Multiple Linear Regression - Estimated Regression Equation
SP[t] = -250.155 + 73.3043V[t] -0.171478SQ[t] + 27.6031BR[t] -78.6611PH[t] + 50.6011GR[t] -6.22991PK[t] + 8.44973AG[t] + 82.5753Re[t] + 1.15958As[t] + 137.4C[t] -25.7549TH[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)-250.2 103.1-2.4270e+00 0.02597 0.01298
V+73.3 49.59+1.4780e+00 0.1567 0.07834
SQ-0.1715 0.09469-1.8110e+00 0.08687 0.04343
BR+27.6 45.97+6.0050e-01 0.5556 0.2778
PH-78.66 47.75-1.6470e+00 0.1168 0.05842
GR+50.6 50.83+9.9540e-01 0.3327 0.1664
PK-6.23 46.29-1.3460e-01 0.8944 0.4472
AG+8.45 3.18+2.6570e+00 0.01605 0.008023
Re+82.58 48.56+1.7010e+00 0.1062 0.05312
As+1.16 0.1378+8.4140e+00 1.185e-07 5.925e-08
C+137.4 42.22+3.2540e+00 0.004405 0.002202
TH-25.75 70.41-3.6580e-01 0.7188 0.3594


Multiple Linear Regression - Regression Statistics
Multiple R 0.9833
R-squared 0.9669
Adjusted R-squared 0.9467
F-TEST (value) 47.86
F-TEST (DF numerator)11
F-TEST (DF denominator)18
p-value 5.011e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 77.03
Sum Squared Residuals 1.068e+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
1 955 986.7-31.73
2 960 915.7 44.27
3 1156 1166-9.75
4 1433 1429 4.177
5 769 826.4-57.38
6 1310 1352-41.58
7 840 818.5 21.45
8 1590 1488 102.4
9 716 686.6 29.36
10 892 981.8-89.84
11 1717 1651 66.46
12 930 901.5 28.52
13 920 1029-109.3
14 838 796.6 41.39
15 1310 1192 117.6
16 1275 1293-17.88
17 1780 1792-12.03
18 1148 1112 36.01
19 900 924-23.98
20 839 824.5 14.5
21 1975 1986-10.56
22 865 905.4-40.42
23 1018 960.7 57.35
24 1550 1525 25.15
25 1190 1248-58.24
26 1182 1201-18.76
27 783 686.2 96.77
28 920 1070-149.6
29 788 761.2 26.76
30 1193 1234-41.16


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
15 0.4679 0.9359 0.5321


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 = 7.285, df1 = 2, df2 = 16, p-value = 0.005631
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = -0.36952, df1 = 22, df2 = -4, p-value = NA
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.3125, df1 = 2, df2 = 16, p-value = 0.736


Variance Inflation Factors (Multicollinearity)
> vif
        V        SQ        BR        PH        GR        PK        AG        Re 
 1.639363 17.057586  3.335171  1.601112  1.509764  2.696835  3.891731  2.400751 
       As         C        TH 
10.963412  2.243383  2.896230