Purpose 
This assignment is intended to help you learn how to apply statistical methods when analyzing operational data, evaluating the performance of current marketing strategies, and recommending actionable business decisions. This is an opportunity to build critical-thinking and problem-solving skills within the context of data analysis and interpretation. You’ll gain a first-hand understanding of how data analytics supports decision-making and adds value to an organization.

Scenario: 
Pastas R Us, Inc. is a fast-casual restaurant chain specializing in noodle-based dishes, soups, and salads. Since its inception, the business development team has favored opening new restaurants in areas (within a 3-mile radius) that satisfy the following demographic conditions:

• Median age between 25 – 45 years old
• Household median income above national average
• At least 15% college educated adult population
• Last year, the marketing department rolled out a Loyalty Card strategy to increase sales. Under this program, customers present their Loyalty Card when paying for their orders and receive some free food after making 10 purchases.

  The company has collected data from its 74 restaurants to track important variables such as average sales per customer, year-on-year sales growth, sales per sq. ft., Loyalty Card usage as a percentage of sales, and others. A key metric of financial performance in the restaurant industry is annual sales per sq. ft. For example, if a 1200 sq. ft. restaurant recorded $2 million in sales last year, then it sold $1,667 per sq. ft.

  Executive management wants to know whether the current expansion criteria can be improved. They want to evaluate the effectiveness of the Loyalty Card marketing strategy and identify feasible, actionable opportunities for improvement. As a member of the analytics department, you’ve been assigned the responsibility of conducting a thorough statistical analysis of the company’s available database to answer executive management’s questions.

  Report:
  Write a 750-word statistical report that includes the following sections:

• Section 1: Scope and descriptive statistics
• Section 2: Analysis
• Section 3: Recommendations and Implementation
• Section 1 – Scope and descriptive statistics
• State the report’s objective.
• Discuss the nature of the current database. What variables were analyzed?
• Summarize your descriptive statistics findings from Excel. Use a table and insert appropriate graphs.
• Section 2 – Analysis
• Using Excel, create scatter plots and display the regression equations for the following pairs of variables:
• “BachDeg%” versus “Sales/SqFt”
• “MedIncome” versus “Sales/SqFt”
• “MedAge” versus “Sales/SqFt”
• “LoyaltyCard(%)” versus “SalesGrowth(%)”
• In your report, include the scatter plots. For each scatter plot, designate the type of relationship observed (increasing/positive, decreasing/negative, or no relationship) and determine what you can conclude from these relationships.
• Section 3: Recommendations and implementation
• Based on your findings above, assess which expansion criteria seem to be more effective.Could any expansion criterion be changed or eliminated? If so, which one and why?
• Based on your findings above, does it appear as if the Loyalty Card is positively correlated with sales growth? Would you recommend changing this marketing strategy?
• Based on your previous findings, recommend marketing positioning that targets a specific demographic. (Hint: Are younger people patronizing the restaurants more than older people?)
• Indicate what information should be collected to track and evaluate the effectiveness of your recommendations. How can this data be collected? (Hint: Would you use survey/samples or census?)
• Cite references to support your assignment.

  Format your citations according to APA guidelines.

  Submit your assignment.

Resources: 

• Microsoft Excel®
• “How Netflix Expanded to 190 Countries in 7 Years” from Harvard Business Review
• Call Center Waiting Time
• Part 1: Globalization and Information Research
  Context: Companies that perform well in their country of origin usually consider expanding operations in new international markets. Deciding where, how, and when to expand is not an easy task, though.

  Many issues need to be considered before crafting an expansion strategy and investing significant resources to this end, including:

• the level of demand to be expected for the company’s products/services
• presence of local competitors
• the regulatory, economic, demographic, and political environments
• Carefully researching and analyzing these and other factors can help mitigate the inherent risk associated with an overseas expansion strategy, thus increasing the likelihood of success.

  As a data analyst in your company’s business development department, you’ve been tasked with the responsibility of recommending countries for international expansion. You’ll write a report to the company’s executive team with your research, analysis, and recommendations.

  Instructions: 
  Write a 525-word summary covering the following items:

• According to the article listed above, what were the most important strategic moves that propelled Netflix’s successful international expansion?
• The article mentions investments in big data and analytics as one of the elements accompanying the second phase of overseas expansion. Why was this investment important? What type of information did Netflix derive from the data collected?
• According to the article, what is exponential globalization?
• Not all international expansion strategies are a resounding success, however. Research an article or video that discusses an instance in which an American company’s expansion efforts in another country failed. According to the article/video you selected, what were the main reasons for this failure? Do you agree with this assessment?
• Explain some of the reasons why certain companies’ expansion plans have failed in the past.
• Part 2: Hypothesis testing 
  Context: 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.

