# [Instant Help From 10$/Pg] 65 Week 5 Data

[Instant Help From 10$/Pg] 65 Week 5 Data

**Purpose **

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Order Paper NowThis assignment provides an opportunity to develop, evaluate, and apply bivariate and multivariate linear regression models.

**Resources: **__Microsoft Excel® DAT5/65 Week 5 Data File__

**Instructions: **

The Excel file for this assignment contains a database with information about the tax assessment value assigned to medical office buildings in a city. The following is a list of the variables in the database:

*FloorArea*: square feet of floor space*Offices*: number of offices in the building*Entrances*: number of customer entrances*Age*: age of the building (years)*AssessedValue*: tax assessment value (thousands of dollars)

**Use** the data to construct a model that predicts the tax assessment value assigned to medical office buildings with specific characteristics.

- Construct a scatter plot in Excel with
*FloorArea*as the independent variable and*AssessmentValue*as the dependent variable. Insert the bivariate linear regression equation and r^2 in your graph. Do you observe a linear relationship between the 2 variables? - Use Excel’s Analysis ToolPak to conduct a regression analysis of
*FloorArea*and*AssessmentValue*. Is*FloorArea*a significant predictor of*AssessmentValue*? - Construct a scatter plot in Excel with
*Age*as the independent variable and*AssessmentValue*as the dependent variable. Insert the bivariate linear regression equation and r^2 in your graph. Do you observe a linear relationship between the 2 variables? - Use Excel’s Analysis ToolPak to conduct a regression analysis of Age and Assessment Value. Is
*Age*a significant predictor of*AssessmentValue*?

**Construct **a multiple regression model.

- Use Excel’s Analysis ToolPak to conduct a regression analysis with
*AssessmentValue*as the dependent variable and*FloorArea*,*Offices*,*Entrances*, and*Age*as independent variables. What is the overall fit r^2? What is the adjusted r^2? - Which predictors are considered significant if we work with α=0.05? Which predictors can be eliminated?
- What is the final model if we only use
*FloorArea*and Offices as predictors? - Suppose our final model is:
*AssessedValue*= 115.9 + 0.26 x*FloorArea*+ 78.34 x*Offices*- What wouldbe the assessed value of a medical office building with a floor area of 3500 sq. ft., 2 offices, that was built 15 years ago? Is this assessed value consistent with what appears in the database?