Fall 2017

**Note: Even though this assignment is worth 16 points,
doing the problems will help you
immensely in preparing for the final exam.
**

**
Question 1: One-Way ANOVA Problems: F tests (Chapter 13)**

**Part I.**
Problem
# 5, p. 561.

**
Part II. **
That the
starting salaries of new accounting graduates would differ according to
geographic regions of the United States seems logical. A random sample of
accounting firms is taken from three geographic regions - South, Northeast, and
West - and each is asked to state the starting salary for newly employed
accounting graduates. The table below contains the salary data (in $000).

South |
Northeast |
West |

$28 |
$38.5 |
$33 |

29 |
37 |
31 |

27.5 |
36.5 |
32.5 |

28.5 |
35.5 |
34 |

29 |
37 |
33.5 |

**1**. From
the above problem description identify the following:

a. Response variable.

b.
Factor.

c.
Treatments. Please list them.

d. Statistical or experimental units.

**2.**
Is this a case of balanced or unbalanced experimental design? Please explain
your answer.

**3.** Does a significant difference exists in the
average starting salaries of the accounting
graduates due to regional differences? Please state the null hypothesis and the
alternative hypothesis for investigating this question.

**
4**.
Is there a significant difference in the average wages of mechanics in the four
townships?

State the correct null and the alternative hypotheses for investigating this
question.

**5**. Use Excel to compute the ANOVA table
(attach your Excel output).

**6**. From
your Excel output, would you reject or fail to reject your null
hypothesis in part 3 above at 1% level of significance?

**a**. Use the critical value approach. Please state the
decision rules.

**b**. Use the p-value approach. Please state the decision
rules.

**c.
**
Based on
your conclusions in parts 'a' and 'b', in which region would you accept a job
offer

and why (assuming you are an accounting
graduate)? Please be specific.

**
Question 3: Simple Regression Problem (Chapter 14)
**The following data are the monthly salaries (Salary) and the grade
point averages (GPA) for students who obtained a bachelor's degree in business
administration.

GPA | Salary |

2.6 | 3600 |

3.4 | 3900 |

3.6 | 4300 |

3.2 | 3800 |

3.5 | 4200 |

2.9 | 3900 |

**Note: Please attach Excel output with complete results.
1**. Use the data to construct a scatter diagram that relates Salary to
GPA.

2

3

4a

**5.** Estimate the model that you have formulated in part 4a. Use the results to answer all the questions as stated in the text.

**a**. What is your estimated sample regression line (SRL)? Write it
out from the Excel output; show all solutions if you choose to solve the problem manually
by using the regression formulas to derive the estimated SRL.

**b**. Interpret the estimates for the intercept (or 'a') and the
slope (or 'b') of your estimated SRL.

** c**. Interpret the estimates for the intercept (or 'a') and the
slope (or 'b') of your estimated SRL.

**d**. How strong is the relationship between Salary and GPA?

**e**. Are the regression parameters (A, B) statistically significant? Use
α = .05 for your test.

**f**. Is the regression parameter (A) economically/practically
significant as suggested by your theory in part 4b above? Use
α = .05 for your test.

**6.** Linda will earn her degree in Business Administration in two weeks with a GPA of
3.0, predict her starting monthly salary?

**
Question 4: Multiple Regression Problem (Chapters. 15)**

The manager of Showtime Movie Theaters Inc, Boardman, would like to estimate the effects of advertising expenditures on weekly gross revenue using regression analysis. The manager hires you to do the analysis for her. The following historical data for a sample of eight weeks are given to you:

Weekly Gross Revenue ($1000s) | Weekly TV Advertising ($1000s) | Weekly Newspaper Adv. ($1000s) |

96 | 5.0 | 1.5 |

90 | 2.0 | 2.0 |

95 | 4.0 | 1.5 |

92 | 2.5 | 2.5 |

95 | 3.0 | 3.3 |

94 | 3.5 | 2.3 |

94 | 2.5 | 4.2 |

94 | 3.0 | 2.5 |

**Note: Please attach Excel output with complete results. **
**
1a**. Explain to the managers the causal relationship between Weekly Gross Sales,
TV Advertising and Newspaper Advertising. In your statement identify the DV (call it Y) and the IVs
(call them X

**2****a**. Use Excel to estimate
the model that you have specified in part 1b
above.

** b**. Are the estimated signs consistent with
you expectation on the basis of theory? Please be specific.

**
3**. Use the results in your Excel output to answer these questions

**d**. Suppose the owner plans to spend $3000 a week on TV advertising and
$1800 a week on newspaper advertising, how much should the owner expect to gross
in revenue for a week (a) using the simple regression model, and (b) using the
multiple regression model?

**e**.
Which medium of advertising is relatively more important in predicting
gross revenue and why?

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Last revised:
Saturday, November 18, 2017.**