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...

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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?






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Answered Same DayMay 08, 2021

Answer To: Instructions: The Excel file for this assignment contains a database with information about the tax...

Suraj answered on May 09 2021
139 Votes
Introduction: The main objective of this assignment is to make a model to predict the value of the response variable. The data set consist of 5 different variables out of them 4 are independent variable and 1 variable is response variable. The description about the variables is given as follows:
Floor Area: square feet of floor space
Offices: number of offices in the building
Entrances: number of customer entrances
Age: age of the building (years)
Assessed Value: tax assessment value (thousands of dollars)

The above top 4 variables are the independent variables and last variable Assessed value is response variable.
The scatter plot between the Floor area and the Assessed value is given as follows:
Here, we can see that there is a linear positive relationship between both the variables.
The linear regression equation is given as follows:
Thus, the regression equation is given as follows:
Assessed Value = 162.66 + 0.3067*Floor Area (Sq. Ft.)
The slope coefficient is 0.3067. It interprets that with increase of 1 unit in the independent variable the dependent variable is increased by 0.3067.
The p-value corresponding to the coefficient of Floor Area is 0. Which indicates that is a significant variable for the Floor Area coefficient.
The coefficient of determination that...
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