JAM007 PLEASE

hiii

Sales	Age	Growth	Income Save Time On Research and Writing Hire a Pro to Write You a 100% Plagiarism-Free Paper. Get My Paper	HS	College
1695712.620	33.1574	0.8299	26748.51	73.5949	17.8350
3403862.053	32.6667	0.6619	53063.79	88.4557	31.9439
2710352.905	35.6553	0.9688	36090.14	73.5362	18.6198
529215.459	33.0728	0.0821	32058.07	79.1780	20.6284
663686.654	35.7585	0.4646	47843.42	84.1838	35.2032
2546324.335	33.8132	2.1796	50180.97	93.4996	41.7057
2787046.202	30.9797	1.8048	30710.08	78.0234	28.0250
612696.054	30.7843	-0.0569	29141.70	70.2949	15.0882
891822.033	32.3164	-0.1577	25980.15	70.6674	10.9829
1124967.965	32.5312	0.3664	18730.88	63.7395	13.2458
909500.976	31.4400	2.2256	31109.23	76.9059	19.5500
2631166.881	33.1613	1.5158	35614.12	82.9452	20.8135
882972.654	31.8736	0.1413	23038.43	65.2127	16.9796
1078573.124	33.4072	-1.0400	34531.72	73.4944	32.9920
844320.194	34.0470	1.6836	30350.36	80.2201	22.3185
1849119.029	28.8879	2.3596	38964.94	87.5973	24.5670
3860007.316	36.1056	0.7840	49392.77	85.3041	30.8790
826573.880	32.8083	0.1164	25595.69	65.5884	17.4545
604682.868	33.0538	1.1498	29622.61	80.6176	18.6356
1903611.600	33.4996	0.0606	31586.10	80.3790	38.3249
2356808.391	32.6809	1.6338	39674.56	79.8526	23.7780
2788571.957	28.5166	1.1256	28878.98	81.2371	16.9300
634878.286	32.8945	1.4884	24287.08	70.2244	19.1429
2371627.369	30.5024	4.7937	46711.24	87.1046	30.8843
2627837.961	30.2922	1.8922	33449.81	80.2057	26.5570
1868116.330	31.2911	1.8667	31694.45	75.2914	28.3600
2236796.862	33.0498	1.7896	25459.22	77.6162	19.2490
1318876.234	32.9348	0.2707	47047.34	85.1753	35.4994
1868097.836	31.8381	3.0129	26433.24	74.1792	18.6375
1695218.566	31.0794	23.4630	33396.66	81.6991	41.1130
2700194.415	32.1807	0.7041	26179.36	73.4140	17.8566
1156049.774	31.6944	-0.1569	33454.64	73.7161	26.5426
643858.444	34.0263	0.7084	42271.50	78.6493	29.8734
2188687.363	34.7315	0.1353	46514.75	80.9503	24.5374
830351.940	30.5613	0.3848	27030.81	66.8057	14.1390
1226905.572	33.5183	0.7417	42910.08	77.8905	20.8340
566903.589	32.3952	0.6693	40561.40	79.3622	19.0309
826518.398	29.9108	0.1111	22325.96	58.3610	10.6729

1) Can demographic information be helpful in

predicting sales at sporting goods stores? The file

contains the monthly sales totals from a random sample of

38 stores in a large chain of nationwide sporting goods

stores. All stores in the franchise, and thus within the

sample, are approximately the same size and carry the same

merchandise. The county or, in some cases, counties in

which the store draws the majority of its customers is

referred to here as the customer base. For each of the 38

stores, demographic information about the customer base is

provided. The data are real, but the name of the franchise is

not used, at the request of the company. The data set

contains the following variables:

Sales—Latest one-month sales total

(dollars)

Age—Median age of customer base (years)

HS—Percentage of customer base with a high school

diploma

College—Percentage of customer base with a college

diploma

Growth—Annual population growth rate of customer

base over the past 10 years

Income—Median family income of customer base

(dollars)

a. Construct a scatter plot, using sales as the dependent

variable and median family income as the independent

variable. Discuss the scatter plot.

b. Assuming a linear relationship, use the least-squares

method to compute the regression coefficients and

c. Interpret the meaning of the Y intercept, , and the

slope, , in this problem.

d. Compute the coefficient of determination, , and interpret

its meaning.

e. Perform a residual analysis on your results and determine

the adequacy of the fit of the model.

f. At the 0.05 level of significance, is there evidence of a

linear relationship between the independent variable and

the dependent variable?

