Need real bad, how do you even do this?

(a) Null Hypothesis, :

    Alternate Hypothesis, :

(b) At a .01 level of significance, we conclude that the variance in annual repair costs is larger for older automobiles.

Step-by-step explanation:

We are given that a researcher is interested in finding out whether the variance of the annual repair costs also increases with the age of the automobile.

For this, a sample of 26 automobiles 4 years old showed a sample standard deviation for annual repair costs of $170 and a sample of 25 automobiles 2 years old showed a sample standard deviation for the annual repair cost of $100.

Let the population and sample variance for 4 years old automobiles be and respectively;

And the population and sample variance for 2 years old automobiles be and  respectively.

(a) Null Hypothesis, : {means that the variance in annual repair costs is same for both type of automobiles}

Alternate Hypothesis, : {means that the variance in annual repair costs is larger for older automobiles}

(b) The test statistics that will be used here is;

                  ~  where, = 26  and   = 25

Test Statistics =   ~

                       = 2.89

Now, at 1% level of significance, F table gives the critical value of between 0.411 and 2.435 at degree of freedom 25 , 24. since our test statistics is higher than both the critical value and it will fall in rejection region so we conclude that null hypothesis will be rejected .

And we conclude that the variance in annual repair costs is larger for older automobiles.

P-value is the exact % where our test statistics lie.

         If P-value > Significance level   -  Accept null hypothesis

         If P-value < Significance level   -  Reject null hypothesis  

So, here since our test statistics gets rejected so we conclude that p-value is less than 0.01 .