TERM PROJECT IS 601 - 03 Objective Implementing linear regression analysis on real-world data. Data US Stock Market data will be used. Below, we have the data item description. Yyymm is the date variable. The time-series data is at monthly frequency. MktNext is one-month forward total return of the US equity market index. Divyield is the average dividend yield of the US equity market index. Earnyield is the average earnings yield of the US equity market index. Value is the book-to-market ratio of the US equity market. It signifies how much value the stocks have in terms of fundamentals. Ntis is the total net stock issuance in the US. Firms issue new stocks and buy back existing stocks due to several financial reasons. Net issuance is the net change in the number of stock shares on the public equity market. Default is the difference between yields of high-risk corporate bonds and investment grade corporate bonds traded in the US corporate bond market. Term is the difference between yields of the long-term government bonds and short-term government bills in the US treasury market. Infl is the inflation rate. Questions Can we predict equity market returns? Which variables are significant in predicting market returns? Why do univariate and multi-variate regressions give different estimates for the same variables? Hypothesis H0 (null) = Variable X is not a predictor for next month's market return. H1 (alternate) = Variable X is a predictor for next month's market return. Test Please use linear regression to test the hypothesis for each single variable and for all variables together (multiple regression). Test Results Please interpret the test results from a statistical significance point of view. Interpretation What can we conclude about market return prediction?