based on the instructions and the report given please only write an introduction to this report. Please follow the guidelines of the first screenshot to complete this. Thank you for your time

Introduction (identify company/industry, problems/issues/questions to be answered/analyzed, models/frameworks to be applied) Follow Case Writing Guidelines in Preparing this case report. Read an article "WHY FORECASTS FAIL" (pested in the CONTENT folder under ADDITIONAL READINGS). Although it mostly discusses forecasting in a general sense of projecting the future, in OM we don't use forecasting to project technology, business cycles or social sciences. We use forecasting to project sales for demand). Think and prepare your response on how this articles translates into specific managerial suggestions for operations managers, who need to rely on sales forecasts to plan their resources allocation to meet future demand. Incorporate your response into the body of the case below. Case of the Western-Eastern Airline. You would need to perform two regression models for each airline (total of four forecasts), One question that arises with this case is whether the data should be merged for the two airlines, resulting in two regressions instead of four. My suggestion is not combining the data and performing regressions separately for each airline. The consultant who was actually hired to analyze the data performed four regressions, while the airline's own internal analysts did merge the data sets. Thus, statisticians can take different views of the same data. As you prepare your case, please include software output as part of the support for your statements. You can put them in appendix/exhibit and refer to them in the body of the case. THE WESTERN-EASTERN AIRLINE In 2019, Western Airlines merged with Eastem Airlines to create the fourth largest U.S. carrier. The new West-East Airline inherited both an aging fleet of Boeing 737-200 aircraft and Sam Rutherford. Ruth was a tough former secretary of the navy who stepped in as new president and chairman of the board. Rutherford's first concem in creating a financially solid company was maintenance costs. It was commonly believed in the airline industry that maintenance costs rose with the age of the aircraft. Rutherford quickly noticed that, historically, there has been a significant difference in reported B737200 maintenance costs (from ATA Form 41s) both in the airframe and engine arcas between Westem Airlines and Eastern Airlines, with Eastem having the newer fleet. On November 12, 2019, Rutherford assigned Page Young, vice president for operations and maintenance, to study the issue Specifically, Rutherford wanted to know (1) whether the average fleet age was correlated to direct airframe maintenance costs and (2) whether there was a relationship between average fleet age and direct engine maintenance costs. Young was to report back with the answer, along with quantitative and graphical descriptions of the relationship, by November 26th First, Young had her staff construct the average age of Western and Eastem B737-200 fleets, by quarter, since the introduction of the aircraft to service by each airline in late 2011 and early 2012. The average age of each fleet was calculated by first multiplying the total number of calendar days that cach aircraft had been in service at the pertinent point in time by the average daily utilization of the respective fleet to total fleet-hours flown. The total fleet-hours flown was then divided by the number of aircraft in service at that time, giving the age of the "average" aircraft in the fleet. The average utilization was found by taking the actual total fleet-hours flown at September 30, 2019, from Western and Eastern data, and dividing by total days in service for all aircraft at that time. The average utilization for Eastern was 8.3 hours per day, and the average utilization for Western was 8.7 hours per day. Because the available cost data were calculated for each yearly period ending at the end of the first quarter, average fleet age was calculated at the same points in time. The fleet data are shown in the following table. Airframe cost data and engine cost data are both shown paired with fleet average age. Year 2014 2015 2016 2017 2018 2019 2020 West-East Airline Data for Boeing 737-200 Jets Western Airlines Data Eastern Airlines Data Airframe Engine Average Airframe Engine Average Cost per Cost per Age Cost per Cost per Age Aircraft Aircraft (hours) Aircraft Aircraft (hours) $51.80 $43.50 6,612 $13.29 $18.86 5,127 54.95 38.60 8,405 25.15 31.558.155 69.70 51.50 11,079 32.18 40.437,370 