Multiple Linear Regression - Estimated Regression Equation
EPS[t] = -1.51285 + 0.00055424Volume[t] + 0.116359HoogstevixperQ[t] -0.242243AvVixQ[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)-1.513 0.7352-2.0580e+00 0.08534 0.04267
Volume+0.0005542 0.0001983+2.7950e+00 0.03137 0.01569
HoogstevixperQ+0.1164 0.02559+4.5480e+00 0.0039 0.00195
AvVixQ-0.2422 0.07774-3.1160e+00 0.02068 0.01034


Multiple Linear Regression - Regression Statistics
Multiple R 0.9727
R-squared 0.9461
Adjusted R-squared 0.9191
F-TEST (value) 35.09
F-TEST (DF numerator)3
F-TEST (DF denominator)6
p-value 0.0003359
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 0.1889
Sum Squared Residuals 0.2142


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 0.18-0.05167 0.2317
2 0.37 0.5874-0.2174
3 2.36 2.218 0.1419
4 0.17 0.2549-0.0849
5 0.12 0.1616-0.04156
6 0.27 0.2811-0.01108
7 0.29 0.1312 0.1588
8 0.54 0.7773-0.2373
9 0.32 0.2714 0.04856
10 0.62 0.6087 0.01129


Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 8.9084, df1 = 2, df2 = 4, p-value = 0.03362
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = NaN, df1 = 6, df2 = 0, p-value = NA
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 12.422, df1 = 2, df2 = 4, p-value = 0.01923


Variance Inflation Factors (Multicollinearity)
> vif
        Volume HoogstevixperQ         AvVixQ 
      4.222898       8.172385       8.267912