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Edward Currently Manufactures a Subcomponent That Is Used in Its

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Edward currently manufactures a subcomponent that is used in its main product.A supplier has offered to supply all the subcomponents needed at a price of $22.Edward currently produces 100,000 subcomponents at the following manufacturing costs:
Edward currently manufactures a subcomponent that is used in its main product.A supplier has offered to supply all the subcomponents needed at a price of $22.Edward currently produces 100,000 subcomponents at the following manufacturing costs:    a.If Edward has no alternative uses for the manufacturing capacity,what would be the profit impact of buying the subcomponents from the supplier? b.If Edward has no alternative uses for the manufacturing capacity,what would be the maximum price per unit they would be willing to pay the supplier? c.Now assume Edward would avoid $300,000 in equipment leases and salaries if the subcomponent were purchased from the supplier.Now what would be the profit impact of buying from the supplier? a.If Edward has no alternative uses for the manufacturing capacity,what would be the profit impact of buying the subcomponents from the supplier?
b.If Edward has no alternative uses for the manufacturing capacity,what would be the maximum price per unit they would be willing to pay the supplier?
c.Now assume Edward would avoid $300,000 in equipment leases and salaries if the subcomponent were purchased from the supplier.Now what would be the profit impact of buying from the supplier?


Definitions:

Multiple Regression Model

A statistical technique that uses several explanatory variables to predict the outcome of a response variable, allowing analysis of the effects of the variables on the response.

Independent Variable

A variable in an experiment or study that is manipulated or categorized to observe its effect on a dependent variable.

Adjusted R Square

A statistical measure that indicates the proportion of variance in the dependent variable predictable from the independent variable(s), adjusted for the number of predictors in the model.

Variance

The square of the standard deviation, indicating how much the values in a set vary from the mean.

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