Solution-Build a parsimonious linear regression model

Build a parsimonious linear regression model

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Reference no: EM133168903

The facility manager at a pharmaceutical company wants to build a regression model to forecast monthly electricity cost. Three main variables are thought to dictate electricity cost (in $): (1) average outdoor temperature (in F), (2) working days per month, and (3) tons of product produced. A portion of the past year's monthly data is provided in "Electricity Cost" datasheet.

Use the provided data to answer the following questions:

Build a parsimonious linear regression model to forecast the electricity cost given the available variables.
Interpret the meaning of the Intercept and the slop of the linear regression model.
Predict the electricity cost of the facility if the average outdoor temperature is 24 F, the working days per months is 24 days, and 79 tons of product are produced.
Calculate the correlation between the variables. Is there multicollinearity in the model, and what is its effect if it does exist? Comment on the forecasted value.
What are the limitations of the model?

Data:

Cost	Ave Temp	Work Days	Tons Produced
24100	26	24	80
23700	32	21	73
26800	46	24	90
27400	61	23	69
30200	66	24	75
31500	73	23	80
30100	81	24	82
29500	85	24	80
26800	76	23	78
27500	61	24	79
28900	51	23	75
26000	39	22	69

Reference no: EM133168903

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Build a parsimonious linear regression model

Reference no: EM133168903

Reference no: EM133168903

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