Applying Analytic Techniques to Business
In the last assessment, you were asked to prepare the first part of your analytics report by creating graphs and calculating some descriptive statistics. In this assessment, you will write your 6-8 page analytics report by interpreting those graphs and statistics, and explicitly connecting those interpretations to implications in the practical business context.
The first step in creating meaningful information from raw data is to represent the data effectively in graphical format and to calculate any required statistics. The second step is interpreting and explaining those graphs and statistics in order to apply them in the business context.
In the previous assessment, you were asked to create the first part of your analytics report by preparing graphs and calculating some descriptive statistics. In this assessment, you will complete your analytics report by interpreting those graphs and statistics, and connecting those interpretations explicitly to implications in the business context.
In business and applied analytics, oftentimes you are interested in drawing conclusions about a population of interest. However, it may not be feasible or practical to gather data on the entire population. In those cases, data is gathered from a sample or subset of the population. Analyses done on the sample are then used to draw inferences regarding the overall population; this mathematic process is referred to as inferential statistics. In this assessment, we begin discussing the topics of sampling and drawing inferences.
All the inferential statistical techniques and methods covered in this course are considered parametric techniques and require certain assumptions to be used and for the results to be reliable, many of which are assumptions about an underlying distribution. Nonparametric techniques require no assumption about underlying distributions and are often used when the assumptions of parametric techniques are not met. Although these are beyond the scope of this introductory course, they are a great option for additional reading and research.
Analytics projects often result in two distinct types of reports or summaries: one tailored to the executive level, which takes the form of a presentation, and the other, a detailed analytics report, which documents an analysis so thoroughly that another analyst can reproduce the analysis exactly. Many times, the latter type is referred to by other departments or analysts wishing to conduct a similar analysis on similar data or by the same analyst who wants to repeat the analysis on a new or revised set of data. In this assessment, you will learn the essential elements that should be included in a report at this level of detail and you will create your own analytics report addressing the business problem you have been working on.
Scenario
Your supervisor has asked you to prepare a report for the quarterly company meeting. The first part of the task was to download the data and create scatterplots and histograms, and to calculate mean, median, and mode of the stock prices that you presented graphically in your report for the last assessment. This time your task is to analyze and interpret those graphical representations of the company stocks and to write a report about your findings for your supervisor.
Your Role
You are an analyst in the same business that you used for the last assessment. Your role is to turn data into meaningful information through the use of descriptive statistics and analysis.
Instructions
After reviewing and integrating your instructor’s feedback on your previous assessment, complete the report as follows:
For each graph you created, write at least one paragraph interpreting the graph.
What does that graph represent?
What does the shape of the graph tell you about how the data have changed over time?
For each statistic you calculated, spend at least one sentence explaining what the statistic represents.
What does the mean tell you?
What does it imply if the median is different from the mean?
What does the standard deviation tell you about the volatility of the data?
Write a new conclusions section in which you explain how these interpretations can be used in the company:
What are some trends about which company leaders should be aware?
How might the information you have provided be used to inform business decisions?
How will you connect those interpretations explicitly to implications for the practical business context?
Create a 6–8 page report containing:
An APA-formatted title page.
A one-page introduction of your chosen company that you created in your previous assessment.
A section labeled “Graphical Representations of Data” that includes the four graphs you created as well as your interpretations of each graph.
A section labeled “Descriptive Statistics” with the statistics you calculated as well as your interpretations of the statistics.
A one-page conclusion in which you describe the potential business applications of the data and your interpretations.
An APA-formatted references page. Remember to cite the source of your financial data.
Example Assessment: You may use the following to give you an idea of what a Proficient or higher rating on the scoring guide would look like:
Assessment 3 Example [PDF]. (Please see attached. -Alyssa)
Additional Requirements
Include APA-formatted in-text citations where appropriate.
Follow the typical double-spaced analytics report format.
Make sure your written communication is free of errors that detract from the overall message.
Evaluation
By successfully completing this assignment, you will demonstrate your proficiency in the following course competencies through corresponding scoring guide criteria:
Competency 3: Apply data analytic techniques to make inferences about a business need.
Interpret four different graphical representations of data.
Interpret descriptive statistics for two different variables.
Competency 4: Present the results of data analysis in clear and meaningful ways to multiple stakeholders.
Explain the business applications from the interpretations of the data.
Correctly format citations and references using current APA style.
Write content clearly and logically, with correct use of grammar, punctuation, and mechanics.
Resources:
Lind, D. A., Marchal, W. G., & Wathen, S. A. (2019). Basic statistics for business and economics (9th ed.). New York, NY: McGraw-Hill.
Chapter 5, “A Survey of Probability Concepts.”
While previous chapters on descriptive statistics have concerned data on past phenomena, this chapter addresses computing the likelihood that something will occur in the future.
Chapter 8, “Sampling Methods and the Central Limit Theorem.”
Sampling a population gives us information to make judgments and inferences about the population. This chapter discusses methods of selecting a sample from a population and how to think about the distribution of the sample.
Chapter 9, “Estimation and Confidence Intervals.”
This chapter will help you think about different aspects of sampling, such as estimating a population value and the range of values, or the confidence interval.
Hewitt, F. (2015). Storytelling: The heart of leadership. New Zealand Management, 62(1), 26–27.
This article is a brief account of the importance of using personal connections to make data accessible in presentations.
Nussbaumer Knaflic, C. (2015). Storytelling with data: A data visualization guide for business professionals. Hoboken, NJ: Wiley.
Chapter 1, “The Importance of Context.”
The components of context are who, what, and how. This chapter offers strategies for interpreting context so you can communicate visually with data.
Stikeleather, J. (2013, Apr 24). How to tell a story with data [Blog post]. Retrieved from https://hbr.org/2013/04/how-to-tell-a-story-with-data
This short post highlights five steps to tell a good story with data from the journalistic perspective, including a helpful categorization of the different types of audiences that you may be addressing with your data visualization.
Watson, H. J. (2017). Data visualization, data interpreters, and storytelling. Business Intelligence Journal, 22(1), 5–10.
This cautionary reading gives specific examples of what not to do when creating an executive presentation with data visualizations.
These resources are optional and are provided to support and extend the learning in the course.
Dr Nic’s Maths and Stats. (2013). Understanding confidence intervals: Statistics help [Video]. | Transcript. Retrieved from https://www.youtube.com/watch?v=tFWsuO9f74o
Dr. Nic’s Maths and Stats. (2015). Understanding statistical inference [Video]. | Transcript. Retrieved from https://www.youtube.com/watch?v=tFRXsngz4UQ
Seth, S. (2018, March 12). Hypothesis testing in finance: Concept and examples. Retrieved from https://www.investopedia.com/articles/active-trading/092214/hypothesis-testing-finance-concept-examples.asp
Confidence Interval Calculator.
Hypothesis Tester.
