BUSI 275 Spring 2012 Term Project


The objective of this course is not only that you understand the theory (math) behind statistical analyses, but also that you demonstrate how to apply them appropriately to practical situations in business. As such, the term project is a vital component of the course and should represent the pinnacle of your work in this course (more so even than the final exam).
For the term project, you will In the business world you will often (nearly always) need to work in teams, and so in this project you will be expected to work in teams of 2-4 students. Once the project proposal is submitted, the team cannot be disbanded -- so choose your teammates wisely! Learning how to work in a team is one of the main objectives of this project; it is not easy, but it is important and worth the effort. Giving up on working together is giving up on one of the primary objectives; it is your responsibility to make it work! If you are having difficulty with your teammates, I can offer suggestions on conflict resolution, but I will not be your mediator; you must learn to be peacemakers.
"Be completely humble and gentle; be patient, bearing with one another in love. Make every effort to keep the unity of the Spirit through the bond of peace." (Ephesians 4:2-3, NIV)


There is a lot of flexibility for your project topic, but the idea is for you to dive into a dataset to glean new insight relevant to business in some way. Most analyses will revolve around a single variable chosen as the outcome (dependent variable, Y). The outcome is typically a quantitative variable; you may choose a nominal or dichotomous outcome, but the analysis will be more tricky. Examples of interesting outcome variables could include: A typical goal for statistical analysis is to gain insight into what factors drive or influence the outcome variable: e.g., what other variables might influence the unemployment rate? These variables are your predictors (independent variables, X's). They may be quantitative (regression) or qualitative (ANOVA). You typically want at least 3-4 in order for your model not to be too simplistic. If you have many predictors, you need to figure out which ones are most important. A different but related kind of analysis that is very common in business is time-series analysis; this tracks a single variable over time in an attempt to predict its value in the future (e.g., stock prices, sales revenue, etc.). If your team is keen on this, you may do this for your project, but be warned that we don't have enough time to cover this material in-class, so you will need to do a lot of self-learning.

Project Proposal (due 24hrs before meeting, before 3 Feb)

Your entire team must meet with me to go over your project idea. I am usually only on campus on Tuesdays, but during the two weeks from 23 Jan to 3 Feb I will make an exception, but you need to email me to book a time. The proposal meeting must happen by Fri 3 Feb at the latest. Every member from your team must attend the meeting, no exceptions. At least 24hrs before our scheduled appointment, upload a short summary (one-half to one page) describing your chosen topic and the work your team proposes to do:


Dataset Description (due 7 Feb)

Obtain an existing dataset or describe in detail how you are going to collect your own data. Existing data can be from public sources (e.g., StatCan, U.S. Census, etc.) or from private sources (in which case you need to make sure you have permission to use it, as well as possible REB approval). By the due date of this milestone, you need to actually have all your data in-hand, preferably already imported into an Excel spreadsheet. If you plan to gather your own data, you can't actually get it yet (you need REB approval), but you need to detail your sampling strategy (e.g., friends+family via word-of-mouth, stand on campus/street corner, work with a local retailer, etc.) as well as your data collection strategy (e.g., online questionnaire, oral interview). If you plan on offering participants an incentive (e.g., chance to win a gift card), you need to make that clear (because it does affect your sampling). In short, you should have everything that you need to complete your REB form. Projects that are gathering new data on human subjects (e.g., questionnaires) need to have their REB applications approved by TWU's Research Ethics Board before data is permitted to be gathered. Your dataset description should include all of the following:


REB Forms (due 14 Feb)

By Canadian law, any research involving human subjects must be done according to standards of research ethics. If you are gathering your own data (e.g., questionnaires) or using existing non-public data that involves human subjects, you will need to submit a form to TWU's Research Ethics Board (REB) for approval. You are not permitted to begin recruiting subjects or gathering data until you have received REB approval! Allow 3 weeks for this to happen. REB rules allow for certain studies to be exempt from requiring REB approval. We will discuss in class the rules for REB exemption; generally, if you are using public data (e.g., StatCan or US Census), then you will be exempt. This means you don't need to wait for approval before doing your analysis, however, for class purposes, you will still need to complete and upload an REB form to myCourses (it should be fairly easy for you to do). For more on why research ethics is important and what the REB will be looking for, see the Tri-Council Course on Research Ethics (CORE). If the REB rejects your application with major revisions, you may be required to complete this online tutorial. See TWU's REB page for more details; the forms you need are at the bottom of the page: either "Request for Ethical Review" (if you are gathering new data) or "Analysis of Existing Data" (if you are using data that was originally gathered for another REB-approved study, or public data). On the form, list the instructor as your project's supervisor. Your project's principal investigator ("PI") should be one from your team; you may select one team member arbitrarily.


Presentation (in-class, 10 Apr)

Deliver a 15-min, in-class presentation on your project. If your analysis is not yet complete by the time you do your presentation, that is okay, but you should have some preliminary results to present.


Paper (due Mon 16 Apr)

Your paper must be a complete, well-written exposition of the topic you have chosen, the analysis you performed, and your results and conclusions. You will need to do background research and cite reliable sources. As appropriate, include select tables or figures in the body of the paper to illustrate the points made in your paper. Further tables or charts can go in an appendix. As with the presentation, your target audience is someone like your CEO or board of directors -- they are giving you a chance to communicate your results, but you need to convince them of the relevance and validity of your work in a way that they can understand. Do not assume they are familiar with statistical methods. There is no length limitation on the paper! However, your paper must satisfy all the requirements given, following the outline below. Typically, BUSI275 papers that meet these requirements have averaged around 4000 words. But there is no minimum length; if you can write a clear, concise paper that meets all the requirements in less than 4000 words, so much the better! Your paper should be in proper, professional English. Avoid colloquialisms. As appropriate, prefer the active voice ("We performed linear regression on the data") over the passive ("linear regression was performed on the data"). Actions have agents, so indicate who they are: if you were the one who performed a step, then the use of the pronoun "We" (or "I") is appropriate ("We performed..."). If you are citing someone else's analysis, then indicate who did it: "McDermott et al. performed a similar analysis...". I will not be strict on formatting, as long as your paper is clear and readable. A suggested format is the MLA style: Purdue OWL has a helpful guide. I highly recommend that you email me a rough draft early on to get feedback. This paper constitutes a major portion of your final grade, and you don't want to be surprised that you were heading down the wrong path!



Dataset Description:8%
REB Application:4%