0 Data for Sales Over Time The accompanying data describe sales over time at a franchise outlet of a major U.S. oil company. Each row summarizes sales for one day. This particular stati dollar sales of the convenience store. The explanatory variable Volume gives the number of gallons of gasoline sold, and Washes gives the number of car wash E Click the icon to view the table of data for sales over time. Full data set e Sales Volume Washes Sales Volume Washes B. Car washes because the slope for Washes is larger than the slope for Volume and the daily number of car washes is more than the volume of gallons se c. Gasoline sales because the slope for Volume is larger than the slope for Washes and the daily volume of gallons sold is more than the number of car wa D. Car washes because one car wash costs more than a tank of gas. (Dollars) (Gallons) 3478 3721 3619 2970 3808 3675 3516 3795 (Dollars) (Gallons) 2184 3283 2258 3606 3501 2234 127 95 148 2210 2286 149 222 118 2261 2112 2306 2274 2237 2304 2411 2271 2314 2363 1986 2051 2113 2154 306 294 106 62 2285 1982 2236 2187 2354 2271 2327 1876 2306 2321 2289 2204 1971 2258 2019 2357 2145 3716 2411 3511 3292 271 193 92 256 Do the slopes of these variables in the multiple regression provide the full answer? O A. The slopes do not provide any part of the answer. The answer depends on the p-value corresponding to each explanatory variable. 358 324 279 4014 260 4260 3658 3844 3664 99 OB. The slopes provide the full answer. The larger the (positive) slope, the more that variable contributes to the model. OC. The slopes only provide part of the answer. The volume of gallons of gasoline and the number of car washes per day is also important. 165 169 246 251 316 302 182 3900 177 1952 3807 3872 3731 3367 4052 403 307 168 327 (c) Find the variance inflation factor. 2422 2709 2970 3150 3178 2767 2816 3338 VIF(Volume) = VIF(Washes) = (Round to two decimal places as needed.) 278 2361 Interpret the variance inflation factor. Choose the correct answer below. 2160 2065 2076 2196 271 211 181 3603 2567 4028 3115 240 319 324 185 O A. Collinearity has a significant effect on the standard errors. 38 OB. The two variables Volume and Washes are perfectly collinear. One of the variables should be dropped from the model (the one with the lower p-value). c. Collinearity has litle effect on the standard errors. OD. Collinearity has no effect on the standard errors. Print Done (d) One of the explanatory variables is just barely statistically significant. Assuming the same estimated value, would a complete lack of collinearity have made this explanatory variable noticeably more statistically significant?