ha - Naive 2 - MA 43 - WMA #4 - SES #5 - Regression #6 - Sesonality Summary H Regression - compute a linear regression forecast 0 5 Y = a + bx (a= intercept, b=slope) 2 Yand intercept Slope HINT INTERSEPTE...) HINTI Slot.) ABS Percentage Aburro) ener Error Square Month Jana Period Historical Sales Repression Error Linear Regression 2 1 2018 March April M June AN AL Sebe 5 6 7 16 16 Nov D 10 11 12 20 Wann 22 22 M 2019 23 ALL Bebe O ve Det January 20 30 31 39 14 15 14 17 18 19 20 21 22 33 20 25 26 27 20 29 30 11 32 31 34 35 36 Mar Art MU Ju 2020 AU 20 ver Decoreer Regression - compute a linear regression forecast Y = a + bx (a= intercept, b=slope) Yaxis intercept Slope HINT: INTERSUPTC.) HINTE SLOPE ARS Percentage Abs[error) error Linear Regression Error Error Squared TDOO 16.000 14000 12.000 10.000 EOS 000 4000 2000 0 5 15 Historical Yra Month Period Sales Regression nary 1 4,302 February 2 3.336 March 3 7.700 April 4 11,820 MY 513.890 Pune 11.902 July 10.951 At 10,503 Septembe 7,883 Oder 10 6957 November 11 5820 December 12 5,164 January 4,381 February 14 3,677 March 8.506 Wil 16 13.789 17 14,306 2015 18 12,504 July 19 12.170 Aug 20 11.301 Septembe 21 8.557 October 22 6.905 November 23 0,685 December 24 5,300 nary 25 4795 Fy 26 4.062 March 27 5,645 Apr 18,150 May 29 15.450 2000 lun 30 14070 Historical Sales Data 20 Hotel #1 - Naive #2 - MA W3 - WMA #4 - SES #5 - Regression #6 - Seaonality Summary Ready