You can use this file as a **skeleton** for your poster project. Either copy these section headers where you’re already working, or copy those into this file.

This week we’ll focus on the research questions, methods, and results. Next week we’ll complete the rest (intro, discussion, future work, bibliography). The last week we’ll make sure it all looks good on a poster.

Finish getting your poster sections organized at the bottom of your HW document. Continue to revise, tighten up presentation, improve plots, etc. Remember, you’ll be presenting your poster. It’s not intended to be read, it’s their to be your visual aid as you discuss your work.

Complete the content for each of these sections:

(Lit Review) 2-4 bullets describing the study, previous research.

(Cf. ADA1 Week 2 videos on Research questions and Literature review with Mendeley and Google scholar.)

- (Research Questions) 2 bullets, one for each research question, stated as the alternative hypothesis.

(Personal Codebook) Data source(s).

(Personal Codebook) Variables used.

(Cf. ADA1 Week 1 videos on Personal codebook.)

(MANOVA or other) Statistical methods used to answer the research questions.

*While this would follow your results, let’s put it here so you have a full column to show the results of the analysis of both research questions.*

- Put the results you found for each research question in the context you provided in your introduction.

…or Further directions or Next steps or something else that indicates there more to do and you’ve thought about it.

- What do these results lead you to want to investigate next?

By citing sources in your introduction, this section will automatically have your bibliography.

(Cf. ADA1 Week 2 videos on Literature review, in the YAML header of this Rmd file, use the

`bibliography:`

option to specify your`.bib`

file.)

**(2 p)**Plot and describe the data, as well as the statistical model.Start with a scatterplot matrix, then think of whether there’s a plot that captures.

MANOVA: A mean with CI bars is the statistical model overlayed on the data points.

Contingency table: A mosaic plot with colored boxes relative to contribution to Pearson \(\chi^2\) shows the data with evidence towards the alternative hypothesis.

Simple linear regression: A regression line is the statistical model overlayed on the data points.

Logistic regression: The logistic curve is the statistical model overlayed on the top/bottom histograms of the data.

**(2 p)**Write out the statistical model (as an equation) and show the output (e.g.,`summary(lm.fit)`

) of the model fit.**(2 p)**State the conclusion of the hypothesis test, or interpret the final model.**(2 p)**Interpret the results in the context of the research question.If your final model is simple enough to provide an informative plot, consider a final plot that summarizes the results.