can you help with 11-28. this needs to go in forecasting model. I think it is supposed to be like the 11-20 example I attached. ihave a deadline of 11pm if possible. thank you no more problems after this.

11-28 Consider the patient data for Dr. Schalkoff given in Problem 11-27. (a) Draw the relationship between the crime rate and Dr. Schalkoff's patient load. Is a linear model between these two variables reasonable? (b) Apply linear regression to study the relationship between the crime rate and Dr. Schalkoff's patient load. (c) If the crime rate increases to 140.2 in year 11, how many patients will Dr. Schalkoff treat? (d) If the crime rate drops to 98.6, what is the patient projection? 11-27 example Highland Automotive Forecasting Weighted moving averages - 3 period moving average Data Week Weights Demand 1 2 3 4 5 6 7 8 9 10 11 12 22 26 23 27 21 25 28 26 29 29 27 31 Next period 29.4444444 Forecasts and Error Analysis Forecast Error Absolute Squared Abs Pct Err 1 3 5 23.8889 3.1111 3.1111 9.6790 11.52% 25.5556 -4.5556 4.5556 20.7531 21.69% 23.22222 1.777778 1.777778 3.160494 07.11% 23.88889 4.111111 4.111111 16.90123 14.68% 26.2222 -0.2222 0.2222 0.0494 00.85% 26.55556 2.444444 2.444444 5.975309 08.43% 27.8889 1.1111 1.1111 1.2346 03.83% 28.6667 -1.6667 1.6667 2.7778 06.17% 27.8889 3.1111 3.1111 9.6790 10.04% Total 9.2222 22.1111 70.2099 84.33% Average 1.0247 2.4568 7.8011 09.37% Bias MAD MSE MAPE SE 3.16701 Highland Automotive Forecasting Alpha Data Week Exponential smoothing 0.45 Demand 1 2 3 4 5 6 7 8 9 10 11 12 22 26 23 27 21 25 28 26 29 29 27 31 Next period 25.9052417 Forecasts and Error Analysis Forecast Error Absolute 23 -1 1 22.55 3.45 3.45 24.1025 -1.1025 1.1025 23.60638 3.393625 3.393625 25.13351 -4.133506 4.133506 23.27343 1.726572 1.726572 24.05039 3.949614 3.949614 25.82771 0.172288 0.172288 25.90524 3.094758 3.094758 27.29788 1.702117 1.702117 28.06384 -1.063836 1.063836 27.58511 3.41489 3.41489 Total 13.60402 28.20371 Average 1.133669 2.350309 Bias MAD SE Squared 1 11.9025 1.215506 11.51669 17.08587 2.981049 15.59945 0.029683 9.577529 2.897203 1.131746 11.66148 86.59871 7.216559 MSE 2.942766 Abs Pct Err 04.55% 13.27% 04.79% 12.57% 19.68% 06.91% 14.11% 0.00662646 0.1067158 0.05869369 0.03940132 0.11015776 108.03% 09.00% MAPE Midwestern Manufacturing Forecasting Data Week 1 2 3 4 5 6 7 8 9 10 11 12 Intercept Slope Regression/Trend analysis Demand (y) Period(x) 22 1 26 2 23 3 27 4 21 5 25 6 28 7 26 8 29 9 29 10 27 11 31 12 22.1212121 0.62237762 Next period 30.2121212 Forecasts and Error Analysis Forecast Error Absolute 22.74359 -0.7435897436 0.7435897 23.3660 2.6340 2.6340 23.9883 -0.9883 0.9883 24.6107 2.3893 2.3893 25.2331 -4.2331 4.2331 25.8555 -0.8555 0.8555 26.4779 1.5221 1.5221 27.1002 -1.1002 1.1002 27.7226 1.2774 1.2774 28.3450 0.6550 0.6550 28.9674 -1.9674 1.9674 29.5897 1.4103 1.4103 Total -3.552714E-015 19.7762 Average -2.960595E-016 1.6480 Bias MAD SE Squared 0.5529257 6.9381 0.9768 5.7086 17.9191 0.7318 2.3169 1.2105 1.6317 0.4290 3.8705 1.9888 44.2751 3.6896 MSE 2.1041639 13 Correlatio 0.7454989 The linear equation is: Y = 0.622X + 22.12 Abs Pct Err 03.38% 10.13% 04.30% 08.85% 20.16% 03.42% 05.44% 04.23% 04.40% 02.26% 07.29% 04.55% 78.40% 06.53% MAPE Midwestern Manufacturing It is convenient to use 3 -week weighted moving average technique because the exponential smoothing model predicts a very low value for trend, this trend is not linear as it is very low correlation values over time, then linear trend analysis is not useful ed moving ial smoothing d, this trend is values over ot useful