# week 5_ regression Modeling

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Purpose

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This enactment provides an turn to clear, evaluate, and adduce bivariate and multivariate straight retirement designs.

Resources: Microsoft Excel®, DAT565_v3_Wk5_Data_File

Instructions:

The Excel rasp restraint this enactment contains a groundsbase with advice about the rate impost compute assigned to medical duty fabrics in a city. The forthcoming is a catalogue of the inconstants in the groundsbase:

BottomArea: balance feet of bottom space
Dutys: calculate of dutys in the fabric
Entrances: calculate of customer entrances
Date: date of the fabric (years)
AssessedValue: rate impost compute (thousands of dollars)
Truth the grounds to erect a design that predicts the rate impost compute assigned to medical duty fabrics with restricted characteristics.

Erect a strew batch in Excel with BottomArea as the recalcitrant inconstant and ImpostCompute as the trusting inconstant. Insert the bivariate straight retirement equation and r^2 in your graph. Do you note a straight alliance betwixt the 2 inconstants?
Truth Excels Segregation ToolPak to influence a retirement segregation of BottomArea and ImpostValue. Is BottomArea a weighty predictor of ImpostValue?
Erect a strew batch in Excel with Date as the recalcitrant inconstant and ImpostCompute as the trusting inconstant. Insert the bivariate straight retirement equation and r^2 in your graph. Do you note a straight alliance betwixt the 2 inconstants?
Truth Excels Segregation ToolPak to influence a retirement segregation of Date and Impost Compute. Is Date a weighty predictor of ImpostValue?
Erect a multiple retirement design.

Truth Excels Segregation ToolPak to influence a retirement segregation with ImpostCompute as the trusting inconstant and BottomArea, Dutys, Entrances, and Date as recalcitrant inconstants. What is the overall accord r^2? What is the adjusted r^2?
Which predictors are considered weighty if we operation with a=0.05? Which predictors can be eliminated?
What is the conclusive design if we singly truth BottomArea and Dutys as predictors?
Suppose our conclusive design is:
AssessedCompute = 115.9 + 0.26 x BottomArea + 78.34 x Dutys
What wouldbe the assessed compute of a medical duty fabric with a bottom area of 3500 sq. ft., 2 dutys, that was built 15 years gone? Is this assessed compute compatible with what appears in the groundsbase?

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