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Solving and analyzing hypothesis testing question

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Chapter 14 Chapter 14 ( total 10 points = part I and part II)
2. A member of the state legislature has expressed concern about the differences in the mathematics test scores of high school freshmen across the state. She asks her research assistant to conduct a study to investigate what factors could account for the differences. The research assistant looked at a random sample of school districts across the state and used the factors of percentage of mathematics teachers in each district with a degree in mathematics, the average age of mathematics teachers and the average salary of mathematics teachers:
Regression Output
Predictor Coef. SE Coef.
Constant 25.05 9.850
Math Degree 0.35 0.095
Age 0.38 0.185
Salary 0.12 0.077

Analysis of Variance
Source DF SS
Regression 3 1220.50
Residual Error 36 330.89

Part I (5 points)
Write the least squares prediction equation. What is the number of observations in the sample? Based on the multiple regression model given above, estimate the mathematics test score and calculate the value of the residual, if the percentage of teachers with a mathematics degree is 50.0, the average age is 43 and the average salary is 48,300 (48.3). If the actual mathematics test score for these factors is 69.50, what is the error for this observation? What is the total sum of squares? What is the explained variation? What is the mean square error?
Part II (5 points)
For the results above, calculate the Coefficient of Determination and the Adjusted coefficient of Determination and Test for the overall usefulness of the model using F-Statistic at 5% and 1% significance levels. Finally, test the usefulness (or significance of the three independent variables using t-test for 5% and 1% significance levels.

My answers:
Least Squares Prediction Equation:
Ŷ = 25.05 + 0.35X1 + 0.38X2 + 0.12X3 n=3+36+1=40
Estimated test score= _____ and residual= _____
Ŷ = 25.05 + 0.35 (50) + 0.38 (43) + 0.12 (48.3) = -69.50
e= 68.50-69.50 = -1.00
What is the number of observation in the sample? Based on the multiple regression model given above, estimate the mathematics test score and calculate the value of the residual, if the percentage of teachers with a math degree is 50.0, the avg. Age is 43 and the avg. Salary is 48,300 (48.3). If the actual mathematics test score for these factors is 69.50 what is the error for this observation?
What is the total sum of squares? What is the explained variation? What is the mean square error?

SS Total=SST = 1220.50 + 330.89 = 1551.39

SSR = explained variation = 1220.50

SSE = 1551.39 - 1220.50 = 330.89

MSE = 330.89/36 = 9.19

Part II
For the results above, calculate the Coefficient of Determination and the Adjusted coefficient of Determination and Test for the overall usefulness of the model using F-statistic at 5% and 1% significance levels. Finally, test the usefulness (or significance of the three independent variables using t-test for 5% and 1% significance levels.

R2 = 1220.50/1551.39 = 0.79

R2 adjusted = (_____________ - (3/39)) (39/36) =

MSR = 1220.53 / 3 = 406.83
MSE = 330.89/36 = 9.19
F - MSR/MSE = 406.83/9.19 = 44.27
F .01, 3, 36 = _______________________

Test the usefulness of significance of the three independent variables using t-test for 5% and 1% significance levels.

T1 = 0.35/0.095 =3.68
T2 = 0.38/0.185=2.05
T3 = 0.12/0.077=14.55

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Chapter 14 Chapter 14 ( total 10 points = part I and part II)
2. A member of the state legislature has expressed concern about the differences in the mathematics test scores of high school freshmen across the state. She asks her research assistant to conduct a study to investigate what factors could account for the differences. The research assistant looked at a random sample of school districts across the state and used the factors of percentage of mathematics teachers in each district with a degree in mathematics, the average age of mathematics teachers and the average salary of mathematics teachers:
Regression Output
Predictor Coef. SE Coef.
Constant 25.05 9.850
Math Degree 0.35 0.095
Age 0.38 0.185
Salary 0.12 0.077

Analysis of Variance
Source DF SS
Regression 3 1220.50
Residual Error 36 330.89

Part I (5 points)
Write the least squares prediction equation. What is the number of observations in the sample? Based on the multiple regression model given above, estimate ...

Solution Summary

The solution gives detailed steps on solving and then analyzing one hypothesis testing question.

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Answer question in green

(See attached file for full problem description)

Answer questions in green.

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Answer these questions below (a, b, c, d, & e) about the case study:

All case studies address the following points:
a. define the problem statement
b. define any and all assumptions made to address the case study
c. analyze the data utilizing concepts and knowledge relevant to this course
d. describe the specific recommendations or solution
e. answer the assigned questions of the case.

Answer this question also:
Then, after solving the case study, emphasize how you can use this technique to solve business problems. What business decisions will you be able to make using this type of analysis?

Case Study
ABC Corporation of California publishes a variety of statistics, including the number of individuals who got a new job during the past 12 months and the mean length of time the individuals have been on the job. The Statistical Analysis Department of ABC Corporation reported that the mean length of time of newly employed individuals in California was 17.00 weeks.
A local Chamber of Commerce for the City of Riverside has commissioned a study on the status of employment in the Riverside area. A sample of 16 employed residents of Riverside included data on the age and the number of weeks on a job. A portion of the data collected in October 2001 is shown as follows:

Age Weeks Employed Age Weeks Employed
55 21 25 6
30 18 40 21
23 11 25 13
52 36 25 11
41 19 59 34
25 12 49 27
42 7 33 18
45 25 35 20

Use EXCEL to answer these questions:

In a 700-1,050-word analysis, address the following:
a. Based on the above data, use descriptive statistics to summarize the data. Use EXCEL to generate your statistical results.
b. Develop a 99% confidence interval estimate of the mean age of newly hired employees.
c. Conduct a hypothesis test to individuals and determine whether the mean duration of employment in Riverside is greater than the California mean duration of 17.00 weeks. Use a .01 level of significance. What is your conclusion?
d. Is there a relationship between the age of a newly employed individual and the number of weeks of employment? Explain. For this case analysis, just answer the above questions in your prepared paper.
e. Cut and paste your results from EXCEL into your paper.
f. See the EXCEL spreadsheet with the data to get you started.
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