?

7. Solution
• : As the variance needs to be compared, the f test needs
to be used.
H0 : s21=s22
H1 : s21>s22
n1 = 8, n2 = 6
df1 = 8 - 1 = 7
df2 = 6 - 1 = 5
s21 = 600, s22 = 400
The f test formula is given as follows:
F = s21s22 = 600 / 400
F = 1.5
Now from the F table the critical value F(0.05, 7, 5) = 4.88

9. Answer
• As 1.5 < 4.88, thus, the null hypothesis
cannot be rejected and there is not enough
evidence to conclude that there was an
improvement in insomnia after using the new
drug.
Answer: Fail to reject the null hypothesis

10. Merits of F test
• F-tests are surprisingly flexible
because you can include
different variances in the ratio
to test a wide variety of
properties. F-tests can
compare the fits of different
models, test the overall
significance in regression
models, test specific terms in
linear models, and determine
whether a set of means are all
equal.

11. Demerits of F test
• A limitation of the t
test was that the
amount of data that
was provided was slim
and therefore Samples
of larger amount of
values would more
accurately represent
the population.

12. Conclusion & References
• The F-test of overall
significance is the
hypothesis test for this
relationship. If the overall
F-test is significant, you
can conclude that R-
squared does not equal
zero, and the correlation
between the model and
dependent variable is
statistically significant.