Multiple Linear Regression - Estimated Regression Equation
SP[t] = -437.526 + 112.392V[t] + 1.50667SQ[t] -0.000205937SQ2[t] + 43.4069BR[t] -71.6116PH[t] -10.2263AG[t] + 108.215C[t] -260.046TH[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)-437.5 129.3-3.3830e+00 0.002678 0.001339
V+112.4 49.62+2.2650e+00 0.03371 0.01685
SQ+1.507 0.1468+1.0260e+01 7.536e-10 3.768e-10
SQ2-0.0002059 2.814e-05-7.3190e+00 2.503e-07 1.252e-07
BR+43.41 40.72+1.0660e+00 0.298 0.149
PH-71.61 48.88-1.4650e+00 0.1571 0.07854
AG-10.23 2.95-3.4670e+00 0.002192 0.001096
C+108.2 44.03+2.4580e+00 0.02234 0.01117
TH-260.1 77.72-3.3460e+00 0.002926 0.001463


Multiple Linear Regression - Regression Statistics
Multiple R 0.9733
R-squared 0.9472
Adjusted R-squared 0.928
F-TEST (value) 49.35
F-TEST (DF numerator)8
F-TEST (DF denominator)22
p-value 2.779e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 88.11
Sum Squared Residuals 1.708e+05


Menu of Residual Diagnostics
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
1 955 1043-88.18
2 960 976-15.99
3 1156 1225-68.99
4 1433 1364 68.81
5 769 796.2-27.24
6 1310 1364-53.8
7 840 811.3 28.72
8 1590 1505 84.57
9 716 769.4-53.39
10 892 902.8-10.79
11 1717 1510 206.6
12 930 913.8 16.17
13 920 1055-135.3
14 838 811 27.03
15 1310 1167 142.7
16 1275 1291-15.79
17 1780 1860-79.85
18 1148 1132 15.55
19 900 865.2 34.83
20 839 838 1.048
21 1975 1973 1.902
22 865 897.3-32.33
23 1018 1009 8.913
24 1550 1590-39.75
25 1190 1242-52.27
26 1182 1213-30.97
27 1198 1091 106.7
28 783 685.8 97.16
29 920 1057-136.8
30 788 753.7 34.33
31 1193 1227-33.57


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
12 0.0944 0.1888 0.9056
13 0.7353 0.5294 0.2647
14 0.7101 0.5798 0.2899
15 0.8993 0.2014 0.1007
16 0.8186 0.3628 0.1814
17 0.8727 0.2547 0.1273
18 0.8009 0.3982 0.1991
19 0.6656 0.6688 0.3344


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level00OK
10% type I error level00OK


Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 3.1711, df1 = 2, df2 = 20, p-value = 0.06365
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.32075, df1 = 16, df2 = 6, p-value = 0.9678
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.51573, df1 = 2, df2 = 20, p-value = 0.6048


Variance Inflation Factors (Multicollinearity)
> vif
        V        SQ       SQ2        BR        PH        AG         C        TH 
 1.315653 31.548096 22.832638  2.039700  1.290921  2.729898  1.933849  2.711129