2- For the data of Problem 13.85, repeat (a) through (f), using Age as the independent variable.

3 – For the data of Problem 13.85, repeat (a) through(f), using HS as the independent variable.

4 – For the data of Problem 13.85, repeat (a) through (f), using College as the independent variable.

Use Excel, StatCrunch, or any other statistical packages to solve the following

4

problems

Develop a model to predict the assessed value (in

thousands of dollars), using the size of the houses (in thousands

of square feet) and the age of the houses (in years) from the following table

18

5.9

1.93

2.00

0.00

0.00

2.75

1.59

Assessed Value	Heating Area	Age
1 8 4 .4		2 . 0 0	3 .42
1 7 7.4	1.71	1 1. 5 0
1 75 .7	1.45	8.33
	1 85 . 9	1.7 6			0.00
1 79 .1		1. 93	7.42
1 70 .4	1. 20	32.00
17 5.8	1.55	16 .00
1 78 .5		1. 59	1.75
179.2	1.50		2.75
1 86 .7	1.90
179.3	1.39
1 74 .5	1.54	12 .58
1 83 .8	1.89
176.8	7.17

a. State the multiple regression equation.

b. Interpret the meaning of the slopes in this equation.

c. Predict the assessed value for a house that has a size of

1,750 square feet and is

10

years old.

d. Perform a residual analysis on the results and determine

whether the regression assumptions are valid.

e. Determine whether there is a significant relationship between

assessed value and the two independent variables

(size and age) at the 0.05 level of significance.

f. Determine the p-value in (e) and interpret its meaning.

g. Interpret the meaning of the coefficient of multiple determination

in this problem.

h. Determine the adjusted

i. At the 0.05 level of significance, determine whether

each

independent variable makes a significant contribution to

the regression model. Indicate the most appropriate

regression

model for this set of data.

j. Determine the p-values in (i) and interpret their meaning.

2)

14

.73 Crazy Dave, a well-known baseball analyst, wants

to determine which variables are important in predicting a

team’s wins in a given season. He has collected data related

to wins, earned run average (ERA), and runs scored for the

2009 season. Develop a model to predict

the number of wins based on ERA and runs scored.

65

86

4.26

75

86

87

4.29

95

4.83

70

4.45

75

	Team		Wins		E.R.A.	Runs Scored
	Baltimore		64		5. 15	741
	Boston				95		4.3 5	87 2
	Chicago White Sox		79		4.1 4	724
	Cleveland				65		5.06	773
	Detroit						86				4.2 9	743
	Kansas City				4.8 3	686
	Los Angeles Angels		97				4.4 5	88 3
	Minnesota		4.5 0	817
	New Y ork Yankees		103				4.26	91 5
	Oakland						75	759
	Seattle		85		3.8 7	640
	Tampa Bay		84		4.33	80 3
	Texas				87		4.38	764
	Toronto		4.47	798
	Arizona				70		4.42	720
	Atlanta		3.5 7	735
	Chicago Cubs		83		3.84	707
	Cincinnati		78		4.18	673
	Colorado		92		4. 22	804
	Florida	772
	Houston		74		4.54	643
	Los Angeles Dodgers		3.41	780
	Milwaukee		80	785
	New York Mets	671
	Philadelphia		93		4.16	820
	Pittsburgh		62		4.59	636
	St. Louis		91		3.66	7 30
	San Diego		4.37	638
	San Francisco		88		3.55	657
	Washington		59		5.00	710

a. State the multiple regression equation.
b. Interpret the meaning of the slopes in this equation.

c. Predict the number of wins for a team that has an ERA

of 4.50 and has scored 750 runs.

d. Perform a residual analysis on the results and determine
whether the regression assumptions are valid.

e. Is there a significant relationship between number of

wins and the two independent variables (ERA and runs

scored) at the 0.05

level of significance?

f. Determine the p-value in (e) and interpret its meaning.
g. Interpret the meaning of the coefficient of multiple determination
in this problem.
h. Determine the adjusted
i. At the 0.05 level of significance, determine whether

each independent variable makes a significant contribution

to the regression model. Indicate the most appropriate

regression model for this set of data.
j. Determine the p-values in (i) and interpret their meaning.