  Instructions:
  Access the Call Center Waiting Time file. Each row in the database corresponds to a different call. The 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 of 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 of α=0.05.
• Assess if the new protocol served its purpose. (Hint: this should be a test of means for 2 independent groups.)
• Submit your calculations and a 175-word summary of your conclusions.
• Submit your assignment.

Resources: Microsoft Excel®, DAT565_v3_Wk6_Data_File

Scenario: A city’s administration isn’t driven by the goal of maximizing revenues or profits but instead looks at improving the quality of life of its residents. Many American cities are confronted with high traffic and congestion. Finding parking spaces, whether in the street or a parking lot, can be time consuming and contribute to congestion. Some cities have rolled out data-driven parking space management to reduce congestion and make traffic more fluid.

You’re a data analyst working for a mid-size city that has anticipated significant increments in population and car traffic. The city is evaluating whether it makes sense to invest in infrastructure to count and report the number of parking spaces available at the different parking lots downtown. This data would be collected and processed in real-time, feeding an app that motorists can access to find parking space availability in different parking lots throughout the city.

Instructions: Work with the provided Excel database. This database has the following columns:

• LotCode: A unique code that identifies the parking lot
• LotCapacity: A number with the respective parking lot capacity
• LotOccupancy: A number with the current number of cars in the parking lot
• TimeStamp: A day/time combination indicating the moment when occupancy was measured
• Day: The day of the week corresponding to the TimeStamp
• Insert a new column, OccupancyRate, recording occupancy rate as a percentage with one decimal. For instance, if the current LotOccupancy is 61 and LotCapacity is 577, then the OccupancyRate would be reported as 10.6 (or 10.6%).
• Using the OccupancyRate and Day columns, construct box plots for each day of the week. You can use Insert > Insert Statistic Chart >Box and Whisker for this purpose. Is the median occupancy rate approximately the same throughout the week? If not, which days have lower median occupancy rates? Which days have higher median occupancy rates? Is this what you expected?
• Using the OccupancyRate and LotCode columns,construct box plots for each parking lot. You can use Insert > Insert Statistic Chart >Box and Whisker for this purpose. Do all parking lots experience approximately equal occupancy rates?Are some parking lots more frequented than others? Is this what you expected?
• Select any 2 parking lots. For each one, prepare as scatter plot showing occupancy rate against TimeStamp for the week 11/20/2016 –11/26/2016. Are occupancy rates time dependent? If so, which times seem to experience highest occupancy rates? Is this what you expected?
• Presentation: 
  Create a 10- to 12-slide presentation with speaker notes and audio. Your audience is the City Council members who are responsible for deciding whether the city invests in resources to set in motion the smart parking space app.

  Complete the following in your presentation:

• Outline the rationale and goals of the project.
• Utilize boxplots showing the occupancy rates for each day of the week. Include your interpretation of results.
• Utilize box plots showing the occupancy rates for each parking lot. Include your interpretation of results.
• Provide scatter plots showing occupancy rate against time of day of your selected four parking lots. Include your interpretation of results.
• Make a recommendation about continuing with the implementation of this project.
• Submit your assignment.

Review the Wk 2 – Apply: Statistical Report assignment.

In preparation for writing your report to senior management next week, conduct the following descriptive statistics analyses with Excel®. Answer the questions below in your Excel sheet or in a separate Word document:

• Insert a new column in the database that corresponds to “Annual Sales.” Annual Sales is the result of multiplying a restaurant’s “SqFt.” by “Sales/SqFt.”
• Calculate the mean, standard deviation, skew, 5-number summary, and interquartile range (IQR) for each of the variables.
• Create a box-plot for the “Annual Sales” variable. Does it look symmetric? Would you prefer the IQR instead of the standard deviation to describe this variable’s dispersion? Why?
• Create a histogram for the “Sales/SqFt” variable. Is the distribution symmetric? If not, what is the skew? Are there any outliers? If so, which one(s)? What is the “SqFt” area of the outlier(s)? Is the outlier(s) smaller or larger than the average restaurant in the database? What can you conclude from this observation?
• What measure of central tendency is more appropriate to describe “Sales/SqFt”? Why?
• Submit your assignment.