| mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb |
|---|---|---|---|---|---|---|---|---|---|---|
| 21.0 | 6 | 160 | 110 | 3.90 | 2.620 | 16.46 | 0 | 1 | 4 | 4 |
| 21.0 | 6 | 160 | 110 | 3.90 | 2.875 | 17.02 | 0 | 1 | 4 | 4 |
| 22.8 | 4 | 108 | 93 | 3.85 | 2.320 | 18.61 | 1 | 1 | 4 | 1 |
| 21.4 | 6 | 258 | 110 | 3.08 | 3.215 | 19.44 | 1 | 0 | 3 | 1 |
| 18.7 | 8 | 360 | 175 | 3.15 | 3.440 | 17.02 | 0 | 0 | 3 | 2 |
| 18.1 | 6 | 225 | 105 | 2.76 | 3.460 | 20.22 | 1 | 0 | 3 | 1 |
22 Final Project
Note
The preview of this template is rendered in HTML. However, all assignments must be rendered in PDF for submission on Canvas. The textstat_tools repository is already set up to do this for you. Be sure to follow the directions including the installation of tinytex.
Abstract
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22.1 Introduction
- What is the context? In other words, what’s the topic/territory?
- What is your research question?
- Why is it interesting/important? What are the stakes? What are we to learn from your analysis?
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22.2 Data
- What are data you’re using?
- Where do they come from? How were they collected/compiled?
- Describe the data and include a summary table of the corpus.
- Use the proper syntax to reference your tables (and figures); see the example at the end of this paragraph. https://quarto.org/docs/authoring/cross-references.html
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22.3 Methods
- Efficiently describe your methods.
- Explain why you chose these methods. You have many options (from this course and elsewhere). What is the explanatory power of what you’re doing here?
- Note that this section and the previous one should work together. You are likely to justify your selection of the data in the Data section and what you’re doing with those data here.
- Cite sources where appropriate. For example, if you’re carrying out a Biber-esqu multi-dimensional analysis, cite a couple of similar studies that use this method. Citations can signal both the validity of the methods and allow you to be very efficient in your descriptions. For including citations, see the examples at the end of the next two paragraphs. https://quarto.org/docs/authoring/citations.html
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22.4 Results
- Be sure your visualizations are well-designed and properly captioned. Note that the toy example in Figure 22.1 is not good!
- Also, be sure to follow the appropriate conventions for reporting your statistical results as outlined in Brezina.
- Be strategic and selective about what results you choose to show. Even in your final project (which is much more complete and elaborated than either of the coffee-break experiments) you are not going to show all of your work. You must decide what is most interesting/explanatory.
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22.4.1 A subsection
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22.4.2 Another subsection
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22.5 Discussion
- Summarize your main findings.
- Explain the implications of your findings. What do you want your audience to take away from your analysis?
- What are the limitations? In the data you used? In your methodological approach?
- Final words or next steps…
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22.6 Acknowledgments
If you used Generative AI to assist you in the writing of the report, describe how you used the LLM as part of your writing process here. For example, did you use it help generate a first draft? Or perhaps to revise your prose as you finalized your report? Finally, evaluate its usefulness. Did you find it helpful? If you didn’t use an LLM as part of your process, simply delete this secton.
22.7 Works Cited
Mosteller, Frederick, and David L Wallace. 1963. “Inference in an Authorship Problem: A Comparative Study of Discrimination Methods Applied to the Authorship of the Disputed Federalist Papers.” Journal Article. Journal of the American Statistical Association 58 (302): 275–309. https://doi.org/10.1080/01621459.1963.10500849.
Mosteller, Frederick, and David L. Wallace. 1984. Applied Bayesian and Classical Inference: The Case of the Federalist Papers. Book. 2nd of Inference and disputed authorship, The Federalist. Springer Series in Statistics. New York: Springer-Verlag.