You are to add the data to a spreadsheet in Excel. Next you will create a graph of the data to see if you see any visible evidence of a relationship between either sales dollars and supplies expense and between sales units and supplies expense. You will then use the Regression tool under the Data ribbon to complete a regression analysis and table. Create a multi-linear regression and then determine if you need to drop either sales units or sales dollars. Determine what the final regression cost equation should be and use that equation to predict what sales expense will be for each month of 2022. 145 Supplies Expense Sales (units) Sales (dollars) June 19 2745 354 20090 July 3200 436 21900 August 3232 525 28780 September 2199 18560 October 2321 199 21680 November 3432 543 21520 December 4278 1189 24630 January 20 2310 212 19990 February 2573 284 21900 March 2487 246 18940 April 2484 278 21340 May 3384 498 32100 June 2945 424 28500 July 2758 312 22650 August 3394 485 24350 September 2254 188 18930 October 2763 276 22320 November 3245 489 30040 December 4576 1045 33090 January 21 2103 104 21950 February 2056 167 20450 March 3874 298 23010 April 2784 398 23450 2345 187 18150 June 2912 334 20940 July 2093 264 19340 August 2873 333 20540 September 2563 143 19770 October 2384 245 18570 November 2476 232 21890 December 3364 1122 34330 January.22 180 16000 May 5 16 17 28 29 BO 31 B2 33 33 34 35 36 37 38 39 40 41 42 43 44 45 January.22 February March April May June July August September October November December 180 230 190 450 350 350 450 550 300 300 450 950 16000 20000 19000 24000 23000 23000 25000 30000 25000 25000 32000 39000