Multiple Linear Regression - Estimated Regression Equation
a[t] = + 25.2267 -0.211086b[t] -0.477191c[t] + 0.0347213d[t] -0.152048t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)+25.23 6.626+3.8070e+00 0.01253 0.006267
b-0.2111 0.48-4.3980e-01 0.6784 0.3392
c-0.4772 0.479-9.9610e-01 0.3649 0.1825
d+0.03472 0.5566+6.2380e-02 0.9527 0.4763
t-0.152 0.1316-1.1550e+00 0.3003 0.1501


Multiple Linear Regression - Regression Statistics
Multiple R 0.7168
R-squared 0.5138
Adjusted R-squared 0.1248
F-TEST (value) 1.321
F-TEST (DF numerator)4
F-TEST (DF denominator)5
p-value 0.3766
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.006
Sum Squared Residuals 5.057


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 17 17.23-0.232
2 15 15.7-0.7034
3 16 15.11 0.8911
4 16 15.65 0.3549
5 16 15.72 0.2755
6 15 14.95 0.04643
7 13 14.5-1.501
8 16 15 0.9978
9 15 14.71 0.2915
10 15 15.42-0.4211


Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 2.3534, df1 = 2, df2 = 3, p-value = 0.2429
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = -Inf, df1 = 8, df2 = -3, p-value = NA
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.20858, df1 = 2, df2 = 3, p-value = 0.8226


Variance Inflation Factors (Multicollinearity)
> vif
       b        c        d        t 
1.936188 1.724591 1.715467 1.413556