Multiple Linear Regression - Estimated Regression Equation
YY[t] = + 67.7757 + 7.70333X1[t] -11.1058X2[t] + 6.25483X3[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)+67.78 27.09+2.5020e+00 0.04088 0.02044
X1+7.703 2.082+3.7010e+00 0.007649 0.003824
X2-11.11 4.036-2.7520e+00 0.02844 0.01422
X3+6.255 1.997+3.1320e+00 0.01657 0.008287


Multiple Linear Regression - Regression Statistics
Multiple R 0.8294
R-squared 0.688
Adjusted R-squared 0.5542
F-TEST (value) 5.144
F-TEST (DF numerator)3
F-TEST (DF denominator)7
p-value 0.03436
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 19.19
Sum Squared Residuals 2578


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 94 93.45 0.5527
2 65 41.48 23.52
3 16 35.71-19.71
4 75 96.3-21.3
5 76 63.58 12.42
6 64 46.77 17.23
7 0 19.27-19.27
8 90 74.64 15.36
9 51 57.78-6.783
10 50 44.86 5.143
11 60 67.18-7.181


Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 2.1554, df1 = 2, df2 = 5, p-value = 0.2113
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.1201, df1 = 6, df2 = 1, p-value = 0.6188
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.088001, df1 = 2, df2 = 5, p-value = 0.9171


Variance Inflation Factors (Multicollinearity)
> vif
      X1       X2       X3 
1.846086 1.841739 1.587416