please help me to answers this excel sheets. my subject in Economic in business decision. need to fill the green area with formula. then, Analysis (Which among the models best implies to the given data set, explain ): this activity is need to work in excel sheet with formulas.

1st sheet ( Naive model)

G A B E F C D Time (t) Sales (Xt) Forecast F(t) Error e(t) Absolute Error le(t)| Absolute Percentage Error le(t)/X(t)| *100 Squared Error {e(t)} 2 28 27 WN H 33 27 25 33 5 34 25 33 34 35 33 8 30 35 10 9 33 30 11 10 35 33 12 11 27 35 13 12 29 27 14 Totals 15 n 16 MAD 17 MAPE 18 MSE 19 20 21 22 23 24 25 26 27 28 29 30 1 4 NAIVE MODEL MOVING AVERAGE WEIGHTED MOVING AVERAGE SIMPLE EXPONENTIAL SMOOTHING SummaryG C E F A B D Time (t) Sales (Xt) Forecast F(t) Error e(t) Absolute Error le(t)| Absolute Percentage Error le(t)/X(t)| *100 Squared Error {e(t)}2 28 27 28 W NF 33 27 25 33 5 34 25 33 34 7 35 33 LD 00 00 30 35 10 9 33 30 11 10 35 33 12 11 27 35 13 12 29 27 14 Totals 15 n 16 MAD 17 MAPE 18 MSE 19 20 21 22 23 24 25 26 27 28 29 30 14 4 NAIVE MODEL MOVING AVERAGE WEIGHTED MOVING AVERAGE SIMPLE EXPONENTIAL SMOOTHING SummaryB E F G A C D Sales (X) Forecast F(t) Error e(t) Absolute Error le(t)| Absolute Percentage Error le(t)/X(t)| *100 Squared Error {e(t)} 2 Time (t) 1 28 27 33 25 30.20 34 33 35 LD 00 30 10 33 11 10 35 12 11 27 13 12 29 14 Totals 15 n 16 MAD 17 MAPE 18 MSE 19 20 21 22 23 24 25 26 27 28 29 30 SIMPLE EXPONENTIAL SMOOTHING Summary 1 4 NAIVE MODEL MOVING AVERAGE | WEIGHTED MOVING AVERAGEA D E G B C F Time (t) Sales (X) Forecast F(t) Error e(t) Absolute Error le(t)| Absolute Percentage Error le(t)/X(t)| *100 Squared Error {e(t)} 2 28 30 -2 WON 27 29.80 33 25 INQUI AWN 34 33 35 LD DO 30 10 33 11 10 35 12 11 27 13 12 29 14 Totals 15 n 16 MAD 17 MAPE 18 MSE 19 20 21 22 23 24 25 26 27 28 29 30 1 4 |NAIVE MODEL MOVING AVERAGE WEIGHTED MOVING AVERAGE SIMPLE EXPONENTIAL SMOOTHING SummaryA B C D E F G H K L M N Dawn Wright: Mean square error [MISE] is probably the Dawn Wright: most commonly used error metric. It 1838888 The mean absolute deviation (MAD) is penalizes larger errors because squaring Dawn Wright: Dawn Wright: the sum of absolute differences larger numbers has a greater impact than the root mean square Mean Absolute Percentage between the actual value and the squaring smaller numbers. The MSE is the error (RMSE) is the square Error (MAPE) is the average of forecast divided by the number of sum of the squared errors divided by the root of the MSE absolute errors divided by observations. number of observations actual observation values. 1 At - Fe At MAD = MSE = RMSE = MAPE = x 100 2 n n 3 Absolut Absolute Values of e Value Errors Divided by Actual 4 Period Actual Forecast Error of Error Square of Error Values. t A A -F. I A - F.I ( A -F. )^2 I (A -F.)/Al 27.580 27.580 0.000 0.000 0.000 0.0000 25.950 26.765 -0.815 0.815 0.664 0.0314 26.080 26.015 0.065 0.065 0.004 0.0025 9 26.360 26.220 0.140 0.140 0.020 0.0053 10 27.990 27.175 0.815 0.815 0.664 0.0291 11 6 29.610 28.800 0.810 0.810 0.656 0.0274 12 28.850 29.230 -0.380 0.380 0.144 0.0132 13 29.430 29.140 0.290 0.290 0.084 0.0099 14 29.670 29.550 0.120 0.120 0.014 0.0040 15 10 30.190 29.930 0.260 0.260 0.068 0.0086 16 11 31.790 30.990 0.800 0.800 0.640 0.0252 17 12 31.980 31.885 0.095 0.095 0.009 0.0030 18 Totals 2.200 4.590 2.968 0.160 19 12 20 MAD 0.383 21 MSE 0.247 2.2 RMSE 0.497 23 MAPE 1.33