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1. Using the dataset below, which is collected for a study on the demand for ros

ID: 1108052 • Letter: 1

Question

1. Using the dataset below, which is collected for a study on the demand for roses, please follow the steps and provide answers as indicated.

Year = Year and Quarter

Y    = Quantity of Roses Sold, Dozens

X2   = Average Wholesale Price of Roses, $ Per Dozen

X3   = Average Wholesale Price of Carnations, $ Per Dozen

X4   = Average Weekly Family Disposable Income, $ Per Week

X5   = Trend Variable Taking Values of 1, 2, and so on, for the Period

         1971.3 to 1975.3 in the Detroit Metropolitan Area

year

y

x2

x3

x4

x5

1971.3

11484

2.26

3.49

158.11

1

1971.4

9348

2.54

2.85

173.36

2

1972.1

8429

3.07

4.06

165.26

3

1972.2

10079

2.91

3.64

172.92

4

1972.3

9240

2.73

3.21

178.46

5

1972.4

8862

2.77

3.66

198.62

6

1973.1

6216

3.59

3.76

186.28

7

1973.2

8253

3.23

3.49

188.98

8

1973.3

8038

2.6

3.13

180.49

9

1973.4

7476

2.89

3.2

183.33

10

1974.1

5911

3.77

3.65

181.87

11

1974.2

7950

3.64

3.6

185

12

1974.3

6134

2.82

2.94

184

13

1974.4

5868

2.96

3.12

188.2

14

1975.1

3160

4.24

3.58

175.67

15

1975.2

5872

3.69

3.53

188

16

a. Estimate the following sample regression function:

Y = b1 + b2X2 + b3X3 + ei                                                  Eq. 0

Present your output below in a clear and precise manner (do not manually enter this, rather paste it from its source….most of you will probably use excel):

Highlight the sample slope coefficients for b2 and b3. Here, I’ll give you this part for free! See below:

the output snapshot above is a good idea of what I want to see in some of the future questions that ask for software output.

b. Estimate the following sample regression function:

Y = b1 + b2X2 + ei                                                                                                                       Eq. 1

Obtain the residuals and call them e1. They represent all the things that influence Y except X2.

Present your output below in a clear and precise manner (do not manually enter this, rather paste it from its source….most of you will probably use excel):

c. Estimate the following sample regression function:

X3 = b1 + b2X2 + ei                                                                                            Eq. 2

Obtain the residuals and call them e2. They represent all the things that influence X3 except X2.

Present your output below in a clear and precise manner (do not manually enter this, rather paste it from its source….most of you will probably use excel):

d. Estimate the following sample regression function:

e1 = b1 + b2e2 + ei                                                                                            Eq. 3

Present your output below in a clear and precise manner (do not manually enter this, rather paste it from its source….most of you will probably use excel):

Highlight the sample slope coefficients on b2. Comment on whether its value matches the value from Eq. 0.

e. In a few sentences, describe what you learned from this exercise.

______________________________________________________________________________

______________________________________________________________________________

______________________________________________________________________________

______________________________________________________________________________

year

y

x2

x3

x4

x5

1971.3

11484

2.26

3.49

158.11

1

1971.4

9348

2.54

2.85

173.36

2

1972.1

8429

3.07

4.06

165.26

3

1972.2

10079

2.91

3.64

172.92

4

1972.3

9240

2.73

3.21

178.46

5

1972.4

8862

2.77

3.66

198.62

6

1973.1

6216

3.59

3.76

186.28

7

1973.2

8253

3.23

3.49

188.98

8

1973.3

8038

2.6

3.13

180.49

9

1973.4

7476

2.89

3.2

183.33

10

1974.1

5911

3.77

3.65

181.87

11

1974.2

7950

3.64

3.6

185

12

1974.3

6134

2.82

2.94

184

13

1974.4

5868

2.96

3.12

188.2

14

1975.1

3160

4.24

3.58

175.67

15

1975.2

5872

3.69

3.53

188

16

Coefficients Standard Error t Stat P-value Intercept x2 x3 9734.217401 3782.19573 2815.251709 2888.059254 3.370505 .005019 572.45466086.60698 947.5111748 1.7E-05 2.971207 0.010822

Explanation / Answer

a.

b. Y = 9734.22 - 3782.20 X2 + 2815.25 X3

c.

d.

It does not match

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 9734.22 2888.06 3.37 0.01 3494.94 15973.49 3494.94 15973.49 x2 -3782.20 572.45 -6.61 0.00 -5018.91 -2545.48 -5018.91 -2545.48 x3 2815.25 947.51 2.97 0.01 768.28 4862.23 768.28 4862.23