Question

Companies considering the purchase of a computer must first assess their future needs in order to determine the proper equipment. A computer scientist collected data from seven similar company sites so that computer hardware requirements for inventory management could be developed. The data collected is as follows:

Customer Orders
(in thousands)		 Add-delete items
(in thousands)		 CPU time
(in hours)
123.5 2.108 141.5
146.1 9.213 168.9
133.9 1.905 154.8
128.5 0.815 146.5
151.5 1.061 172.8
136.2 8.603 160.1
92.0 1.125 108.5

 i) Find a linear regression equation that best fit the data.

 ii) Estimate the error variance for the regression model obtained in i) above.

08 Mar 2024
Answer :
Word Count : 559
To solve this numerically, we will perform the following steps: ### Step 1: Organize the Data The data is structured into three columns: - Customer Orders (Y, in thousands) - Add-delete items (X₁, in thousands) - CPU time (X₂, in hours) Here is the data summarized: | Customer Orders (Y) | Add-delete items (X₁) | CPU time (X₂) | |---------------------|-----------------------|---------------| | 123.5 | 2.108 | 141.5 | | 146.1 | 9.213 | 168.9 | | 133.9 | 1.905 | 154.8 | | 128.5 | 0.815 | 146.5 | | 151.5 | 1.061 | 172.8 | | 136.2 | 8.603 | 160.1 | | 92.0 | 1.125 | 108.5 | ### Step 2: Fit a Multiple Linear Regression Model The multiple linear regression model is given by: \[ Y = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \epsilon \] ____ _______ _______ _________ _________ ______.
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Companies considering the purchase of a computer must first assess their future needs in order to determine the proper equipment. A computer scientist collected data from seven similar company sites so that computer hardware requirements for inventory management could be developed. The data collected is as follows:

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(in thousands)		 Add-delete items
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(in hours)
123.5 2.108 141.5
146.1 9.213 168.9
133.9 1.905 154.8
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151.5 1.061 172.8
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92.0 1.125 108.5

 i) Find a linear regression equation that best fit the data.

 ii) Estimate the error variance for the regression model obtained in i) above.

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