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.(Attached) 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?
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.
Category: Data and Business Analytics
Assignment Directions
(Intsructors Video below)
Your organization is evaluating the quality of its call center operations. One of the most important metrics in a call center is Time in Queue (TiQ), which is the time a customer has to wait before he/she is serviced by a Customer Service Representative (CSR). If a customer has to wait for too long, he/she is more likely to get discouraged and hang up. Furthermore, customers who have to wait too long in the queue typically report a negative overall experience
with the call. You’ve conducted an exhaustive literature review and found that the average
TiQ in your industry is 2.5 minutes (150 seconds).
Another important metric is Service Time (ST), also known as Handle Time, which is the time a CSR spends servicing the customer. CSR’s with more experience and deeper knowledge tend to resolve customer calls faster. Companies can improve average ST by providing more training to their CSR’s or even by channeling calls according to area of expertise. Last month your company had an average ST of approximately 3.5 minutes (210 seconds). In an effort to improve this metric, the company has implemented a new protocol that channels calls to CSR’s based on area of expertise. The new protocol (PE) is being tested side-by-side with the traditional (PT) protocol.
Download the Call Center Waiting Time database.(Attached)
Each row in the database corresponds to a different call. Column variables are as follows.
• ProtocolType: indicates protocol type, either PT or PE
• QueueTime: Time in Queue, in seconds
• ServiceTime: Service Time, in seconds
Perform a test of hypothesis to determine whether the average TiQ is lower than the industry standard of 2.5 minutes (150 seconds). Use a significance level α=0.05.
Evaluate if the company should allocate more resources to improve its average TiQ.
Perform a test of hypothesis to determine whether the average ST with service protocol PE is lower than with the PT protocol. Use a significance level α=0.05.
Assess if the new protocol served its purpose. (Hint: This should be a test of means for 2 independent groups).
Write a 175-word summary of your conclusions.