The target length for the paper is 10-12 pages. 
 
 Intro: 
 What question do you want to answer with your regressions?
 Why is this question important? 
 What is your hypothesis about what the answer will be?
 What reasons do you have to form this hypothesis rather than the opposite hypothesis?
 Are there reasons your hypothesis might turn out to be wrong? What are they? 
 Data
 What organization created your data?
 Where and when are the data from ? 
 What variables do they contain? 
 Present summary statistics about the data 
 how many observations are there?
 mean, median, min, max, standard deviation of the important variables in your data? put these in a table.
 What are some limitations of the data? 
 Are they representative of the whole population or just some kinds of people?
 Are they missing any values? 
 Are all of the variables measured well? Or might some of them contain error? 
 What measures do you construct from the variables in your dataset?
 Do you use them as they are? 
 Do you make any into categories ?
 Do you add, subtract, divide, multiply, or otherwise transform any of them to make new measures from your data?
 Empirics 
 What regressions will you run? Write them down in the order that you'll run them 
 How to choose regressions: Did you make any important, subjective decisions when deciding how to run the regression? e.g. did you drop any observations, impute any missing values, create any binary or categorical variables from continuous variables? Try making these decisions a few different ways and run each of the corresponding regressions. 
 Display the y = B0 + B1*x + .... formulas for each of the regressions you'll run 
 Results 
 Display each of your regressions one-by-one and discuss them individually
 Discuss which coefficients are significant, which coefficients are large, and how their significance and size does or does not stay consistent across all the regressions you run. 
 Comment on how your R^2 and adjusted R^2 evolve 
 Show residual plots and discuss which observations are/are not systematically over- or under-predicted.
 Conclusion 
 Summarize your findings and re-state why they are important. 
 
 
 
 
 Basically, 
 It is using some kind of econ stat.
 
 Discrete probability distributions 
 Continuous probability distributions 
 Sampling and sampling distributions 
 Interval estimation 
 Hypothesis testing 
 Inference with two populations 
 Inference about population variance 
 Simple linear regression 
 Multiple regression 
 Time Series 
 
 With real-life sample problem in the real world with the data that we find through online.