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QUESTION 9 Table: Sherwin-Williams Company new Data Observations Sales Promotional Expenditures Selling Price Disposable Income y A P M 1 160 100 15 19 2 200 160 14 17.5 3 140 50 16.5 14 4 190 190 14.5 20 5 130 90 17 15.5 6 160 70 16 14.5 7 200 140 15 21.5 8 150 110 18 18 9 210 200 12 18.5 10 190 100 15.5 20 Total 1750 1250 151 179.5 Mean 175 125 15.1 17.95 What is regression equation for paint sales using multiple linear regression model? $Y = 240.3 + 0.05867A - 10.0P + 4.435M$ $Y = 240.3 + 0.04867A - 10.0P + 4.235M$ $Y = 240.3 + 0.05867A + 8.0P + 4.435M$ None of above

          QUESTION 9
Table: Sherwin-Williams Company new Data
Observations
Sales
Promotional Expenditures Selling Price
Disposable Income
y
A
P
M
1
160
100
15
19
2
200
160
14
17.5
3
140
50
16.5
14
4
190
190
14.5
20
5
130
90
17
15.5
6
160
70
16
14.5
7
200
140
15
21.5
8
150
110
18
18
9
210
200
12
18.5
10
190
100
15.5
20
Total
1750
1250
151
179.5
Mean
175
125
15.1
17.95
What is regression equation for paint sales using multiple linear regression model?
$Y = 240.3 + 0.05867A - 10.0P + 4.435M$
$Y = 240.3 + 0.04867A - 10.0P + 4.235M$
$Y = 240.3 + 0.05867A + 8.0P + 4.435M$
None of above

Show more…

QUESTION 9
Table: Sherwin-Williams Company new Data
Observations
Sales
Promotional Expenditures Selling Price
Disposable Income
y
A
P
M
1
160
100
15
19
2
200
160
14
17.5
3
140
50
16.5
14
4
190
190
14.5
20
5
130
90
17
15.5
6
160
70
16
14.5
7
200
140
15
21.5
8
150
110
18
18
9
210
200
12
18.5
10
190
100
15.5
20
Total
1750
1250
151
179.5
Mean
175
125
15.1
17.95
What is regression equation for paint sales using multiple linear regression model?
Y = 240.3 + 0.05867A - 10.0P + 4.435M
Y = 240.3 + 0.04867A - 10.0P + 4.235M
Y = 240.3 + 0.05867A + 8.0P + 4.435M
None of above

Added by Thomas D.

Principles of Economics

Gregory Mankiw 8th Edition

Instant Answer

Solved by Expert Lucas Finney

06/13/2022

Step 1

Step 1: Identify the variables in the regression equation: - Sales (Y) is the dependent variable - Promotional Expenditures (A), Selling Price (P), and Disposable Income (M) are the independent variables Show more…

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QUESTION 9 Table: Sherwin-Williams Company new Data able[[Observations,Sales,Promotional Expenditures,Selling Price,Disposable Income],[i,y,A,P,M],[1,160,100,15,19],[2,200,160,14,17.5],[3,140,50,16.5,14],[4,190,190,14.5,20],[5,130,90,17,15.5],[6,160,70,16,14.5],[7,200,140,15,21.5],[8,150,110,18,18],[9,210,200,12,18.5],[10,190,100,15.5,20],[,,,151,],[Total,1750,1250,15.1,179.5],[Mean,175,125,17.95,]] What is regression equation for paint sales using multiple linear regression model? Y=240.3+.05867^(***)A-10.0^(**)P+4.435^(***)M Y=240.3+.04867^(***)A-10.0^(***)P+4.235^(***)M Y=240.3+.05867^(**)A+8.0^(**)P+4.435^(***)M None of above QUESTION 9 Table:Sherwin-Williams Company new Data Observations - 1 234 5678 9 10 Sales y 160 200 140 190 130 160 200 150 210 190 Promotional Expenditures Selling Price A P 100 15 160 52 14 50. 16.5 190 14.5 90 70 140 110 1162 200 100 15.5 Disposable Income M 19 17.5 14 20 15.5 14.5 21.5 18 18.5 20 Total Mean 1750 175 1250 125 151 15.1 179.5 17.95 What is regression equation for paint sales using multiple linear regression model? Y=240.3+.05867*A-10.0*P+4.435*M Y=240.3+.04867*A-10.0*P+4.235*M Y=240.3+.05867*A+8.0*P+4.435*M O None of above

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Transcript

00:01 For this problem, we are first asked to complete the given table.

00:06 So we can see that we are missing values for the total of the xys and the total of the y squared.

00:12 So i've already copied down the data into my software here.

00:18 So what we want to do then is i'm just going to put down sigma xy equals the total of x times y.

00:28 And that's 25 ,670, and sigma y2, it's going to be equal to total y2.

00:40 So that's 308 ,500.

00:44 Then we're asked to find our regression parameters.

00:47 So i'm just going to pull up the equation for a linear regression here.

00:52 So we have, let me find it.

00:56 Okay.

00:57 So we have that one, equals a plus b x where b is going to be equal to let's see here so that's n times sigma xy it's going to copy the sigma so it's easy for me to put that on minus the sum of the x's which is 151 times the sum of the y's which is 1730 and we have that all of this is going to be divided by n times the sum of x squared, where the sum of x squared is 2 ,312.

01:44 Then we'd have minus the sum of x's squared.

01:48 Sum of x's was 151.

01:51 That gives us our b value.

01:54 I'm just going to make a change, so it displays as a decimal.

01:56 It's negative 14 .206, or we can safely round that to negative 14 .2 .2.

02:02 And then for a equals the sum of y's, which is 1730, minus b times the sum of x's, 151, divided by n.

02:28 So we have that our a equals 387 .49.

02:35 So we can then say that our linear regression line is yve x equals a plus bx...

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