Data Analysis Paper Structure: The paper must follow this specific structure. You must label each section of your paper as such: • Introduction. This section introduces your reader to the topic, provides a sense of why the topic is important and includes a detailed thesis statement. Your thesis statement must: specify your hypothesis, mention what data you are using, and include a summary of your findings. For example: In this paper I study the relationship between sunlight and plant growth. Using an original dataset, I test the hypothesis that as sunlight increases plants grow at a faster rate. Findings are consistent with expectations. This study shows that plants grow faster when exposed to sunlight. • Literature Review: an organized review of previous studies on your chosen topic. This is not a summary of prior studies, but an analysis that leads the reader to understand what your contribution will be. Your literature review must include a minimum of 5 scholarly sources, these must be academic journal articles or academic books. Reports from other sources are not scholarly sources and do not meet this criterion. See the earlier lessons on how to write a literature review. • Data and Methods—this is the core of your paper (describe your hypotheses, data, and methods). You must describe how you obtained the dataset and where it comes from, cite accordingly. For example, first you would state your hypothesis. Then, describe the data. Next, present your statistical model (see the lesson on Bivariate regression, this is your Y= α + βX +μi, describe the variables, explain how these are coded and also how you recoded them. Note that unless you are creating your own dataset, you must recode your variables. Then, describe your method, which in this case is bivariate regression analysis—a statistical model. • Results. This is the section where you interpret the findings—it’s one of the most important sections of your paper. In this section you need to interpret the regression output, explain the magnitude and direction of the coefficient (is it consistent with expectations?), are the results significant? If the results are not significant, what might we take away from the analysis? Are the findings substantively significant? What might improve this model? • Discussion/Conclusion (In the discussion explain what we take away from these findings. In the conclusion you might discuss how the results fit within the literature, the contribution made, and you should provide thoughts on what further research should be conducted to study this question.