easy statistics project ideas
Exploring Practical and Engaging Statistics Project Ideas for Students
This paper will first briefly describe the historical development (in contrast to the formal literature) of the statistics course, and of the EDA course. Then I highlight some other articles about the course materials used, and about the general structure of project-based courses. Next comes the heart of the paper, the guidelines for selecting an engaging EDA project for an undergraduate statistics course. A short example project concerning college tuition dollars and the number of students per faculty member in a university is also described. Finally, I discuss possible course extensions and a general plan to help students in their project development.
Some unique aspects of the EDA project-centered course described are that the students primarily use a mainframe computer for processing their data and writing their papers, and the students decide on their individual project topic. I have found both of these aspects to be very effective in keeping students interested in the data analysis process. However, I have found it necessary to give the students some guidelines for selecting a project suitable for a first statistics course. In order to accentuate the exploratory nature of the project, the nature of most of the questions posed for the projects are descriptive. By pointing out future extensions as the students are working on their projects, simple hypotheses testing and inference ideas are also put into perspective. I can envision using a more complicated project in the second statistics course.
Statistical concepts can be made more concrete and practical in a student’s mind by engaging in formal, exploratory data analysis and presentation. The non-mathematically oriented student often has trouble understanding how the various formulas used in the computation of descriptive statistics are helpful in summarizing the large amount of information contained in a single observation, and thus a collection of observations. The development of the tools in this analysis is often presented in an awkward and abstract way. When we illustrate a concept or a technique using a real data set, we can more readily motivate the necessity or development of the concept.
In her 2003 paper in the Teaching of Statistics in the Health Sciences on an introductory statistics project and using spreadsheets to describe data, Alverdy suggests that students be assigned the following project: “Think of any action on how to help the environment: Reduce, Recycle, Reuse. Gather data on actions. Decide on a sample you can afford. Find out what exactly is done. Create a spreadsheet and use descriptive statistics to investigate what the data tell you about the importance of reducing, recycling, or reusing products.” This project falls into our Basic Knowledge category as it does not require the use of statistical software, and we would suggest the same basic topics for the students to complete and three to four pages of written work interpreting the results. Students in multiple sections are responsible for completing a partially peer-assessed poster before meeting as a class to vote on the most creative presentation. Three statistics students from the host class where the project was modified adopted Kruskal’s principles in a modified peer evaluation process.
A long, collective history of such student projects demonstrates to me that recessive traits become less frequent among future generations of students who are assigned them. However, individual students find the projects to be worthwhile. Then, I have identified three possible culprits: the selection of inappropriate datasets, a lack of support, and the dominance of the students’ misconceptions concerning the bridging from physical to statistical significance.
A stereotypical purpose of statistics is inferences about populations from which samples were collected. We often partition these projects into descriptive statistics and hypothesis tests. For my students, especially those who primarily want to work with money, reliable precision seems more important. Therefore, the following projects also seek to aid them in making predictions, then minimizing their risks. Either activity alone, making inferences about the population results, could be viewed as trivial. What makes them engaging for students is some combination of the activities involved and the attendant possibilities for substantive subject matter immersion. All project suggestions should be achievable given the standards for students’ training level.
4.1. Time Series Analysis: Dow Jones, S&P 500, Apple, and Google Time series analysis is a widely recognized sub-field of statistics and the most valuable in the stock market. Students can easily download the stock prices of publicly traded companies like Apple and Google as well as stock market indexes like the Dow Jones Industrial Average and the S&P 500. A basic analysis using auto-correlation and partial auto-correlation can be completed to determine whether the returns are volatile over time. Financial companies and programmers perform this analysis to perform risk assessments and understand return potential. In addition, time series guidelines can be used to predict the stock price over time. Topics that can be addressed in the final project include integrating the stock price with other data sources, using more sophisticated predictive models, and working with higher frequency data.
Students often appreciate the allure of numbers by conducting projects on more advanced statistical topics or novel data sources. Given the introduction of tools like R and Python in earlier statistics courses, students have the requisite programming knowledge to attempt a wider range of projects. This section provides some small projects on popular advanced topics ranging from time series analysis, to sports analytics, to data visualization. These projects can be used in an independent statistics course or as a guided tutorial if students need more support. The use of these advanced statistics projects can attract a wider array of students who have a penchant for programming and working with real-world datasets stemming from their data science experiences.
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