Multiple Linear Regression - Estimated Regression Equation
V1[t] = -0.000580433 + 0.539555V2[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)-0.0005804 0.000616-9.4220e-01 0.3466 0.1733
V2+0.5395 0.03935+1.3710e+01 1.867e-36 9.337e-37


Multiple Linear Regression - Regression Statistics
Multiple R 0.5262
R-squared 0.2769
Adjusted R-squared 0.2754
F-TEST (value) 188
F-TEST (DF numerator)1
F-TEST (DF denominator)491
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 0.01367
Sum Squared Residuals 0.09176


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


Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 3.3861, df1 = 2, df2 = 489, p-value = 0.03464
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 3.3861, df1 = 2, df2 = 489, p-value = 0.03464
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 3.3861, df1 = 2, df2 = 489, p-value = 0.03464