Multiple Linear Regression - Estimated Regression Equation
a[t] = + 51.5827 + 0.0757179b[t] -3.46203c[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)+51.58 8.825+5.8450e+00 2.778e-06 1.389e-06
b+0.07572 0.05531+1.3690e+00 0.1819 0.09095
c-3.462 0.957-3.6180e+00 0.00116 0.0005798


Multiple Linear Regression - Regression Statistics
Multiple R 0.5699
R-squared 0.3248
Adjusted R-squared 0.2766
F-TEST (value) 6.734
F-TEST (DF numerator)2
F-TEST (DF denominator)28
p-value 0.004094
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 2.159
Sum Squared Residuals 130.5


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 22.66 20.09 2.57
2 18.95 19.46-0.5094
3 21.11 22.99-1.888
4 20.73 18.24 2.488
5 18.75 19.39-0.6362
6 13.68 17.14-3.462
7 20.83 20.76 0.06984
8 22.06 21.09 0.9679
9 20.05 19.31 0.745
10 22.66 20.17 2.485
11 24.28 22.17 2.107
12 17.07 20.56-3.489
13 21.11 21.52-0.4148
14 24.11 21.63 2.482
15 19.78 20.46-0.685
16 16.52 21.11-4.585
17 18.3 18.96-0.6568
18 22.35 22.14 0.2023
19 21.02 21.7-0.6856
20 19.48 18.64 0.8339
21 16.52 16.99-0.4744
22 16.16 19.34-3.179
23 24.02 21.45 2.571
24 21.59 19.15 2.438
25 20.05 20.36-0.3048
26 18.3 21.18-2.884
27 22.18 20.39 1.781
28 22.23 19.89 2.341
29 21.59 21.98-0.3906
30 19.92 21.79-1.872
31 22.4 20.37 2.035


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
6 0.4969 0.9938 0.5031
7 0.6202 0.7595 0.3798
8 0.6201 0.7597 0.3799
9 0.5217 0.9567 0.4783
10 0.4998 0.9996 0.5002
11 0.4347 0.8694 0.5653
12 0.6439 0.7122 0.3561
13 0.5274 0.9452 0.4726
14 0.6092 0.7817 0.3908
15 0.4988 0.9976 0.5012
16 0.7047 0.5906 0.2953
17 0.6207 0.7586 0.3793
18 0.5231 0.9539 0.4769
19 0.404 0.808 0.596
20 0.3324 0.6647 0.6676
21 0.2598 0.5196 0.7402
22 0.5506 0.8987 0.4494
23 0.9152 0.1697 0.08485
24 0.8474 0.3051 0.1526
25 0.721 0.5581 0.279


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 = 0.89829, df1 = 2, df2 = 26, p-value = 0.4195
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.21554, df1 = 4, df2 = 24, p-value = 0.9272
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.29568, df1 = 2, df2 = 26, p-value = 0.7465


Variance Inflation Factors (Multicollinearity)
> vif
       b        c 
1.046821 1.046821