Multiple Linear Regression - Estimated Regression Equation
TVDCSUM[t] = + 2.78913 + 0.687324`SK/EOU1`[t] + 0.893553`SK/EOU2`[t] + 0.604347`SK/EOU4`[t] + 0.0329226IKSUM[t] + 0.512012GW1[t] + 0.560009GW2[t] + 0.0616104ECSUM[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)+2.789 2.026+1.3770e+00 0.1725 0.08623
`SK/EOU1`+0.6873 0.2003+3.4310e+00 0.0009535 0.0004768
`SK/EOU2`+0.8935 0.242+3.6920e+00 0.0004046 0.0002023
`SK/EOU4`+0.6044 0.2786+2.1700e+00 0.03301 0.01651
IKSUM+0.03292 0.08156+4.0370e-01 0.6875 0.3438
GW1+0.512 0.2376+2.1550e+00 0.03415 0.01707
GW2+0.56 0.1574+3.5570e+00 0.0006329 0.0003165
ECSUM+0.06161 0.05167+1.1920e+00 0.2367 0.1183


Multiple Linear Regression - Regression Statistics
Multiple R 0.704
R-squared 0.4956
Adjusted R-squared 0.4515
F-TEST (value) 11.23
F-TEST (DF numerator)7
F-TEST (DF denominator)80
p-value 8.054e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.332
Sum Squared Residuals 142


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 13 13.48-0.4826
2 16 14.26 1.741
3 17 15.57 1.434
4 16 15.22 0.7803
5 17 16.71 0.2898
6 17 15.37 1.627
7 15 15.62-0.6214
8 16 15.71 0.2935
9 14 14.82-0.8245
10 16 15.69 0.315
11 17 15.33 1.671
12 16 14.55 1.446
13 16 16.31-0.3109
14 16 14.62 1.385
15 15 15.47-0.4672
16 16 15.85 0.1479
17 13 15.06-2.057
18 15 15.74-0.7434
19 17 17.03-0.0311
20 13 13.95-0.9524
21 17 16.59 0.413
22 14 14.72-0.7184
23 14 14.17-0.1698
24 18 15.95 2.049
25 17 16.91 0.09427
26 13 13.79-0.7918
27 16 17.02-1.022
28 15 16.39-1.394
29 13 15.75-2.747
30 17 17.75-0.7493
31 11 12.95-1.95
32 13 14.23-1.228
33 17 16.18 0.8207
34 16 15.97 0.03216
35 17 17.56-0.5644
36 16 15.13 0.8734
37 16 16.7-0.6996
38 16 15.19 0.8133
39 17 14.8 2.203
40 14 15.89-1.891
41 14 15.52-1.522
42 16 14.93 1.074
43 15 14.97 0.03101
44 16 15.6 0.4011
45 14 13.81 0.1911
46 15 14.3 0.7026
47 17 15.59 1.413
48 17 15.84 1.158
49 20 17.06 2.939
50 17 16.55 0.4465
51 18 16.51 1.486
52 14 13.15 0.849
53 17 16.14 0.8646
54 17 17.34-0.3412
55 16 15.96 0.04096
56 18 15.63 2.368
57 18 19.46-1.456
58 16 16.9-0.9015
59 13 15.56-2.562
60 16 16.29-0.2915
61 12 12.83-0.8316
62 16 14.38 1.624
63 16 16.25-0.2535
64 16 15.77 0.2272
65 14 16.75-2.748
66 15 14.68 0.3232
67 14 14.82-0.8193
68 15 15.41-0.4141
69 15 16.07-1.074
70 16 16.08-0.07599
71 11 11.35-0.3516
72 18 16.34 1.658
73 11 13.98-2.977
74 18 17.85 0.1497
75 17 16.84 0.1559
76 14 15.24-1.239
77 17 16.31 0.6934
78 14 15.41-1.41
79 19 16.92 2.079
80 16 17.46-1.455
81 16 15.13 0.8686
82 15 15.93-0.9314
83 12 14.37-2.369
84 17 16.98 0.0169
85 15 14.13 0.8745
86 18 16.06 1.936
87 16 16.4-0.4012
88 16 14.16 1.838


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
11 0.02055 0.0411 0.9795
12 0.01012 0.02025 0.9899
13 0.003408 0.006815 0.9966
14 0.0009154 0.001831 0.9991
15 0.02102 0.04204 0.979
16 0.008694 0.01739 0.9913
17 0.02951 0.05902 0.9705
18 0.2283 0.4566 0.7717
19 0.1789 0.3578 0.8211
20 0.2233 0.4466 0.7767
21 0.1705 0.3409 0.8295
22 0.1354 0.2709 0.8646
23 0.09326 0.1865 0.9067
24 0.1858 0.3716 0.8142
25 0.1383 0.2765 0.8617
26 0.1103 0.2206 0.8897
27 0.1146 0.2292 0.8854
28 0.1036 0.2073 0.8964
29 0.237 0.474 0.763
30 0.1887 0.3773 0.8113
31 0.3684 0.7368 0.6316
32 0.3642 0.7285 0.6358
33 0.3187 0.6374 0.6813
34 0.2584 0.5169 0.7416
35 0.2088 0.4176 0.7912
36 0.1766 0.3532 0.8234
37 0.1404 0.2807 0.8596
38 0.1177 0.2354 0.8823
39 0.1651 0.3301 0.8349
40 0.2155 0.431 0.7845
41 0.2388 0.4776 0.7612
42 0.227 0.4539 0.773
43 0.1839 0.3677 0.8161
44 0.1468 0.2936 0.8532
45 0.1148 0.2295 0.8852
46 0.09616 0.1923 0.9038
47 0.116 0.232 0.884
48 0.1137 0.2275 0.8863
49 0.3083 0.6166 0.6917
50 0.2785 0.5571 0.7215
51 0.2751 0.5501 0.7249
52 0.243 0.4861 0.757
53 0.2162 0.4323 0.7838
54 0.1733 0.3465 0.8267
55 0.1359 0.2718 0.8641
56 0.2245 0.4491 0.7755
57 0.2302 0.4604 0.7698
58 0.1917 0.3834 0.8083
59 0.3479 0.6958 0.6521
60 0.292 0.584 0.708
61 0.2424 0.4848 0.7576
62 0.3431 0.6861 0.6569
63 0.2786 0.5572 0.7214
64 0.2537 0.5075 0.7463
65 0.4234 0.8468 0.5766
66 0.3802 0.7605 0.6198
67 0.3137 0.6274 0.6863
68 0.243 0.4859 0.757
69 0.2113 0.4227 0.7887
70 0.2447 0.4894 0.7553
71 0.1778 0.3557 0.8222
72 0.1362 0.2723 0.8638
73 0.4835 0.9669 0.5165
74 0.3659 0.7318 0.6341
75 0.3121 0.6241 0.6879
76 0.4142 0.8285 0.5858
77 0.2713 0.5425 0.7287


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level2 0.02985NOK
5% type I error level60.0895522NOK
10% type I error level70.104478NOK


Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.651, df1 = 2, df2 = 78, p-value = 0.1985
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.1562, df1 = 14, df2 = 66, p-value = 0.3292
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.5993, df1 = 2, df2 = 78, p-value = 0.2086


Variance Inflation Factors (Multicollinearity)
> vif
`SK/EOU1` `SK/EOU2` `SK/EOU4`     IKSUM       GW1       GW2     ECSUM 
 1.138869  1.230208  1.092730  1.050695  1.083880  1.067510  1.023205