- Apply: Regressioodeling [dnt Purpose This assignment provides an opportunity to develop, evaluate, and apply bivariate and multivariate linear regression models.Resources: Microsoft Excel®, DAT565_

- Apply: Regressioodeling [dnt Purpose This assignment provides an opportunity to develop, evaluate, and apply bivariate and multivariate linear regression models.Resources: Microsoft Excel®, DAT565_v3_Wk5_Data_FileInstructions: 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 would be 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? APA Style