Problem 3. Suppose we have a data set on used iPhones sold on eBay. Let price, be the selling price of phone i in USD. Let go; be the memory of the phone in gigabytes. In the data set, there are two types of phones, namely iPhone 4 and iPhone 5. Let v4; be a dummy variable, which is equal to 1 if phone i is an iPhone 4 (and 0 otherwise). Let v5, be a dummy variable, which is equal to 1 if phone i is an iPhone 5 (and 0 otherwise). Using the model price; Both , . 05; 18 2 . gb; +8 3 - (05, . gb,) + U. with 10, 014 observations yields the following (partial) Stata output: Source | SS df MS Number of obs = 10014 F( 3, 10010) = 118.22 Model | 25905173.7 3 8635057.91 Prob > F 0. 0000 Residual | 731172190 10010 73044. 1748 R-squared 0. 0342 Adj R-squared = 0.0339 Total | 757077364 10013 75609. 4441 Root MSE 270.27 price | Coef. Std. Err. t P>It| [95%% Conf. Interval] v5 | 120.6 12.58 9.59 0. 000 gb | 1.33 . 7296 1.82 0. 069 gb*v5 / -. 8683 . 7743 -1. 12 0. 262 _cons| 40 . 18 10.80 3.72 0.000 (a) Interprets 2 in a descriptive manner. (b) Test Ho B 1 = 0 and3 2 =0 and3 3 = 0 at the 5% significance level. (c) Find a 90% confidence interval for - (d) In terms of the parameters, state the null hypothesis that iPhones 5 with gb = 16 cost on average as much as iPhones 4 with gb = 32. (e) Suppose we ran a regression of price, on go, using iPhones 5 only. What is the estimated intercept? What is the estimated slope coefficient