Multiple Linear Regression - Estimated Regression Equation
SP[t] = -178.397 + 69.2751V[t] -0.175723SQ[t] + 25.7879BR[t] -100.782PH[t] + 6.02176AG[t] + 105.971Re[t] + 1.17152As[t] + 105.094C[t] -1.16872T[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)-178.4 105.4-1.6930e+00 0.106 0.05299
V+69.28 44.05+1.5730e+00 0.1315 0.06574
SQ-0.1757 0.08091-2.1720e+00 0.04208 0.02104
BR+25.79 35.95+7.1720e-01 0.4815 0.2408
PH-100.8 41.47-2.4300e+00 0.02462 0.01231
AG+6.022 3.747+1.6070e+00 0.1237 0.06186
Re+106 45.24+2.3430e+00 0.02961 0.01481
As+1.171 0.127+9.2240e+00 1.209e-08 6.043e-09
C+105.1 39.14+2.6850e+00 0.01423 0.007114
T-1.169 0.9968-1.1720e+00 0.2548 0.1274


Multiple Linear Regression - Regression Statistics
Multiple R 0.9834
R-squared 0.9672
Adjusted R-squared 0.9524
F-TEST (value) 65.45
F-TEST (DF numerator)9
F-TEST (DF denominator)20
p-value 7.178e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 72.83
Sum Squared Residuals 1.061e+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 1003-47.86
2 960 901.5 58.54
3 1156 1178-21.98
4 1433 1420 13.14
5 769 859.9-90.91
6 1310 1369-59.34
7 840 828 11.98
8 1590 1496 93.79
9 716 691.6 24.43
10 892 967.2-75.16
11 1717 1650 67.1
12 930 881.5 48.45
13 920 1037-116.7
14 838 785.1 52.86
15 1310 1217 92.92
16 1275 1282-6.996
17 1780 1785-4.517
18 1148 1126 22.21
19 900 923.4-23.36
20 839 820.1 18.88
21 1975 1990-15.06
22 865 908.4-43.36
23 1018 945.9 72.05
24 1550 1518 31.5
25 1190 1227-36.69
26 1182 1209-26.63
27 783 682.8 100.2
28 920 1052-131.8
29 788 763.5 24.51
30 1193 1225-32.2


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
13 0.7982 0.4036 0.2018
14 0.6754 0.6491 0.3246
15 0.546 0.9081 0.454
16 0.4613 0.9225 0.5387
17 0.3492 0.6983 0.6508


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 = 9.0364, df1 = 2, df2 = 18, p-value = 0.001918
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.241, df1 = 18, df2 = 2, p-value = 0.5378
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.64159, df1 = 2, df2 = 18, p-value = 0.5381


Variance Inflation Factors (Multicollinearity)
> vif
        V        SQ        BR        PH        AG        Re        As         C 
 1.446600 13.933951  2.282755  1.350788  6.044902  2.331050 10.417095  2.156168 
        T 
 1.914313