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Competency 3 Statement
Utilizing statistical regression and time series analysis models, you will be able to evaluate and analyze how multiple variables impact an organization. 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:
Floor Area: square feet of floor space
Offices: number of offices in the building
Entrances: number of customer entrances
Age: age of the building (years)
Assessed Value: 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 Floor Area as the independent variable and Assessment Value 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 Excels Analysis ToolPak to conduct a regression analysis of Floor Area and Assessment Value.
Is Floor Area a significant predictor of Assessment Value?
3. Construct a scatter plot in Excel with Age as the independent variable and Assessment Value 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 Excels Analysis ToolPak to conduct a regression analysis of Age and Assessment Value.
Is Age a significant predictor of Assessment Value?
Construct a multiple regression model.
Use Excels Analysis ToolPak to conduct a regression analysis with Assessment Value as the dependent variable and Floor Area, 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 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?

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