| Period | Price |
| 1 | 82.87 |
| 2 | 83 |
| 3 | 83.61 |
| 4 | 83.15 |
| 5 | 82.84 |
| 6 | 83.99 |
| 7 | 84.55 |
| 8 | 84.36 |
| 9 | 85.53 |
| 10 | 86.54 |
| 11 | 86.89 |
| 12 | 87.77 |
| 13 | 87.29 |
| 14 | 87.99 |
| 15 | 88.8 |
| 16 | 88.8 |
| 17 | 89.11 |
| 18 | 89.1 |
| 19 | 88.9 |
| 20 | 89.21 |
a) Using Excel, go to Data, select Data Analysis, choose Regression. Put period in X input range and Price in Y input range. Tick Residuals.
| SUMMARY OUTPUT | |||||
| Regression Statistics | |||||
| Multiple R | 0.970857528 | ||||
| R Square | 0.94256434 | ||||
| Adjusted R Square | 0.93937347 | ||||
| Standard Error | 0.603819028 | ||||
| Observations | 20 | ||||
| ANOVA | |||||
| df | SS | MS | F | Significance F | |
| Regression | 1 | 107.6999465 | 107.6999 | 295.3942 | 1.29551E-12 |
| Residual | 18 | 6.562753534 | 0.364597 | ||
| Total | 19 | 114.2627 | |||
| Coefficients | Standard Error | t Stat | P-value | ||
| Intercept | 81.98942105 | 0.280492932 | 292.3048 | 1.51E-34 | |
| Period | 0.40243609 | 0.023415098 | 17.18703 | 1.3E-12 |
Price = 81.989 + 0.402*Period
b) For December 2, Period = 21
Forecast of 2nd December = 81.989 + 0.402*21 = 90.43
c) Durbin Watson
Using Excel:

Durbin Watson = SUMXMY2(F26:F44,F25:F43)/SUMSQ(F25:F44) = 0.798
dL = 1.2, dU = 1.41
H0: There is auto-correlation
H1: There is no auto-correlation
Since 0.798 falls between dL and dU, we do not reject the null hypothesis and conclude that auto-correlation is present.
83.99 14.55 8436 85.53 Date Now Nov, a Now, 7 Now Nov.9 Nov. 10 Nov. 11...
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Company Ticker
Years
Yield
GE
1.00
0.767
MS
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1.716
WFC
1.35
0.897
TOTAL
1.75
1.378
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3.558
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4.413
JPM
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2.310
C
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RABOBK
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2.805
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4.739
AXP
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MTNA
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BAC
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