Problem #1 - Freeze Inc. is an air conditioning company located in Lakeland, Florida. They collected data for the number of air conditioning units sold in the Central Florida area and for the outside temperature on the day that sales took place. The Sales Manager put the following table together: Outside Temperature Sales (Degrees F) (Number of air conditioning units sold) 68 72 78 81 12 84 15 86 16 89 22 91 18 93 19 94 26 a) Plot scatter diagram for the data provided on the table above an the linear regression line calculated in topic (b). Consider that: Y: number of air conditioning units sold X: outside temperature (degrees F) Guidance: graph should look like the one presented in Figure 4.2 of textbook. Graph plotted by MS Excel, as a result of the Regression function will not be accepted. b) Perform the linear regression calculation and provide the linear regression equation that describes the relationship between Y (number of air conditioning units sold) and X (outside temperature in degrees Fahrenheit. Calculate: Coefficient of determination (r²) and the coefficient of correlation (r). SST, SSE and SSR for this linear regression. Estimate for the variance (o) and the standard deviation for the linear regression model you have developed Guidance: Follow the steps presented on Table 4.2 and 4.3 of textbook and show that your calculations followed that method. Do NOT present the resolution using MS Excel, because it will not count for this item. c) for a day that will reach 66 degrees F and for a day that will reach 95 F and for both temperature levels calculate Using the linear regression equation that you developed in topic (b), calculate the estimated sales the error "e" when comparing the estimated value against the actual data provided. At which of the two temperatures, is your model more accurate? Justify. d) Using MS Excel, verify your calculations made in item (b) assuming a 0.05, upload the MS Excel spreadsheet. You can use the linear regression function in MS Excel, if vou prefer.
Problem #1 - Freeze Inc. is an air conditioning company located in Lakeland, Florida. They collected data for the number of air conditioning units sold in the Central Florida area and for the outside temperature on the day that sales took place. The Sales Manager put the following table together: Outside Temperature Sales (Degrees F) (Number of air conditioning units sold) 68 72 78 81 12 84 15 86 16 89 22 91 18 93 19 94 26 a) Plot scatter diagram for the data provided on the table above an the linear regression line calculated in topic (b). Consider that: Y: number of air conditioning units sold X: outside temperature (degrees F) Guidance: graph should look like the one presented in Figure 4.2 of textbook. Graph plotted by MS Excel, as a result of the Regression function will not be accepted. b) Perform the linear regression calculation and provide the linear regression equation that describes the relationship between Y (number of air conditioning units sold) and X (outside temperature in degrees Fahrenheit. Calculate: Coefficient of determination (r²) and the coefficient of correlation (r). SST, SSE and SSR for this linear regression. Estimate for the variance (o) and the standard deviation for the linear regression model you have developed Guidance: Follow the steps presented on Table 4.2 and 4.3 of textbook and show that your calculations followed that method. Do NOT present the resolution using MS Excel, because it will not count for this item. c) for a day that will reach 66 degrees F and for a day that will reach 95 F and for both temperature levels calculate Using the linear regression equation that you developed in topic (b), calculate the estimated sales the error "e" when comparing the estimated value against the actual data provided. At which of the two temperatures, is your model more accurate? Justify. d) Using MS Excel, verify your calculations made in item (b) assuming a 0.05, upload the MS Excel spreadsheet. You can use the linear regression function in MS Excel, if vou prefer.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
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Just D please
Transcribed Image Text:Problem #1 - Freeze Inc. is an air conditioning company located in Lakeland, Florida. They collected data for the number
of air conditioning units sold in the Central Florida area and for the outside temperature on the day that sales took place.
The Sales Manager put the following table together:
Outside Temperature
Sales
(Degrees F)
(Number of air conditioning units sold)
68
3
72
78
7
81
12
84
15
86
16
89
22
91
18
93
19
94
26
a)
calculated in topic (b). Consider that:
Plot a scatter diagram for the data provided on the table above and the linear regression line
Y: number of air conditioning units sold
X: outside temperature (degrees F)
Guidance: graph should look like the one presented in Figure 4.2 of textbook. Graph plotted by MS Excel, as a
result of the Regression function will not be accepted.
b)
Perform the linear regression calculation and provide the linear regression equation that describes
the relationship between Y (number of air conditioning units sold) and X (outside temperature in degrees
Fahrenheit. Calculate:
Coefficient of determination (r?) and the coefficient of correlation (r).
SST, SSE and SSR for this linear regression.
Estimate for the variance (o?) and the standard deviation for the linear regression model you have developed
Guidance:
Follow the steps presented on Table 4.2 and 4.3 of textbook and show that your calculations followed that
method. Do NOT present the resolution using MS Excel, because it will not count for this item.
c)
for a day that will reach 66 degrees F and for a day that will reach 95 F and for both temperature levels calculate
the error "e" when comparing the estimated value against the actual data provided. At which of the two
Using the linear regression equation that you developed in topic (b), calculate the estimated sales
temperatures, is your model more accurate? Justify.
d)
Using MS Excel, verify your calculations made in item (b) assuming a = 0.05, upload the MS Excel
spreadsheet. You can use the linear regression function in MS Excel, if you prefer.
Expert Solution
Regression
Regression is a fitted numerical relationship between a response variable and explanatory variable(s). In the given data we fit a linear regression model to the given data with Sales as the response variable and temperature as the explanatory variable.
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