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SCENARIO 18-2 One of the Most Common Questions of Prospective House Buyers

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SCENARIO 18-2
One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y) . To provide its customers with information on that matter, a large real estate firm used the following 4 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit ( X1 ) , the amount of insulation in inches ( X 2 ) , the number of windows in the house ( X3 ) , and the age of the furnace in years ( X 4 ) . Given below are the EXCEL outputs of two regression models.
SCENARIO 18-2 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y) . To provide its customers with information on that matter, a large real estate firm used the following 4 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit ( X<sub>1</sub> ) , the amount of insulation in inches ( X <sub>2</sub> ) , the number of windows in the house ( X<sub>3</sub> ) , and the age of the furnace in years ( X <sub>4</sub> ) . Given below are the EXCEL outputs of two regression models.     -Referring to Scenario 18-1,at the 0.01 level of significance,what conclusion should the builder draw regarding the inclusion of School in the regression model? A) School is significant in explaining house size and should be included in the model because its p-value is less than 0.01. B) School is significant in explaining house size and should be included in the model because its p-value is more than 0.01. C) School is not significant in explaining house size and should not be included in the model because its p-value is less than 0.01. D) School is not significant in explaining house size and should not be included in the model because its p-value is more than 0.01.
SCENARIO 18-2 One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y) . To provide its customers with information on that matter, a large real estate firm used the following 4 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit ( X<sub>1</sub> ) , the amount of insulation in inches ( X <sub>2</sub> ) , the number of windows in the house ( X<sub>3</sub> ) , and the age of the furnace in years ( X <sub>4</sub> ) . Given below are the EXCEL outputs of two regression models.     -Referring to Scenario 18-1,at the 0.01 level of significance,what conclusion should the builder draw regarding the inclusion of School in the regression model? A) School is significant in explaining house size and should be included in the model because its p-value is less than 0.01. B) School is significant in explaining house size and should be included in the model because its p-value is more than 0.01. C) School is not significant in explaining house size and should not be included in the model because its p-value is less than 0.01. D) School is not significant in explaining house size and should not be included in the model because its p-value is more than 0.01.
-Referring to Scenario 18-1,at the 0.01 level of significance,what conclusion should the builder draw regarding the inclusion of School in the regression model?


Definitions:

Kanban Size

The quantity of work or materials specified by a Kanban card in a pull-based inventory or production control system.

Setup Cost

The expenses incurred to prepare equipment or processes for manufacturing an order, including cleaning and tooling costs.

Carrying Cost

The total cost of holding inventory, including storage, insurance, depreciation, and opportunity costs, synonymous with holding costs but with a rephrased definition for clarification.

Safety Stock

Inventory kept on hand to protect against stockouts caused by fluctuations in demand or supply chain disruptions.

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