Multiple Linear Regression - Estimated Regression Equation
DPSF[t] = + 0.791453 + 0.0936654V[t] + 0.000129461SQ[t] -3.72685e-08SQ2[t] + 0.0349066BR[t] -0.0441961PH[t] + 0.031338GR[t] -0.00971863AG[t] + 0.0964604C[t] -0.121352TH[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)+0.7914 0.1091+7.2540e+00 3.809e-07 1.904e-07
V+0.09366 0.04263+2.1970e+00 0.03935 0.01967
SQ+0.0001295 0.000124+1.0440e+00 0.3084 0.1542
SQ2-3.727e-08 2.401e-08-1.5520e+00 0.1356 0.06778
BR+0.03491 0.03447+1.0130e+00 0.3227 0.1613
PH-0.0442 0.0426-1.0370e+00 0.3113 0.1557
GR+0.03134 0.04759+6.5840e-01 0.5174 0.2587
AG-0.009719 0.00249-3.9030e+00 0.0008189 0.0004095
C+0.09646 0.03919+2.4610e+00 0.02259 0.01129
TH-0.1213 0.06559-1.8500e+00 0.07842 0.03921


Multiple Linear Regression - Regression Statistics
Multiple R 0.931
R-squared 0.8667
Adjusted R-squared 0.8096
F-TEST (value) 15.18
F-TEST (DF numerator)9
F-TEST (DF denominator)21
p-value 2.582e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 0.07424
Sum Squared Residuals 0.1157


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 1.067 1.128-0.06065
2 0.7565 0.7532 0.003297
3 0.9739 1.016-0.04261
4 0.7129 0.6628 0.05016
5 0.7234 0.7419-0.01848
6 0.95 0.9869-0.03697
7 0.8687 0.865 0.003652
8 1.221 1.172 0.04896
9 0.6898 0.7406-0.05084
10 0.8867 0.9211-0.0344
11 1.063 0.9203 0.1429
12 0.9738 0.9683 0.00553
13 0.7474 0.8466-0.09919
14 0.9301 0.9491-0.01906
15 1.125 1.007 0.1185
16 1.006 1.012-0.006088
17 0.9319 1.035-0.1026
18 1.129 1.095 0.03405
19 1.065 1.02 0.04472
20 1.033 1.028 0.00575
21 0.5024 0.5088-0.006371
22 0.8641 0.8895-0.02534
23 1.141 1.098 0.04291
24 0.6504 0.6183 0.03214
25 0.8636 0.892-0.02845
26 0.7828 0.8587-0.07593
27 1.175 1.06 0.1141
28 0.9147 0.8476 0.06713
29 1.014 1.128-0.1141
30 0.9336 0.9113 0.02234
31 1.036 1.051-0.01505


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
13 0.4517 0.9034 0.5483
14 0.2975 0.5949 0.7025
15 0.4767 0.9535 0.5233
16 0.3176 0.6352 0.6824
17 0.5759 0.8481 0.4241
18 0.5264 0.9472 0.4736


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 = 0.64126, df1 = 2, df2 = 19, p-value = 0.5376
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.15411, df1 = 18, df2 = 3, p-value = 0.9964
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.17358, df1 = 2, df2 = 19, p-value = 0.842


Variance Inflation Factors (Multicollinearity)
> vif
        V        SQ       SQ2        BR        PH        GR        AG         C 
 1.367309 31.692274 23.416246  2.057855  1.380795  1.431832  2.739743  2.157496 
       TH 
 2.719387