The sales manager of a large automotive parts distributor wants to estimate the total annual sales for each of the company’s regions. Five factors appear to be related to regional sales: the number of retail outlets in the region, the number of automobiles in the region registered as of April 1, the total personal income recorded in the first quarter of the year, the average age of the automobiles (years), and the number of sales supervisors in the region. The data for each region were gathered for last year. For example, see the following table. In region 1 there were 1,739 retail outlets stocking the company’s automotive parts, there were 9,270,000 registered automobiles in the region as of April 1, and so on. The region’s sales for that year were $37,702,000. constant outlet automobile reg income age bosses 37.702 1,739 9.27 85.4 3.5 9 24.196 1,221 5.86 60.7 5 5 32.055 1,846 8.81 68.1 4.4 7 3.611 120 3.81 20.2 4 5 17.625 1,096 10.31 33.8 3.5 7 45.919 2,290 11.62 95.1 4.1 13 29.6 1,687 8.96 69.3 4.1 15 8.114 241 6.28 16.3 5.9 11 20.116 649 7.77 34.9 5.5 16 12.994 1,427 10.92 15.1 4.1 10 GIVEN: sales outlets outlets 0.899 cars income age automobiles 0.605 0.775 income 0.964 0.825 0.409 age −0.323 −0.489 −0.447 −0.349 bosses 0.286 0.183 0.395 0.155 0.291 Image attached is also fully correct! FIND: Conduct a test of hypothesis on each of the independent variables. Would you consider eliminating "outlets" and "bosses"? Use the 0.05 significance level. (Negative amounts should be indicated by a minus sign. Round your answers to 3 decimal places.) Delete "outlets" and 'bosses". Critical values are and .

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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The sales manager of a large automotive parts distributor wants to estimate the total annual sales for each of the company’s regions. Five factors appear to be related to regional sales: the number of retail outlets in the region, the number of automobiles in the region registered as of April 1, the total personal income recorded in the first quarter of the year, the average age of the automobiles (years), and the number of sales supervisors in the region. The data for each region were gathered for last year. For example, see the following table. In region 1 there were 1,739 retail outlets stocking the company’s automotive parts, there were 9,270,000 registered automobiles in the region as of April 1, and so on. The region’s sales for that year were $37,702,000.

constant	outlet	automobile reg	income	age	bosses
37.702	1,739	9.27	85.4	3.5	9
24.196	1,221	5.86	60.7	5	5
32.055	1,846	8.81	68.1	4.4	7
3.611	120	3.81	20.2	4	5
17.625	1,096	10.31	33.8	3.5	7
45.919	2,290	11.62	95.1	4.1	13
29.6	1,687	8.96	69.3	4.1	15
8.114	241	6.28	16.3	5.9	11
20.116	649	7.77	34.9	5.5	16
12.994	1,427	10.92	15.1	4.1	10

GIVEN:

	sales	outlets
outlets	0.899		cars	income	age
automobiles	0.605	0.775
income	0.964	0.825	0.409
age	−0.323	−0.489	−0.447	−0.349
bosses	0.286	0.183	0.395	0.155	0.291

** Image attached is also fully correct!**

FIND: Conduct a test of hypothesis on each of the independent variables. Would you consider eliminating "outlets" and "bosses"? Use the 0.05 significance level. (Negative amounts should be indicated by a minus sign. Round your answers to 3 decimal places.) Delete "outlets" and 'bosses". Critical values are ____________ and ____________.

SE
Predictor
Coefficient
Coefficient
Constant
-19.672
5.422
-3.63
0.022
Outlets
-0.000629
0.002638
-0.24
0.823
Automobiles
1.7399
0.5530
3.15
0.035
Income
0.40994
0.04385
9.35
0.001
Age
2.0357
0.8779
2.32
0.081
Bosses
-0.0344
0.1880
-0.18
0.864
Analysis of Variance
Source
DF
SS
MS
F
Regression
Residual Error
5
1,593.81
318.76
140.36 0.0001
4
9.08
2.27
Total
1602.89
R2
0.9942

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level of significance = 0.05

Predictor variables : Outlets , Automobile, Income, Age, Bosses

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