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 Choose a topic and hypothesis, find an appropriate dataset, estimate a regression model, and present your model and results in a short essay. Interpret your model in light of the assumptions you are making when estimating regression models. 

 https://ourworldindata.org/ has fascinating long-run datasets, there is also a list of many datasets already included in R here: https://vincentarelbundock.github.io/Rdatasets/datasets.html, 

for example:Does rainfall increase ice cream sales? Has the introduction of airbags/seat belts lowered car accident deaths? Has the BC carbon tax lowered CO2 emissions? etc.). Present your results in the form of a short written essay (maximum length of 2 pages + references).  

 .Sample Structure: 1) Introduction Stating your hypothesis, explain why this topic is important and interesting and what related literature already exists. 2) Data Describe your data (including sources and descriptions of variables), perhaps including a Figure. 3) Methods Stating your estimated model (ideally as an equation), justify the functional form you choose (log, etc.) and what assumptions you make. 4) Results Showing your estimated model results in a Table including: coefficients, standard errors, number of observations, and R-squared (see below for an example in Table 1). Interpret your results carefully, conduct and interpret hypotheses tests (individual and perhaps joint). Table 1: Estimation Results Dependent Variable = log(wage) Intercept 1.02 (0.52) Experience 0.55 (0.33) Experience2 -0.05 (0.03) Education 0.72 (0.23) Number of Observations 342 R-Squared 0.76 Standard errors shown in parentheses. 5) Conclusion Summarise your results and discuss what you conclude based on your findings. 6) References References to the literature and data used