Multiple Linear Regression - Estimated Regression Equation
a[t] = -44.9882 + 1.7506b[t] + 0.367952c[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)-44.99 6.553-6.8660e+00 2.241e-07 1.12e-07
b+1.751 0.08576+2.0410e+01 6.058e-18 3.029e-18
c+0.3679 0.128+2.8730e+00 0.007818 0.003909


Multiple Linear Regression - Regression Statistics
Multiple R 0.9711
R-squared 0.943
Adjusted R-squared 0.9388
F-TEST (value) 223.5
F-TEST (DF numerator)2
F-TEST (DF denominator)27
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 3.097
Sum Squared Residuals 258.9


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 94.1 95.54-1.436
2 101.9 99.16 2.741
3 88.65 88.66-0.006204
4 115.5 106.8 8.658
5 87.5 88.53-1.032
6 72 71.22 0.7821
7 91.5 88.06 3.437
8 113.9 109.7 4.236
9 69.34 63.66 5.684
10 96.9 100.2-3.291
11 96 96.08-0.07745
12 61.9 61.4 0.5029
13 93 90.83 2.173
14 109.5 109 0.4671
15 93.75 95.43-1.683
16 106.7 107.1-0.4079
17 81.5 84.54-3.04
18 94.5 94.08 0.416
19 69 71.41-2.41
20 96.9 95.71 1.191
21 86.5 87.57-1.072
22 97.9 95.8 2.101
23 83 89.15-6.146
24 97.3 99.21-1.913
25 100.8 104.2-3.364
26 97.9 100.1-2.186
27 90.5 90.58-0.08219
28 97 96.5 0.5034
29 92 94.8-2.8
30 95.9 97.85-1.947


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
6 0.582 0.836 0.418
7 0.8032 0.3937 0.1968
8 0.7906 0.4189 0.2094
9 0.9636 0.07286 0.03643
10 0.9862 0.02751 0.01375
11 0.9758 0.04839 0.02419
12 0.9653 0.06936 0.03468
13 0.9663 0.06732 0.03366
14 0.9436 0.1128 0.05639
15 0.9186 0.1629 0.08144
16 0.8693 0.2614 0.1307
17 0.8572 0.2857 0.1428
18 0.7987 0.4026 0.2013
19 0.734 0.532 0.266
20 0.7138 0.5724 0.2862
21 0.6008 0.7983 0.3992
22 0.6782 0.6435 0.3218
23 0.9658 0.06841 0.03421
24 0.9202 0.1596 0.0798


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level20.105263NOK
10% type I error level60.315789NOK


Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 2.7863, df1 = 2, df2 = 25, p-value = 0.08083
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.9404, df1 = 4, df2 = 23, p-value = 0.1377
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 3.0801, df1 = 2, df2 = 25, p-value = 0.06371


Variance Inflation Factors (Multicollinearity)
> vif
       b        c 
1.016201 1.016201