68.90 58.75 11,719 31.78 23.10 5,883 65.78 45.50 13,279 25.34 19.69 7,170 84.75 50.28 15,224 32.78 32.58 9.374 78.76 18,397 35.56 38.07 8,289 79.65 Dates and names of airlines and individuals have been changed in this case to maintain confidentiality. The data and issues described here are actual. Discussion Question 1. Prepare Page Young's response to Sam Rutherford. In order to prepare the response of Page Young to Sam Rutherford, we need to determine the correlation between 1. Average feet age and airframe cost 2. Average feet age and engine maintenance cost For Western Airlines Data: Correlation between Average fleet age and aitame cost. Here X = Average fleet age and Y = airframes X Values 6612 8405 11079 11719 13279 15224 18397 Y Values 1.8 54.95 69.7 68.9 65.78 84.75 78.76 X.M. YM . -5.70 3897.14 103.14) - M, L M ) 323165.495 21121.24 2. 3156.00 22.14 75136.610 1998.92 360 2. 1.670 26. . 8.2 5.17 2. 1176 1121.5 6.57 . X: X Values Y: Y Values My: Mean of x Values My: Mean of Y Values X-My & Y. My Deviation scores (x - M.)7&V - Myf. Deviation Squared X-MyXY - My): Product of Deviation Scores X Values * 84715 Mean - 12102.143 E(X-My=55x95760364857 Done Y Values 424,64 Mean = 60.663 ENV-MyRESSy4580.875 X and Y Combined 2 of 7 My: Mean of x Values My Mean of Y Values X-My & Y - My Deviation scores (x - M.)7 & (V - Myt: Deviation Squared (X-Myyty - My Product of Deviation Scores X Values 84715 Mean = 12102.143 E(X-My-55x95760364.857 Done Y Values = 424.64 Mean = 60.663 ENV-MyR-SSy4580.875 X and Y Combined N-7 EX - MX/Y - My) - 527022.147 R Calculation EX/X - MyKY - My)/VW/SSK5S. 527022.147 / [(95760364.857) (4580.875)) 0.7957 Meta Numeric (cross-check) 0.7957 Y Values X Values Correlatin hohen Anetad Ervaine maintenance cost Here X = Average floet age and Y = Engine maintenance cost X Values 6612 8405 11079 11719 13279 15224 18397 Y Values 43.5 38.6 51.5 58.75 45.5 50.28 79.65 X.M. - MP X-MXY.M Y-M -9.040 -13.140 -1.04 6.210 -7.00 -617.14) -1923.140 183.145 116.85 3121.857 6296.857 M 1202.183 10141608.02 13668865.100 1046821.10 146791.440 1384932.735 978993.020 125.236.40 SI . Y.MX 11.722 194.124 1.00 1.562 74.92 Sun: 10.112 51538,171 1054. -2379.317 285.0.4 -7955901 170652.577 Sur 2516 27.110 32.54 X: X Values Y: Y Values My: Mean of X Values My: Mean of Y Values X-My & Y - My: Deviation scores (X-M.)7 & (Y - MyP: Deviation Squared X-MX Y - My): Product of Deviation Scores X Values E=84715 Mean = 12102.143 EXX-M. 17-5595760364.857 Y Values XX Values Y: Y Values My: Mean of X Values My: Mean of Y Values X-My & Y. My Deviation scores (X-M.17 & (Y - MyP: Deviation Squared (X-MX Y - My Product of Deviation Scores X Values E-84715 Mean - 12102.143 EXX-M.17-5595760364.857 Y Values E367.78 Mean = 52 54 ENV-MYR 55y1105.312 X and Y Combined N-7 EXX-Ma[Y-My)=255166.92 R Calculation r-EX(X-MyXY - Mx}}/VISS.S.) r=255166.92/V195760364.857) (1105.312)) = 0.7843 Y Values XValues Correlation between Average fleet age and astrame cost Here X-Average fleet age and Yarrame cost X X Values Y Y Values My: Mean of x Values My: Mean of Y Values X-My & Y - My Deviation scores (x-MU7 & IY-M): Deviation Squared 5 of 7 XValues Correlation between Average fleet age and airframe cost Here X = Average fleet age and Y = airframe cost X:X Values Y: Y Values My: Mean of X Values My: Mean of Y Values X-My & Y - My: Deviation scores (x-MJ7 & (Y - My): Deviation Squared (X-M.XY-M.)Product of Deviation Scores X Values E=51368 Mean = 7338.286 E(X-M.)7=55x=12751979.429 Y Values I= 196.08 Mean - 28.011 E(Y - My) SSy = 343,344 X and Y Combined N=7 EX - My XY - My) - 42197 717 R Calculation = E(X - Myyty - My))/VISS.SS.)) r = 42197 717 / ((12751979.429/343.344)) =0.6377 The value of Ris 0.6377 This is a moderate positive correlation, which means there is a tendency for high X variable scores go w high Y variable scores (and vice versa). Y Values X Values Correlation between Average fleet age and Engine Maintenance cost: Here X = Average fleet age and Y = Engine Maintenance cost X: X Values Y: Y Values Mx: Mean of X Values My: Mean of Y Values X-My & Y - My: Deviation scores (X-M.)7 & (Y - My": Deviation Squared (X-MyXY - My): Product of Deviation Scores X Values =51368 Mean = 7338.286 (X-M.12 = 55x = 12751979.429 Y Values E=204.28 Mean = 29.183 INV - M7-55y = 456,3 X and Y Combined N=7 E(X-MyXY - My) = 50931.314 R Calculation r=E((X - My (Y - Mx))/VI(SS.X(55.)) r = 50931.314/((12751979.429)(456.3)) = 0.6677 The value of R is 0.6677. This is a moderate positive correlation, which means there is a tendency for high X variable scores go with high Y varlable scores (and vice versa). Y Values X Values