Hypothesis Testing

answer the following questions for the set scenario:
The annual profit for your organisation last year was AED 7,162.28 per employee. The average for the sector was AED 7,311.12 per employee. You need to know if there is evidence that your organisation is not as profitable, on average, as your competitors. The standard deviation for the sector is AED 619.18 per employee.

Question 1. State H0 and H1 for the test to check this?
Assuming that annual per employee profit follows a Normal distribution, the resulting P-value for the test is, P= 0.405 (3 d.p.):
Question 2. Based entirely on this P-value, what would you conclude from the test?
Question 3. What is the probability of getting a profit per employee of AED 7,162.28 or less, when there is no difference between that for your company and the industry average?
Question 4. If your annual per employee profit was such that the test had resulted in a P-value given by P=0.032 and you were testing at the a=0.01 significance level, what would you conclude and why?
Question 5. If you chose to reject H0 but H0 was actually true, would you be making a Type I or Type II error?
Question 6. What assumptions have you made about the annual per employee profit in this test?

Hypothesis testing

Order Description

DT #4 – Hypothesis Testing

The topic is in the Assignments area.

See examples on p. 323 – 324 and 325 – 326. Page 326 shows the computed value of z in the “do not reject Ho” and page 329 describes the calculated z-value with the associated p-value.

I want to be sure each student understands two of the three ways to get to REJECT the null hypothesis. The two methods are actually the same test. You can use the critical value (z, t, F, chi squared, etc) to define the line between acceptance/rejection areas or you can use the alpha to define the rejection area (as a percentage).

If the sample gives enough evidence then you GET to reject the null hypothesis. (Which lends support to your research statement in the alternative hypothesis)

You calculate the test statistic (or use the p-value) to see if you GET to reject the null hypothesis.

1. If the test statistic is outside the acceptance area (in the rejection area) you GET to reject the null hypothesis. . (p. 324) OR

2. If the p-value is inside (less than alpha) the rejection area you GET to reject the null hypothesis. (p. 329)

If you think in terms of the areas on the bell shaped curve as shown on pages 321, 323,324, 326, 328, 329, 334, 335, 337, 340 , and 341 then it is more clearly the same test. The test statistic and p-value are from the sample’s data, they are compared to the critical value and/or alpha (management decision, Step 2).

Page 328 illustrates the two tailed and one tailed usage of this concept.
***If you would like a copy of these pages listed please feel free to call or message me at any time. I am able to scan and send them.

Hypothesis testing

Order Description

DT #4 – Hypothesis Testing

The topic is in the Assignments area.

See examples on p. 323 – 324 and 325 – 326. Page 326 shows the computed value of z in the “do not reject Ho” and page 329 describes the calculated z-value with the associated p-value.

I want to be sure each student understands two of the three ways to get to REJECT the null hypothesis. The two methods are actually the same test. You can use the critical value (z, t, F, chi squared, etc) to define the line between acceptance/rejection areas or you can use the alpha to define the rejection area (as a percentage).

If the sample gives enough evidence then you GET to reject the null hypothesis. (Which lends support to your research statement in the alternative hypothesis)

You calculate the test statistic (or use the p-value) to see if you GET to reject the null hypothesis.

1. If the test statistic is outside the acceptance area (in the rejection area) you GET to reject the null hypothesis. . (p. 324) OR

2. If the p-value is inside (less than alpha) the rejection area you GET to reject the null hypothesis. (p. 329)

If you think in terms of the areas on the bell shaped curve as shown on pages 321, 323,324, 326, 328, 329, 334, 335, 337, 340 , and 341 then it is more clearly the same test. The test statistic and p-value are from the sample’s data, they are compared to the critical value and/or alpha (management decision, Step 2).

Page 328 illustrates the two tailed and one tailed usage of this concept.
***If you would like a copy of these pages listed please feel free to call or message me at any time. I am able to scan and send them.