Question
In: Statistics and Probability
A computer manufacturer has developed a regression model relating his sales (Y in $10,000s) with three...
A computer manufacturer has developed a regression model relating his sales (Y in $10,000s) with three independent variables. The three independent variables are price per unit (Price in $100s), advertising (ADV in $1,000s) and the number of product lines (Lines). Part of the regression results is shown below.
|
Coefficient |
Standard Error |
||
|
Intercept |
1.0211 |
22.8752 |
|
|
Price |
-0.1524 |
0.1411 |
|
|
ADV |
0.8849 |
0.2886 |
|
|
Lines |
-0.1463 |
1.5340 |
|
|
Analysis of Variance |
|||
|
Source of Variation |
Degrees of Freedom |
Sum of Squares |
|
|
Regression |
2708.61 |
||
|
Error (Residuals) Required: |
14 |
2840.51 |
|
|
a. |
Use the above results and write the regression equation that can be used to predict sales. |
||
|
b. |
If the manufacturer has 10 product lines, advertising of $40,000, and the price per unit is $3,000, what is your estimate of their sales? |
||
|
c. |
Compute the coefficient of determination and fully interpret its meaning. |
||
|
d. |
At a = 0.05, test to see if there is a significant relationship between sales and unit price. |
||
|
e. |
At a = 0.05, test to see if there is a significant relationship between sales and the number of product lines. |
||
|
f. |
Is the regression model significant? |
||
|
g. |
Fully interpret the meaning of the regression (coefficient of price) per unit that is, the slope for the price per unit. |
||
Solutions
Expert Solution
The value of n is obtaind from ANOVA table:
Erro df= n-k-1=14
here k=3 (no. of independent variables)
hence, n=18
orchestra answered 1 year agoRelated Solutions
A computer manufacturer has developed a regression model relating his sales (Y in $10,000s) with three...
A computer manufacturer has developed a regression model
relating his sales (Y in $10,000s) with three independent
variables. The three independent variables are price per unit
(Price in $100s), advertising (ADV in $1,000s) and the number of
product lines (Lines). Part of the regression results is shown
below.
Coefficient
Standard Error
Intercept
1.0211
22.8752
Price
-0.1524
0.1411
ADV
0.8849
0.2886
Lines
-0.1463
1.5340
Analysis of Variance
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Degrees
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Sum of
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Regression
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Error (Residuals)
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