## Predict business results by using quantitative methods.

In this assignment, you will be assessed based on the following outcome:
GB513-3: Predict business results by using quantitative methods.
Make sure to use the Unit 4 Assignment template from Course Documents when you turn in your answers.
This assignment requires you to use Excel in all three questions. Make sure you explain your answers and provide the regression output tables for Questions 1 and 2 as you are showing work in the template.
You still need to submit the Excel file you used to generate your answers, in addition to the report in Word. Failure to submit the Excel file will result in a 20 point deduction.

Question 1

Shown below are rental and leasing revenue figures for office machinery and equipment in the United States over a 7-year period according to the U.S. Census Bureau. Use this data and the regression tool in the data analysis tool pack to run a linear regression.
Based on the formula you get from the regression output, answer the following questions:
a)    What is the forecast for the rental and leasing revenue for the year 2011?

b)    How confident are you in this forecast? Explain your answer by citing the relevant metrics. Do not just put down a number without explanation.

Year     Rental and Leasing
(\$ millions)
2004     5,860
2005     6,632
2006     6,543
2007     5,952
2008     5,732
2009     5,423
2010     4,589

Question 2

Suppose a researcher gathered survey data from 19 employees and asked the employees to rate their job satisfaction on a scale from 0 to 100 (with 100 being perfectly satisfied). Suppose the following data represent the results of this survey. Assume that relationship with their supervisor is rated on a scale from 0 to 50 (0 represents a poor relationship and 50 represents an excellent relationship); overall quality of the work environment is rated on a scale from 0 to 100 (0 represents poor work environment and 100 represents an excellent work environment); and opportunities for advancement is rated on a scale from 0 to 100 (0 represents no opportunities and 100 represents excellent opportunities).
a)    What is the regression formula based on the results from your regression?

b)    How reliable do you think the estimates will be based on this formula? Explain your answer by citing the relevant metrics. Do not just put down a number without explanation.

c)    Are there any variables that do not appear to be good predictors of job satisfaction? How can you tell?

d)    If a new employee reports that her relationship with her supervisor is 40, rates her opportunities for advancement to be at 30, finds the quality of the work environment to be at 75, and works 60 hours per week, what would you expect her job satisfaction score to be?

Job satisfaction     Relationship with supervisor     Opportunities for
environment     Total hours
worked per week
55     27     42     50     52
20     35     28     60     60
85     40     7     45     42
65     35     48     65     53
45     29     32     40     58
70     42     41     50     48
35     22     18     75     55
60     34     32     40     50
95     40     48     45     40
65     33     11     60     38
85     38     33     55     47
10     5     21     50     62
75     37     42     45     43
80     37     46     40     42
50     31     48     60     46
90     42     30     55     38
75     36     39     70     43
45     20     22     40     42
65     32     12     55     53

Question 3

Investment analysts generally believe the interest rate on bonds is related to the prime interest rate for loans.

a) Use the following data to construct a scatter graph and then fit a regression line to the data. Report the regression formula and the r-squared value from the chart (right click on the data points, select Add Trend line and select Options to show these metrics).

b) Do you think the bond rate can be predicted by the prime interest rate? Justify your answer using the relevant metrics.
Prime interest rate    Bond rate
0.05    0.28
0.12    0.38
0.09    0.22
0.015    0.14
0.004    0.05
0.11    0.44
0.06    0.28
0.02    0.105