Instructions: The Excel File For This Assignment Contains A

Instructionsthe Excel File For This Assignment Contains A Database Wi

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 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 Floor Area and Offices as predictors? Suppose our final model is: Assessed Value = 115.9 + 0.26 x Floor Area + 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 process of constructing predictive regression models for assessing the value of medical office buildings plays a vital role in real estate valuation and urban planning. This paper discusses methodologies and interpretations associated with modeling the tax assessment value using various building characteristics. Using the variables provided—such as FloorArea, Offices, Entrances, and Age—regression analysis helps in understanding the significance of each predictor and developing an accurate predictive model.

Initially, the exploration begins with bivariate scatter plots to examine the relationship between individual predictors and the assessment value, followed by simple linear regression analyses. By plotting FloorArea against AssessedValue, a clear visualization emerges to determine if a linear relationship exists. In practice, effective modeling depends on examining the regression equation and R-squared values to comprehend the strength and nature of the relationships. These steps provide foundational insights into the degree to which FloorArea alone predicts the assessment value.

Similarly, the relationship between building Age and AssessedValue provides an understanding of whether the age impacts the valuation significantly. Regression analysis quantifies this relationship, with p-values indicating the significance. A significant predictor typically exhibits a low p-value (

Moving beyond simple linear models, multiple regression analysis incorporates all relevant predictors—FloorArea, Offices, Entrances, and Age—to enhance predictive accuracy. The overall fit of the model is expressed through R-squared and adjusted R-squared, which account for the number of predictors and degrees of freedom, providing a more nuanced understanding of model quality. Significance tests on individual predictors identify which variables meaningfully contribute to the model. Predictors with high p-values (>0.05) may be candidates for exclusion, simplifying the model without sacrificing accuracy.

The refined final model typically retains only significant predictors, such as FloorArea and Offices—variables demonstrating strong statistical significance. For example, a final model like: AssessedValue = 115.9 + 0.26 × FloorArea + 78.34 × Offices enables estimation of valuation for specific buildings. Applying this model to a building with known characteristics—say, 3500 sq. ft., 2 offices, and 15 years of age—yields an estimated assessed value, which should be compared to actual database values for validation.

In conclusion, regression analysis provides a comprehensive framework for modeling property assessments based on building characteristics. The systematic evaluation of predictors, significance testing, and model refinement constitute essential steps for developing reliable valuation tools that support economic decision-making, urban planning, and property management.

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