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

DownloadtheCompetency 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,noteyour answers to the given questions so you can easily summarize them in your reflection.

Usethe 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 =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?

Reflection

reflecton the types of medical offices you would advise management to close and open, and why. Use your exercise notes to support your rationale.

FloorArea (Sq.Ft.)	Offices	Entrances	Age	AssessedValue ($'000)
4790	4	2	8	1796
4720	3	2	12	1544
5940	4	2	2	2094
5720	4	2	34	1968
3660	3	2	38	1567
5000	4	2	31	1878
2990	2	1	19	949
2610	2	1	48	910
5650	4	2	42	1774
3570	2	1	4	1187
2930	3	2	15	1113
1280	2	1	31	671
4880	3	2	42	1678
1620	1	2	35	710
1820	2	1	17	678
4530	2	2	5	1585
2570	2	1	13	842
4690	2	2	45	1539
1280	1	1	45	433
4100	3	1	27	1268
3530	2	2	41	1251
3660	2	2	33	1094
1110	1	2	50	638
2670	2	2	39	999
1100	1	1	20	653
5810	4	3	17	1914
2560	2	2	24	772
2340	3	1	5	890
3690	2	2	15	1282
3580	3	2	27	1264
3610	2	1	8	1162
3960	3	2	17	1447