| ISOM 201 Chapters 4 &5: Project 1 Prof. Brooks | | | | |
| Due Date: On blackboard by October 25, 11:59PM |
| Use the given data to do the followings. Show all the calculations in Excel spreadsheets. You may use the exercise template to help set it up. Put each part on a new sheet. |
| Part 1: Regression Model |
| 1. Create a scatter plot, and the trendline . |
| 2. Develop a regression model; find b0 and b1. Show a table with the calculations. |
| 3. Find SST, SSR, SSE and r^2. Explain the meaning of the r^2 value for this model. |
| 4. Find MAD. |
| 5. Find MSR, MSE, and F_calculated |
| 6. Use the significance level of 5% to determine whether or not the Y values depend on the X values. |
| Part 2: Averages |
| 1. Develop a forecast using a 4-month moving average. Find MAD. |
| 2. Develop a forecast using a weighted 4-month moving average in which the revenue in the most recent month is given a weight 2 and revenue in the other 3 months is each given a weight of 1. Find MAD. |
| 3. Develop a forecast using an exponential smoothing with smoothing constant of 0.4. Assuming the forecast for January of 2013 is $445,000. Find MAD. |
| 4. Develop a forecast using an exponential smoothing with trend. Use smoothing constant of 0.4 for forecast, and smoothing constant of 0.3 for trend. Assuming the forecast for January of 2013 is $445,000. Find MAD. |
| Part 3: Decomposition Method for data with Trend and Seasonal variations |
| Use the decomposition model to incorporate both trend and seasonal components into the forecast. |
| 1. Find CMA, seasonal ratio, seasonal indices, deseasonalized revenue. Start CMA in the month of July (half way between one January to the next). |
| 2. Find the equation of a regression line (trend line) using the deseasonalized data (with the seasonal elements taken away). Show graph. |
| 3. Use the regression line to find the Y_reg values for revenue in 2013-2016. |
| 4. Find the final forecast (with the seasonal elements added) for the revenue in 2013-2016. |
| 5. Find MAD. |
| Part 4: The best forecast. |
| 1. Compare the forecasting methods from Part 1 - Part 3. Which one is the best method to use for this problem? Why? |
| 2. Use the best forecasting method to create the revenue forecast for the year 2016 (January-December). |
| Part 5: Do Problem #31 on page 145 |
| After the best model is found, predict the number of victories using the following values, where they are applicable: ERA = 4.5, R = 750, AVG = 0.260, ORP = 0.320 |
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project_1-chapters452_1.xlsx
