Can you assit me with this problem 2 by showing me the process step by step. I prefer it not be in the form of an excel sheet because it is hard to follow along. Thank you kindly The image presents two weekly forecast methods used for predicting the number of gallons of gasoline (in thousands) demanded at a local gasoline station. The actual demand levels are also provided. The data is organized in two tables, each corresponding to a different forecast method:**Table 1: Forecast Method 1 vs. Actual Demand**- **Week 1:** Forecast - 0.95, Actual - 0.72- **Week 2:** Forecast - 1.08, Actual - 1.00- **Week 3:** Forecast - 0.95, Actual - 1.07- **Week 4:** Forecast - 1.22, Actual - 0.97**Table 2: Forecast Method 2 vs. Actual Demand**- **Week 1:** Forecast - 0.80, Actual - 0.72- **Week 2:** Forecast - 1.21, Actual - 1.00- **Week 3:** Forecast - 0.92, Actual - 1.07- **Week 4:** Forecast - 1.15, Actual - 0.97Accompanying this data are calculations needed for each method:- **Mean Absolute Deviation (MAD):** Measures the average magnitude of the errors.- **Mean Squared Error (MSE):** Measures the average squared difference between the forecast and actual values.The boxes to calculate these values are left blank, indicating that users may be prompted to fill them with the calculated MAD and MSE values for each method.

Given - Week Forcast Actual Demand 1 0.95 0.72 2 1.08 1.00 3 0.95 1.07 4 1.22 0.97In excel, it will be quite simple but as you requested, please find the solution. Hopefully, it will help you. Firstly, Please find the formulas used to find MAD and MSE Error (Deviation) = Forecast - Actual Demand Absolute Error = |Forecast - Actual Demand| (It means to ignore the negative sign, take only value) Mean absolute deviation (MAD) = Sum ofAbsolute ErrorNo. of Periods or Average of ∑Absolute Error Mean squared error (MSE) = Square of Absolute ErrorNo. of Periods or Absolute Error2No. of Periods For Forecast Method 1: Week Forcast Actual Demand 1 0.95 0.72 2 1.08 1.00 3 0.95 1.07 4 1.22 0.97 Week Forecast Actual Demand Error Absolute Error Square Error 1 0.95 0.72 (0.72 - 0.95) = -0.23 0.23 (0.23)2 = 0.0529 2 1.08 1.00 (1-1.08) = -0.08 0.08 (0.08)2 = 0.0064 3 0.95 1.07 (1.07-0.95) = 0.12 0.12 (0.12)2 = 0.0144 4 1.22 0.97 (0.97-1.22) = -0.25 0.25 (0.25)2 = 0.0625 Here No. of Period = 4 (Week 1 to 4) So, MAD = 0.23+0.08+0.12+0.254 =0.684=0.17 MSE = 0.0529+0.0064+0.0144+0.06254 = 0.13624=0.03405~0.034 For Forecast Method 2: Week Forcast Actual Demand 1 0.80 0.72 2 1.21 1.00 3 0.92 1.07 4 1.15 0.97 Week Forecast Actual Demand Error Absolute Error Square Error 1 0.80 0.72 (0.72 - 0.80) = -0.08 0.08 (0.08)2 = 0.0064 2 1.21 1.00 (1-1.21) = -0.21 0.21 (0.21)2 = 0.0441 3 0.92 1.07 (1.07-0.92) = 0.15 0.15 (0.15)2 = 0.0225 4 1.15 0.97 (0.97-1.15) = -0.18 0.18 (0.18)2 = 0.0324 So, MAD = 0.08+0.21+0.15+0.184=0.624=0.155 MSE = 0.0064+0.0441+0.0225+0.03244=0.10544=0.02635~0.026

Business

Operations Management

Following are two weekly forecasts made by two different methods for the number of gallons of gasoline, in thousands, demanded at a local gasoline station. Also shown are actual demand levels, in thousands of gallons: Forecast Forecast Method 1 Actual Demand Actual Demand Week Week Method 2 1 0.95 0.72 0.80 0.72 1.08 1.00 1.21 1.00 3 0.95 1.07 0.92 1.07 4 1.22 0.97 4 1.15 0.97 The MAD for Method 1 = thousand gallons (round your response to three decimal places). The mean squared error (MSE) for Method 1 = thousand gallons (round your response to three decimal places). The MAD for Method 2 = thousand gallons (round your response to three decimal places). The mean squared error (MSE) for Method 2 = thousand gallons (round your response to three decimal places).

Following are two weekly forecasts made by two different methods for the number of gallons of gasoline, in thousands, demanded at a local gasoline station. Also shown are actual demand levels, in thousands of gallons: Forecast Forecast Method 1 Actual Demand Actual Demand Week Week Method 2 1 0.95 0.72 0.80 0.72 1.08 1.00 1.21 1.00 3 0.95 1.07 0.92 1.07 4 1.22 0.97 4 1.15 0.97 The MAD for Method 1 = thousand gallons (round your response to three decimal places). The mean squared error (MSE) for Method 1 = thousand gallons (round your response to three decimal places). The MAD for Method 2 = thousand gallons (round your response to three decimal places). The mean squared error (MSE) for Method 2 = thousand gallons (round your response to three decimal places).

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter2: Introduction To Spreadsheet Modeling

Section: Chapter Questions

Problem 20P: Julie James is opening a lemonade stand. She believes the fixed cost per week of running the stand...

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Question

Can you assit me with this problem 2 by showing me the process step by step. I prefer it not be in the form of an excel sheet because it is hard to follow along. Thank you kindly

The image presents two weekly forecast methods used for predicting the number of gallons of gasoline (in thousands) demanded at a local gasoline station. The actual demand levels are also provided. The data is organized in two tables, each corresponding to a different forecast method:

**Table 1: Forecast Method 1 vs. Actual Demand**
- **Week 1:** Forecast - 0.95, Actual - 0.72
- **Week 2:** Forecast - 1.08, Actual - 1.00
- **Week 3:** Forecast - 0.95, Actual - 1.07
- **Week 4:** Forecast - 1.22, Actual - 0.97

**Table 2: Forecast Method 2 vs. Actual Demand**
- **Week 1:** Forecast - 0.80, Actual - 0.72
- **Week 2:** Forecast - 1.21, Actual - 1.00
- **Week 3:** Forecast - 0.92, Actual - 1.07
- **Week 4:** Forecast - 1.15, Actual - 0.97

Accompanying this data are calculations needed for each method:
- **Mean Absolute Deviation (MAD):** Measures the average magnitude of the errors.
- **Mean Squared Error (MSE):** Measures the average squared difference between the forecast and actual values.

The boxes to calculate these values are left blank, indicating that users may be prompted to fill them with the calculated MAD and MSE values for each method.

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