The following is a list of the variables in the database: The Excel file for this assignment contains a database with information about the tax assessment value assigned to medical office buildings in a city. Resources: Microsoft Excel®, DAT565_v3_Wk5_Data_File first appeared on COMPLIANT PAPERS.Develop, evaluate, and apply bivariate and multivariate linear regression models. The post Bivariate and multivariate linear regression models. ft., 2 offices, that was built 15 years ago? Is this assessed value consistent with what appears in the database? What wouldbe the assessed value of a medical office building with a floor area of 3500 sq. What is the final model if we only use FloorArea and Offices as predictors?ĪssessedValue = 115.9 + 0.26 x FloorArea + 78.34 x Offices
Which predictors are considered significant if we work with α=0.05? Which predictors can be eliminated? What is the overall fit r^2? What is the adjusted r^2? 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. Is Age a significant predictor of AssessmentValue? Use Excel’s Analysis ToolPak to conduct a regression analysis of Age and Assessment Value. Do you observe a linear relationship between the 2 variables? Insert the bivariate linear regression equation and r^2 in your graph. Is FloorArea a significant predictor of AssessmentValue?Ĭonstruct a scatter plot in Excel with Age as the independent variable and AssessmentValue as the dependent variable. Use Excel’s Analysis ToolPak to conduct a regression analysis of FloorArea and AssessmentValue. Use the data to construct a model that predicts the tax assessment value assigned to medical office buildings with specific characteristics.Ĭonstruct a scatter plot in Excel with FloorArea as the independent variable and AssessmentValue as the dependent variable. Offices: number of offices in the buildingĪssessedValue: tax assessment value (thousands of dollars) Develop, evaluate, and apply bivariate and multivariate linear regression models.
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