4. (6 points ) Plot a scatter diagram of r trip_fa ( axis ) on d i s t a n (xaxis ). On the same graph , include a linear fit of r trip_fa ( axis ) on d i s t a n (xgaxis ). What do you see from this figure , if any ? Now drop all business -class observations and focus just on coach cabin (economy class ). Perform a regression : Regress r trip _ fan advance roundtrip online rtponline travelrestrict i o,where r t poshlairisothe interaction between ro un d t r iapd online 5. (2 points) Present your entire regression output. (A STATA output screenshot is needed.) 6. (4 points ) Interpret the coefficient (not just the sign ) of h u ba dummy variable . What does the coefficient tell you ? 7. (4 points ) Interpret the coefficient (not just the sign ) of m s h a r(market share ), a continuous numerical variable . What does the coefficient tell you ? Does the sign make sense ? 8. (5 points ) What is the (partial ) effect of r t p on 1 i oner trip_fa dethe interaction necessary 9. (3 points ) Is the effect of market share on roundtrip airfare statistically significant at the 5% significance level ? Is the effect of travel restriction on roundtrip airfare statistically significant at the 19% significance level ? 10. (6 points ) Propose two additional independent variables that you think should be included in this regression . Explain why you think they should be included and provide your rationale on the potential signs (positive or negative ) for each additional variable . Avoid abstract ideas or variables that cannot be easily quantified or measured Question Set B (20 points ) The dataset used in this part of the case analysis , Set B data , comes from "Introductory Econometrics" by Jefferey M. Wooldridge. It contains monthly time-series data on 5&P index from 1947 to June 1993 . The dataset has been modified for this exercise The definition of the variables are as follows : d a t eactual date of the S & P 500 index s p 5 0 :cactual value of the S &P 500 index for that date t : capturing the number of time periods (by using command gen t n quart e capturing the quarter of the time period (by using command ge n quarter quarter X date Use the dataset to answer the following questions Consider the four following regression models for forecasting 5 &P 500 index : a linear time trend (Model 1) a linear time trend and seasonality effects (Model 2) a quadratic time trend (Model 3) a quadratic time trend and seasonality effects (Model 4)