Assignment Excel 5
prince Johnson
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Hints for Exercise 4.xls
C1P2
| BUSI/BMIS 405 | ||||
| Chapter 4: Introuduction to Forecasting with Regression Methods | ||||
| 1. Homework: Exercises 4, 6, 9, 11, and 14 | ||||
| Exercise 4: | ||||
| a. t-ratio=coefficient/standard error (p. 183)(correction H1: β ≠ 0, p.183) | ||||
| The rule of thumb for significance testing is if the absolute value of t-ratio is greater than 2 | ||||
| then, the coefficient is statisically significant which means the coefficient is significantly different from zero. | ||||
| For example, t-ratio for "Constant" is 3.04 which is greater than 2. Therefore, the coefficient of 20,720 is | ||||
| significantly different from zero. | ||||
| If we know the sample size of this problem, we could use table 2.5 for t-test. | ||||
| b. It is R-squared right below the table | ||||
| c. Substitute 20 into EXP in the following equation | ||||
| Salary = 20,720 + 805 (EXP) for point estimate | ||||
| the approximate 95 percent confidence interval estimates would be | ||||
| point estimate ± 2 (SEE) (p. 184) | ||||
| SEE is the standard erro of the estimate which is right under R-squared value | ||||
| Exercise 6: | ||||
| a. The equation is | ||||
| Booking = b0 + b1(Income) | ||||
| Booking is the dependent variable and Income is the independent variable | ||||
| To obtian the regression eq;uation using Forecast X: | ||||
| First, highlight the data for "Bookings" and "Income." Do not highlight "Location" | ||||
| Open Forecast X > Forecast Method > Choose "Multiple Regression" under "Forecast Technique" > Report > Audit>Finish | ||||
| b. Please see pp. 178-184. The three steps of evaluation are summarized | ||||
| at the end of page 183 and the beginning of page 184. | ||||
| c. For point and interval estimates, please refer to exercise 4c. | ||||
| Exercise 9: | ||||
| a. Naïve forecat for 2008 Q1 = actual value in 2007 Q4 | ||||
| b. Highlight the following data. | ||||
| Period | FURN ($Billions) | |||
| Mar-98 | 98.1 | |||
| Jun-98 | 96.8 | |||
| Sep-98 | 96 | |||
| Dec-98 | 95 | |||
| Mar-99 | 93.2 | |||
| Jun-99 | 95.1 | |||
| Sep-99 | 96.2 | |||
| Dec-99 | 98.4 | |||
| Mar-00 | 100.7 | |||
| Jun-00 | 104.4 | |||
| Sep-00 | 108.1 | |||
| Dec-00 | 111.1 | |||
| Mar-01 | 114.3 | |||
| Jun-01 | 117.2 | |||
| Sep-01 | 119.4 | |||
| Dec-01 | 122.7 | |||
| Mar-02 | 125.9 | |||
| Jun-02 | 129.3 | |||
| Sep-02 | 132.2 | |||
| Dec-02 | 136.6 | |||
| Mar-03 | 137.4 | |||
| Jun-03 | 141.4 | |||
| Sep-03 | 145.3 | |||
| Dec-03 | 147.7 | |||
| Mar-04 | 148.8 | |||
| Jun-04 | 150.2 | |||
| Sep-04 | 153.4 | |||
| Dec-04 | 154.2 | |||
| Mar-05 | 159.8 | |||
| Jun-05 | 164.4 | |||
| Sep-05 | 166.2 | |||
| Dec-05 | 169.7 | |||
| Mar-06 | 173.7 | |||
| Jun-06 | 175.5 | |||
| Sep-06 | 175 | |||
| Dec-06 | 175.7 | |||
| Mar-07 | 181.4 | |||
| Jun-07 | 180 | |||
| Sep-07 | 179.7 | |||
| Dec-07 | 176.3 | |||
| Open Forecast X > Forecast Method > Choose "Trend (Linear) Regression" under "Forecast Technique" | ||||
| c. Please refer to exercise 6b. | ||||
| d. Substitue "Time" value into the regression equation for trend forecast. | ||||
| Exercise 10: | ||||
| c. Percentage error in forecat = (actual value - point estimate) / actual value = forecast error / actual value | ||||
| Exercise 11: | ||||
| You will arrange the data in two columns. One for "Year" and one for "Population" for regression analysis | ||||
| Year | Population | |||
| Open Forecast X > Forecast Method > Choose "Trend (Linear) Regression" | ||||
| Exercise 14: | ||||
| a.For trend forecat, open Forecast X > Forecast Method > Choose "Trend (Linear) Regression" | ||||
| b. To reseasonalize the data, time the value with the corresponding seasonal index | ||||
| For example, for Jan-07, actual shoe store sales SSS=SASSS x 0.74 = 2,317 x 0.74=1,714.58 |
End month of quarter
