week 5_ regression Modeling

Enactment Content

Purpose 

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This enactment provides an occasion to unfold, evaluate, and exercise bivariate and multivariate straight retirement moulds.

Resources: Microsoft Excel®, DAT565_v3_Wk5_Data_File

Instructions:

The Excel polish coercion this enactment contains a factsbase with advice about the tribute tribute rate assigned to medical appointment composeions in a city. The forthcoming is a inventory of the changeables in the factsbase:

BottomArea: clear feet of bottom space
Appointments: reckon of appointments in the composeion
Entrances: reckon of customer entrances
Period: period of the composeion (years)
AssessedValue: tribute tribute rate (thousands of dollars)
Representation the facts to compose a mould that predicts the tribute tribute rate assigned to medical appointment composeions with biased characteristics.

Compose a plant batch in Excel with BottomArea as the fractions changeable and TributeRate as the trusting changeable. Insert the bivariate straight retirement equation and r^2 in your graph. Do you respect a straight alliance betwixt the 2 changeables?
Representation Excel’s Resolution ToolPak to guide a retirement resolution of BottomArea and TributeValue. Is BottomArea a telling predictor of TributeValue?
Compose a plant batch in Excel with Period as the fractions changeable and TributeRate as the trusting changeable. Insert the bivariate straight retirement equation and r^2 in your graph. Do you respect a straight alliance betwixt the 2 changeables?
Representation Excel’s Resolution ToolPak to guide a retirement resolution of Period and Tribute Rate. Is Period a telling predictor of TributeValue?
Compose a multiple retirement mould.

Representation Excel’s Resolution ToolPak to guide a retirement resolution with TributeRate as the trusting changeable and BottomArea, Appointments, Entrances, and Period as fractions changeables. What is the overall become r^2? What is the adjusted r^2?
Which predictors are considered telling if we effect with a=0.05? Which predictors can be eliminated?
What is the latest mould if we barely representation BottomArea and Appointments as predictors?
Suppose our latest mould is:
AssessedRate = 115.9 + 0.26 x BottomArea + 78.34 x Appointments
What wouldbe the assessed rate of a medical appointment composeion with a bottom area of 3500 sq. ft., 2 appointments, that was built 15 years since? Is this assessed rate consonant with what appears in the factsbase?
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