Multiple Linear Regression - Estimated Regression Equation
DPSF[t] = + 0.949905 + 0.140787V[t] -0.000148272SQ[t] -4.32454e-09SQ2[t] + 0.0415811BR[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)+0.9499 0.1842+5.1580e+00 2.218e-05 1.109e-05
V+0.1408 0.06824+2.0630e+00 0.04921 0.02461
SQ-0.0001483 0.0001702-8.7110e-01 0.3917 0.1958
SQ2-4.325e-09 3.821e-08-1.1320e-01 0.9108 0.4554
BR+0.04158 0.05523+7.5290e-01 0.4582 0.2291


Multiple Linear Regression - Regression Statistics
Multiple R 0.6751
R-squared 0.4557
Adjusted R-squared 0.372
F-TEST (value) 5.443
F-TEST (DF numerator)4
F-TEST (DF denominator)26
p-value 0.002546
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 0.1348
Sum Squared Residuals 0.4727


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 1.067 1.038 0.02935
2 0.7565 0.8661-0.1096
3 0.9739 0.9918-0.01788
4 0.7129 0.7732-0.06029
5 0.7234 0.9128-0.1894
6 0.95 0.8485 0.1014
7 0.8687 1.026-0.1578
8 1.221 1.015 0.2061
9 0.6898 0.9167-0.2269
10 0.8867 0.9915-0.1048
11 1.063 0.9231 0.14
12 0.9738 0.9995-0.02568
13 0.7474 0.9419-0.1945
14 0.9301 0.9241 0.005958
15 1.125 1.037 0.08844
16 1.006 0.9085 0.09702
17 0.9319 0.832 0.09995
18 1.129 0.9475 0.1813
19 1.065 0.9328 0.1322
20 1.033 0.9521 0.0812
21 0.5024 0.4947 0.007715
22 0.8641 0.9923-0.1282
23 1.141 1.038 0.1031
24 0.6504 0.7249-0.07447
25 0.8636 0.9613-0.09775
26 0.7828 0.8275-0.04469
27 1.175 0.8914 0.2831
28 0.9147 0.903 0.01174
29 1.014 1.036-0.02148
30 0.9336 1.046-0.112
31 1.036 1.039-0.003296


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
8 0.6084 0.7833 0.3916
9 0.6002 0.7996 0.3998
10 0.4637 0.9274 0.5363
11 0.3717 0.7433 0.6283
12 0.2854 0.5707 0.7146
13 0.5622 0.8757 0.4378
14 0.6701 0.6598 0.3299
15 0.6035 0.793 0.3965
16 0.5525 0.8949 0.4475
17 0.5209 0.9583 0.4791
18 0.6886 0.6229 0.3114
19 0.6862 0.6276 0.3138
20 0.5779 0.8442 0.4221
21 0.4375 0.8751 0.5625
22 0.4395 0.8791 0.5605
23 0.4147 0.8293 0.5853


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 = 0.30979, df1 = 2, df2 = 24, p-value = 0.7365
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.6392, df1 = 8, df2 = 18, p-value = 0.7354
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.72839, df1 = 2, df2 = 24, p-value = 0.4931


Variance Inflation Factors (Multicollinearity)
> vif
        V        SQ       SQ2        BR 
 1.062316 18.103205 17.978076  1.601784