Instructions: The Excel File For This Assignment Contains Da

Instructionsthe Excel File For This Assignment Contains A Database Wi

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, Offices, Entrances, Age, AssessedValue. 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 AssessmentValue. 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?

Paper For Above instruction

The goal of this analysis is to develop a predictive model for the tax assessment value of medical office buildings based on various characteristics, utilizing multiple regression techniques within Excel. The process involves exploratory data visualization, individual predictor assessment, and the construction of a comprehensive multivariate model to elucidate the relationships between building features and assessed values.

Initially, a scatter plot between FloorArea and AssessmentValue reveals the potential linear relationship. Based on the visual and statistical analysis, including the linear regression equation and R-squared value, one can determine the strength and nature of this relationship. Typically, a positive correlation suggests that larger floor areas tend to have higher assessment values. The regression analysis using Excel’s Analysis ToolPak confirms whether FloorArea significantly influences AssessmentValue, which is indicated by the p-value associated with its coefficient.

Next, the relationship between Age and AssessmentValue is examined similarly through scatter plots and regression analysis. If the scatter plot indicates linearity, and the regression results yield statistically significant coefficients, Age can be considered a predictor. However, it is often found that newer buildings (less age) might have higher or lower assessments, depending on other factors like location and condition.

Furthermore, a multiple regression model incorporating all key variables—FloorArea, Offices, Entrances, and Age—provides a comprehensive understanding of their combined effects and overall predictive power. The goodness-of-fit measures, particularly the R-squared and adjusted R-squared, indicate how well the model explains the variability in assessment values. Significance of individual predictors is assessed by their p-values; non-significant variables can be eliminated to simplify the model without sacrificing accuracy.

For practical application, a streamlined model including only FloorArea and Offices is proposed: AssessedValue = 115.9 + 0.26 FloorArea + 78.34 Offices. Using this model, the assessed value of a building with specified features (e.g., 3500 sq. ft., 2 offices, built 15 years ago) can be estimated. Substituting these values yields an assessed value, which should be checked against the observed data distribution to validate the model’s consistency.

In conclusion, the step-by-step regression analysis provides insights into the significance and predictive power of various building characteristics, enabling accurate valuation and informed decision-making regarding medical office properties.

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