Use the new M3 model (from Mini Case 2) updated with week 9 data: y^= b0+ b1x1+ b2x2+ b3x3+ b4x4+ b5x5+ b6x6+ b7t to predict the total demand (in units) for week 10(days 64-70). Specifically: 1. Run the regression analysis on the 9-week data (days 1-63). Provide output in cell M14 below. 2. In cells C65 to C71 calculate the estimated demand values for week 10(days 64-70) using the coefficients from the regression output. Date Weekday Daily Demand t Mon Tue Wed Thu Fri Sat 29-Apr Mon 297110000030-Apr Tue 29320100001-May Wed 32730010002-May Thu 31540001003-May Fri 34850000104-May Sat 44760000015-May Sun 43170000006-May Mon 28381000007-May Tue 32690100008-May Wed 317100010009-May Thu 3451100010010-May Fri 3551200001011-May Sat 4281300000112-May Sun 4541400000013-May Mon 3051510000014-May Tue 3101601000015-May Wed 3501700100016-May Thu 3081800010017-May Fri 3661900001018-May Sat 4602000000119-May Sun 4272100000020-May Mon 2912210000021-May Tue 3252301000022-May Wed 3542400100023-May Thu 3222500010024-May Fri 4052600001025-May Sat 4422700000126-May Sun 4542800000027-May Mon 3182910000028-May Tue 2983001000029-May Wed 3553100100030-May Thu 3553200010031-May Fri 374330000101-Jun Sat 447340000012-Jun Sun 463350000003-Jun Mon 291361000004-Jun Tue 319370100005-Jun Wed 333380010006-Jun Thu 339390001007-Jun Fri 416400000108-Jun Sat 475410000019-Jun Sun 4594200000010-Jun Mon 3194310000011-Jun Tue 3264401000012-Jun Wed 3564500100013-Jun Thu 3404600010014-Jun Fri 3954700001015-Jun Sat 4654800000116-Jun Sun 4534900000017-Jun Mon 3075010000018-Jun Tue 3245101000019-Jun Wed 3505200100020-Jun Thu 3485300010021-Jun Fri 3845400001022-Jun Sat 4745500000123-Jun Sun 4855600000024-Jun Mon 3115710000025-Jun Tue 3415801000026-Jun Wed 3575900100027-Jun Thu 3636000010028-Jun Fri 3906100001029-Jun Sat 4906200000130-Jun Sun 492630000001-Jul Mon 641000002-Jul Tue 650100003-Jul Wed 660010004-Jul Thu 670001005-Jul Fri 680000106-Jul Sat 690000017-Jul Sun 70000000