Multiple Linear Regression - Estimated Regression Equation
DPSF[t] = + 0.943992 + 0.0549234V[t] -0.000774414SQ[t] + 7.30672e-08SQ2[t] -0.0506303PH[t] + 0.0427334GR[t] + 0.0275149Re[t] + 0.000655648As[t] + 0.096025C[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)+0.944 0.07466+1.2640e+01 1.448e-11 7.238e-12
V+0.05492 0.03879+1.4160e+00 0.1708 0.08539
SQ-0.0007744 0.0001465-5.2850e+00 2.65e-05 1.325e-05
SQ2+7.307e-08 2.326e-08+3.1410e+00 0.004749 0.002374
PH-0.05063 0.03689-1.3730e+00 0.1837 0.09186
GR+0.04273 0.04153+1.0290e+00 0.3146 0.1573
Re+0.02752 0.03291+8.3610e-01 0.4121 0.206
As+0.0006557 0.000122+5.3720e+00 2.152e-05 1.076e-05
C+0.09602 0.03124+3.0730e+00 0.005561 0.002781


Multiple Linear Regression - Regression Statistics
Multiple R 0.9451
R-squared 0.8932
Adjusted R-squared 0.8544
F-TEST (value) 23
F-TEST (DF numerator)8
F-TEST (DF denominator)22
p-value 5.507e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 0.06493
Sum Squared Residuals 0.09275


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 1.114-0.04703
2 0.7565 0.7057 0.05084
3 0.9739 0.9841-0.01025
4 0.7129 0.6881 0.02485
5 0.7234 0.7529-0.02943
6 0.95 0.9999-0.04998
7 0.8687 0.8742-0.005559
8 1.221 1.166 0.05472
9 0.6898 0.7061-0.01632
10 0.8867 0.9424-0.05575
11 1.063 0.9842 0.07896
12 0.9738 0.9366 0.03719
13 0.7474 0.832-0.08463
14 0.9301 0.95-0.01994
15 1.125 1.019 0.1069
16 1.006 1.025-0.01901
17 0.9319 1.003-0.07076
18 1.129 1.077 0.05156
19 1.065 1.062 0.003264
20 1.033 1.025 0.007972
21 0.5024 0.5118-0.009357
22 0.8641 0.8878-0.02362
23 1.141 1.067 0.07379
24 0.6504 0.5919 0.05857
25 0.8636 0.9175-0.0539
26 0.7828 0.8573-0.07451
27 1.175 1.09 0.08493
28 0.9147 0.8716 0.04311
29 1.014 1.135-0.1209
30 0.9336 0.9042 0.0295
31 1.036 1.051-0.01527


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
12 0.2882 0.5764 0.7118
13 0.353 0.7061 0.647
14 0.2378 0.4756 0.7622
15 0.4514 0.9028 0.5486
16 0.2958 0.5916 0.7042
17 0.2174 0.4348 0.7826
18 0.2981 0.5962 0.7019
19 0.1794 0.3589 0.8206


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 = 2.0356, df1 = 2, df2 = 20, p-value = 0.1568
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.38887, df1 = 16, df2 = 6, p-value = 0.9386
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.78456, df1 = 2, df2 = 20, p-value = 0.4699


Variance Inflation Factors (Multicollinearity)
> vif
        V        SQ       SQ2        PH        GR        Re        As         C 
 1.480216 57.852663 28.737278  1.353528  1.424968  1.843593 12.112496  1.792772