Multiple Linear Regression - Estimated Regression Equation
a[t] = + 165.778 + 52.1111b[t] + 43.2222c[t] + 23.8889d[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)+165.8 37.78+4.3880e+00 0.0003165 0.0001582
b+52.11 11.89+4.3820e+00 0.0003204 0.0001602
c+43.22 21.7+1.9920e+00 0.06095 0.03048
d+23.89 13.28+1.7990e+00 0.08788 0.04394


Multiple Linear Regression - Regression Statistics
Multiple R 0.7993
R-squared 0.6388
Adjusted R-squared 0.5818
F-TEST (value) 11.2
F-TEST (DF numerator)3
F-TEST (DF denominator)19
p-value 0.0001863
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 44.51
Sum Squared Residuals 3.764e+04


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
1 199 285-86
2 395 380.3 14.67
3 245 285-40
4 249 285-36
5 375 380.3-5.333
6 369 356.7 12.33
7 325 285 40
8 319 285 34
9 395 337.1 57.89
10 395 337.1 57.89
11 399 413.1-14.11
12 320 337.1-17.11
13 320 285 35
14 320 361-41
15 325 389.2-64.22
16 499 413.1 85.89
17 369 413.1-44.11
18 450 456.3-6.333
19 399 389.2 9.778
20 399 389.2 9.778
21 439 456.3-17.33
22 385 380.3 4.667
23 390 380.3 9.667