Need help answering please Total StaffRemoteStandbyHours (Y) Present (X,) Hours (X,)245338414177333596270358656211372631196339528135289409195334382118293399116325343147311338154304353146312289115283388161307402274322151245335228201350271183339440237327475175328347152319449188325336188197322267317235261315164232331270 The business problem facing the director of broadcasting operations for a television station was the issue of standby hours(i.e. hours in which unionized graphic artists at the station are paid but are not actually involved in any activity) and whatfactors were related to standby hours. A study of standby hours was conducted for 26 weeks. The variables in the study aredescribed below and the data from the study are shown in the accompanying table. Complete parts a through g below.Standby hours (Y)-Total number of standby hours in a weekTotal staff present (X,)-Weekly total of people-daysRemote hours (X,)-Number of hours worked by employees off-sited. Predict the mean standby hours for a week in which the total staff present have 310 people-days and the remote hoursare 400.There would bestandby hours predicted for the week.(Round to two decimal places as needed.)e. Construct a 95% confidence interval estinate for the mean standby hours for weeks in which the total staff present have310 people-days and the remote hours are 400.The 95% confidence interval estimate is sHyxS-(Round to two decimal places as needed.)f. Construct a 95% prediction interval for the standby hours for a single week in which the total staff present have 310people-days and the remote hours are 400.The 95% prediction interval for the standby hours is SYxs.(Round to two decimal places as needed.)g. What conclusions can you reach concerning standby hours?A. The model uses the number of remote hours to predict the number of standby hours. The number of staff presentonly affects the remote hours directly.B. The model uses both the number of staff present and the remote hours to predict the number of standby hours. Thisproduces a better model than if only one variable were included.

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urs (Y) resent (X1 245 338 414 177 333 596 270 358 656 211 372 631 196 339 528 135 289 409 195 334 382 118 293 399 116 325 343 147 311 338 154 304 353 146 312 289 115 283 388 161 307 402