Possible topics: prediction of prices for a specific stock/bitcoin/commodity/futures/options; prediction of a country's GDP level using market indicators; prediction of auto sales; analysis of bonds liquidity, etc. C o n s t r u c t a solid multivariable regression model by brainstorming which factors can be relevant to prediction, obtain data and use R to carry out statistical analysis. Work with a sufficiently large data set originally to find which factors are significant for the model (twenty to twenty-five f a c t o r s). Including categorical variables along with continuous variables is strongly encouraged. Check the assumptions of the model by carrying out residual analysis (see the R videos on that in particular, videos 8 and 9) and p r o v i d e recommendations if there are significant violations. Test relevant hypotheses. Remove insignificant variables and analyze the new model using R tools. Y o u will need to s u b m i t a report covering the background information (the topic of research), the objective of your research, model description with following rigorous statistical analysis and conclusions. There is no requirement on the size of the final report but typically it should be ten to fifteen p a g e s + Appendix with R code. Make sure to indicate all additional resources you use, including the data sources, in the reference list. Include your R code in the Appendix of the report, that is in the pdf file, and as a separate .R file. An abstract is a concise summary of a research paper or entire thesis. It is an original work, not an excerpted passage. An abstract must be fully self-contained and make sense by itself, without further reference to outside sources or to the actual paper. The abstract to be no more than four hundred wrdds. The abstract is essentially a short presentation of our work that you can use, for example, during the job interview process.