A company wants to find out if the average response time to a request differs across its two servers. Say µ1 is the true mean/expectation response time on server 1 and µ2 the true mean/expectation response time on server 2. Independent samples across the two servers is taken. 196 observations on server 1 return a sample mean of X¯ = 12.5 seconds and a sample variance of s 2 = 9 seconds. 225 observations on server 2 return a sample mean of X¯ = 12.2 seconds and a sample variance of s2 = 16 seconds. Required:

a. Write the null and alternative hypothesis that will address the goal of the company. Compute the test statistic, and state how you would obtain the p-value.
b. Say the p-value is very large (closer to 1). In a sentence or two explain what you would conclude about the null and alternative hypothesis.
c. Create and interpret a 95% confidence interval for the difference in the two servers population expectations.
d. The 95% confidence interval from part c. contains 0. How does this result relate to your explanation in part b? Explain in a sentence or two.

a) The null and alternative hypothesis are:

Test statistic t=0.88

The P-value is obtained from a t-table, taking into acount the degrees of freedom (419) and the type of test (two-tailed).

b)  A P-value close to 1 means that a sample result have a high probability to be obtained due to chance, given that the null hypothesis is true. It means that there is little evidence in favor of the alternative hypothesis.

c) The 95% confidence interval for the difference in the two servers population expectations is (-0.372, 0.972).

d) The consequences of the confidence interval containing 0 means that the hypothesis that there is no difference between the response time (d=0) is not a unprobable value for the true difference.

This relate to the previous conclusion as there is not enough evidence to support that there is significant difference between the response time, as the hypothesis that there is no difference is not an unusual value for the true difference.

Step-by-step explanation:

This is a hypothesis test for the difference between populations means.

The claim is that there is significant difference in the time response for the two servers.

Then, the null and alternative hypothesis are:

The significance level is 0.05.

The sample 1, of size n1=196 has a mean of 12.5 and a standard deviation of 3.

The sample 2, of size n2=225 has a mean of 12.2 and a standard deviation of 4.

The difference between sample means is Md=0.3.

The estimated standard error of the difference between means is computed using the formula:

Then, we can calculate the t-statistic as:

The degrees of freedom for this test are:

This test is a two-tailed test, with 419 degrees of freedom and t=0.88, so the P-value for this test is calculated as (using a t-table):

As the P-value (0.381) is greater than the significance level (0.05), the effect is not significant.

The null hypothesis failed to be rejected.

There is not enough evidence to support the claim that there is significant difference in the time response for the two servers.

Confidence interval

We have to calculate a 95% confidence interval for the difference between means.

The sample 1, of size n1=196 has a mean of 12.5 and a standard deviation of 3.

The sample 2, of size n2=225 has a mean of 12.2 and a standard deviation of 4.

The difference between sample means is Md=0.3.

The estimated standard error of the difference is s_Md=0.342.

The critical t-value for a 95% confidence interval and 419 degrees of freedom is t=1.966.

The margin of error (MOE) can be calculated as:

Then, the lower and upper bounds of the confidence interval are:

The 95% confidence interval for the difference in the two servers population expectations is (-0.372, 0.972).

6, 10

9, -13

12, -21

18, -23

step-by-step explanation:

you just add 6 to the x-coordinate and subtract 10 from the y-coordinate.