On the basis of the sample results, the regression equation is: = 62.65 – 1.86x1 – 0.52x2 a. How much natural gas can homeowners expect to use per month if they install 6 inches of insulation and the outdoor temperature is 40 degrees F? (Round your answer to 2 decimal places.) Natural gas 30.69 cubic feet
On the basis of the sample results, the regression equation is: = 62.65 – 1.86x1 – 0.52x2 a. How much natural gas can homeowners expect to use per month if they install 6 inches of insulation and the outdoor temperature is 40 degrees F? (Round your answer to 2 decimal places.) Natural gas 30.69 cubic feet
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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Cellulon, a manufacturer of home insulation, wants to develop guidelines for builders and consumers on how the thickness of the insulation in the attic of a home and the outdoor temperature affect natural gas consumption. In the laboratory, it varied the insulation thickness and temperature. A few of the findings are:

Transcribed Image Text:Monthly Natural Gas
Consumption (cubic
feet),
Thickness of
Outdoor
Insulation (inches), Temperature (°F),
X1
X2
30.3
6.
40
26.9
12
40
22.1
49
On the basis of the sample results, the regression equation is:
ŷ = 62.65 – 1.86x1 – 0.52x2
a. How much natural gas can homeowners expect to use per month if they install 6 inches of insulation and the outdoor temperature is
40 degrees F? (Round your answer to 2 decimal places.)
Natural gas
30.69 cubic feet
b. What effect would installing 7 inches of insulation instead of 6 have on the monthly natural gas consumption (assuming the outdoor
temperature remains at 40 degrees F)? (Round your answers to 2 decimal places.)
A decrease
of
cubic feet of monthly natural gas consumption, down to
28.83 cubic feet.
Expert Solution

Step 1
Suppose y=a0+a1 x1+ a2 x2 is the fitted regression line, then expected value of the dependent variable y for given values of x1 and x2 is obtained by plugging in the values of x1 and x2 in the fitted regression line.
(a). At x1 = 6, x2 = 40, the expected value of gas consumption is:
y^ = 62.65-1.866 -0.5240
= 30.69
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