Comprehensive Study Guide for Final Academic R-Analysis Paper

Providing Direct Links: It is essential to provide a direct link to the data source used for the paper. This allows the instructor to download the data, import it into R, and replicate the analysis to follow along with the student's work.
Dataset Backstory: The section should include information about where the dataset originated and provide a backstory on its context, similar to how it is presented in academic papers.
Variable Descriptions: Students must clearly define and describe the variables included in their analysis.
Primary Objective: By the end of this section, the reader should have a complete understanding of the data involved and the specific dataset being utilized.

The "Meat and Potatoes": The analysis section is the most significant part of the paper, accounting for one-third of the total grade ( $20$ out of $60$ points). It is structured similarly to the analytical assignments students have completed throughout the semester.
Loading Packages:
* When loading a package (e.g., the car package), students must explain why it is being loaded.
* Explicitly state whether the package is needed for its specific functions or for a dataset it contains.
Function Preferences (str vs. head):
* While the head() function shows the first few rows of a dataset, the str() (structure) function is preferred.
* str() not only displays the first few rows but also reveals the characteristics/types of the variables themselves.
Importing Code:
    * Students must include the specific code used to import the dataset into R.
    * In RStudio, this can be done by going to File > Import Dataset > From Text (base).
    * If the data appears incorrectly (e.g., headers labeled as V1, V2, V3), the "Heading" option should be toggled to "Yes."
    * RStudio generates the import code automatically in the console; students should copy and paste this code directly into their paper.

Immediate Reporting: Unlike some academic styles that wait until the results section, assumption checks should be reported immediately within the analysis section.
Justification for Analysis: Reporting checks early justifies the selection of the specific statistical test. If an assumption is failed, it provides the rationale for switching to an alternative analysis.
Interpreting P-values for Assumptions:
* For normality tests, the null hypothesis ( $H_0$ ) typically posits that the data is normally distributed.
* If the $p$ -value is significant (e.g., p < 0.05), the null hypothesis is rejected, indicating that the data does not have normality.
Handling Failed Assumptions:
* If an assumption fails and no alternative test was taught in the course, students should acknowledge the failure and mention it as a limitation, but continue with the primary analysis as instructed.
* If alternatives were taught, students should switch tests (e.g., switching from a one-way ANOVA to a Kruskal-Wallis test if normality is violated, or using Fisher's Exact Test instead of Chi-Square if expected frequencies are too small).
Collinearity Exception: In a simple linear regression (one predictor), checking for collinearity is unnecessary. This should be explicitly noted to show the student understands why it is being omitted.

Hypothesis Statement: The section must begin by restating the null and alternative hypotheses.
Stat Blocks: Students must include standard statistical reporting blocks. Proper formatting for these blocks can be found in the feedback from analytical assignments $8$ , $9$ , $10$ , and $11$ .
Model Selection:
    * Report the $R^2$ value.
    * Report AIC (Akaike Information Criterion) values.
    * Note: In simple linear regression with only one predictor, model selection using AIC is often trivial because if the predictor is significant, that model is inherently the best option.
Coefficients: Explain the meaning of the Y-intercept and the slope coefficient in the context of the data.
Avoid Redundancy: Reporting assumption checks should not be duplicated in the results section if they were already covered in the analysis section.

Interpreting Relationships: Avoid using causal language. Use terms like "significantly predicts" rather than "affects," unless the study involves direct control/experimentation.
Identifying Limitations:
* Failed assumption checks should be listed as limitations to the interpretation of the results.
* The age of the data should be considered (e.g., pre-COVID datasets may not reflect current 2024 trends).
Concluding Narratives: The conclusion should bring the paper "full circle" by addressing the initial motivation for the study.
* Example: A student analyzed if fertility rates and life expectancy were related because they were worried about "falling behind" peers; the analysis concluded that a decrease in fertility and increase in age were associated with increased life expectancy, effectively answering the personal concern.

Question: For our paper, are we able to base it off any of the assignments that we've used this year?
Response: Yes, specifically Analytical assignments $8$ , $9$ , $10$ , or $11$ . These were designed to serve as blueprints for the paper. You do not have to use every single part of an assignment if it doesn't apply to your specific dataset.
Question: If I did mine similar to hers [the example], can I just delete the parts of my results section where I talked about the assumption checks and then copy and paste whatever is necessary and then put that in the analysis?
Response: Yes, move that content to the analysis section and rework it so it stitches together seamlessly. Reporting it in both places is redundant. Reporting it early explains to the reader why you chose the analysis you did.
Question: My dataset loaded as a text file and wouldn't run unless I used the read.table function. What should I do?
Response: [The transcript ends as the instructor begins to address the specific R coding issue].

R Versioning: The software version discussed is version $0.6$ , not version $6.0$ .
Reference Section: Including a reference section is optional, but if included, students should cite the analytical assignment used as a blueprint and the R version used.