1. Based on the regression in \"die table, give your best estimate and a 9D percent condence interval of what will happen to the ratio of the actual price to the estimated cost if the number of days for a project decreases by 2513, holding the other independent variables fixed. 2. Can you claim at the usual 5 percent signicance level that an increase in the number of bidders, holding 'd'ie other independent variables fixed, would on average decrease the project price ratio relative to the DDDT estimate\"? 3. Suppose that= for a given project where the bidding was not rigged= the winning bid ended up being exactly equal to the DDDT estimate; in other words, Ratio ended up being exactly one. What would be the lowest signicance level at which you could prove (using the regression in the table) that rigging the auction would have raised the price by more than 15 percent? 4. Would it be legitimate to drop the variables FoirPr and FxCost from the regression if you wanted to do so? If the answer is yes= write down the new regression equation. 5. One of your colleagues, Stan, draws your attention to a potential problem in the regression. He claims, \"Almost all of the jobs the attorney general classied as rigged took place during the hot summer months. Everyone lmows that jobs in hot weather are harder to do, and therefore command a greater premium over estimated costs than jobs done at other times of the year.\" (a) If Stan is right, what is wrong with the regression? (b) Suppose you could gather data regarding the time of year each job took place and use it to create a new variable, Hot (= 1 if the job took place during the hot summer months and = CI otherwise). If Stan is right: what would change (and how) when you included Hot as an additional independent variable in the regression