3) Referring to Problem 2, suppose that in addition to

using ERA to predict the number of wins, Crazy Dave wants to

include the league ( American, National) as an independent

variable. Develop a model to predict wins based on ERA

and league. For (a) through (f), do not include an interaction term.

a. State the multiple regression equation.

b. Interpret the slopes in (a).

c. Predict the number of wins for a team with an ERA of

4.50 in the American League. Construct a 95%

confidence interval estimate for all teams and a 95%

prediction interval for an individual team.

d. Perform a residual analysis on the results and determine
whether the regression assumptions are valid.

e. Is there a significant relationship between wins and the

two independent variables (ERA and league) at the 0.05

level of significance?

f. At the 0.05 level of significance, determine whether each

independent variable makes a contribution to the regression

model. Indicate the most appropriate regression

model for this set of data.

Team

Wins

E.R.A.

Baltimore

64

5.15

Boston

95

4.35

0

Chicago White Sox

79

4.14

0

Cleveland

65

5.06

0

Detroit

86

4.29

0

Kansas City

65

4.83

0

Los Angeles Angels

97

4.45

0

Minnesota

86

4.50

0

New York Yankees

103

4.26

0

Oakland

75

4.26

0

Seattle

85

3.87

0

Tampa Bay

84

4.33

0

Texas

87

4.38

0

Toronto

75

4.47

0

Arizona

70

4.42

Atlanta

86

3.57

1

Chicago Cubs

83

3.84

1

Cincinnati

78

4.18

1

Colorado

92

4.22

1

Florida

87

4.29

1

Houston

74

4.54

1

Los Angeles Dodgers

95

3.41

1

Milwaukee

80

4.83

1

New York Mets

70

4.45

1

Philadelphia

93

4.16

1

Pittsburgh

62

4.59

1

St. Louis

91

3.66

1

San Diego

75

4.37

1

San Francisco

88

3.55

1

Washington

59

5.00

1

League

0

1

4) Last year, we contacted a small survey of 27 undergraduate students regarding their school performances (Grade Point Averages) and possible factors which might influence their Grade Point Averages (GPA). The accompanying file summarized the results. Suppose we show the grade point averages by Y, the number of hours per week spent studying by

X1

, the average number of hours spent preparing for tests by

X2

, the number of hours per week spend in Cafeterias by

X3

, whether students take notes or mark highlights when reading texts by

X4

(X4 = 1 if yes, 0 if no), and the average number of credit hours taken per semester by

X5

. Develop a multiple regression model based on the above variables. Fully discus your model by using at least four criteria to evaluate multiple regression models.

1

1

1

0

15

3.8

15

2

1

15

3

4

0

15

4.3

5

3

1

3.8

6

1

17

4.3

3

5

1

4

30

2

6

1

19

3.8

20

6

6

0

15

10

4

7

0

16

3

5

1

15

10

3

4

0

16

18

3

4

0

17

2

4

1

16

12

1

4

0

17

10

10

3

1

17

4

1

4

0

15

15

4

6

1

12

2

7

0

17

28

4

6

1

15

4.3

10

8

5

1

15

5

25

4

5

1

19

3

25

1

4

1

16

30

3

6

1

18

4.1

25

3

7

1

17

4.6

25

4

7

1

15

Y	X1	X2	X3	X4	X5
4.8					25							5								6					16
			4.3	22					2												15
			3.8	9											3															4
	8							17
4.2
		30	13
	20					7
					10			19
3.1
3.9			18
3.2
4.9
4.4			12
4.5
	4.6
	28
3.7	14
3.5
2.8
	4.1