DATCB/565: Data Analysis And Business Analytics Competency 3 – Reflection

You will also be able to create forecasts and interpret data to analyze performance as it impacts strategic planning and comparative advantage for an organization.
Manipulating data to create models helps us describe and summarize relationships between variables. Understanding how variables relate to each other helps businesses predict performance and make informed strategic plans. For example, to make an informed recommendation to management regarding which types of office buildings to acquire or sell, you would model the relationship between assessed value and given variables.
This reflection gives you an opportunity to develop, evaluate, and apply bivariate and multivariate linear regression models, and then reflect on office buildings you recommend acquiring and selling, and why.
Pre-Reflection Exercise
Download the Competency 3 Reflection Data Set. The data set is 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)
As you work through the following exercises, note your answers to the given questions so you can easily summarize them in your reflection.
Use the data set to construct a model that predicts the tax assessment value assigned to medical office buildings with specific characteristics.
1. 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 R2 in your graph.
Do you observe a linear relationship between the 2 variables?
2. Use Excel’s Analysis ToolPak to conduct a regression analysis of FloorArea and AssessmentValue.
Is FloorArea a significant predictor of AssessmentValue?
3. 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 R2 in your graph.
Do you observe a linear relationship between the 2 variables?
4. 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 R2? What is the adjusted R2?
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?
Reflection
In a minimum of 500 words, reflect on the types of medical offices you would advise management to close and open, and why. Use your exercise notes to support your rationale.