Multiple Linear Regression - Estimated Regression Equation
TVDC3[t] = -2.43598 + 0.188296SKEOUSUM[t] + 0.11866GWSUM[t] -0.106827KVDD4[t] + 0.0188012EP4[t] + 0.211656EC3[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)-2.436 0.9075-2.6840e+00 0.008685 0.004343
SKEOUSUM+0.1883 0.02967+6.3470e+00 9.299e-09 4.65e-09
GWSUM+0.1187 0.03514+3.3770e+00 0.001094 0.0005468
KVDD4-0.1068 0.05872-1.8190e+00 0.07228 0.03614
EP4+0.0188 0.04096+4.5900e-01 0.6474 0.3237
EC3+0.2117 0.08435+2.5090e+00 0.01393 0.006963


Multiple Linear Regression - Regression Statistics
Multiple R 0.6461
R-squared 0.4175
Adjusted R-squared 0.3844
F-TEST (value) 12.61
F-TEST (DF numerator)5
F-TEST (DF denominator)88
p-value 3.05e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 0.5129
Sum Squared Residuals 23.15


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 3 3.45-0.4499
2 4 3.876 0.124
3 5 4.535 0.4646
4 5 4.508 0.4917
5 5 3.699 1.301
6 5 4.81 0.1899
7 5 4.182 0.8178
8 4 4.404-0.4035
9 4 4.301-0.3011
10 4 4.244-0.2438
11 4 4.176-0.1756
12 5 4.015 0.9852
13 4 4.01-0.009925
14 4 4.095-0.09455
15 4 4.234-0.2337
16 4 4.389-0.3893
17 4 3.983 0.01724
18 5 4.796 0.2039
19 3 3.945-0.9452
20 5 4.131 0.8693
21 4 4.84-0.8404
22 4 3.913 0.08688
23 5 4.025 0.9751
24 4 4.213-0.2132
25 4 3.799 0.201
26 5 4.471 0.5293
27 5 4.698 0.3017
28 3 3.557-0.5567
29 5 5.318-0.3175
30 4 4.145-0.1453
31 3 3.896-0.8961
32 5 4.778 0.2223
33 3 3.51-0.5099
34 4 3.789 0.2107
35 4 4.369-0.3687
36 4 4.064-0.06381
37 5 4.973 0.02705
38 4 4.171-0.1706
39 4 4.652-0.6521
40 4 3.599 0.4011
41 5 3.583 1.417
42 4 4.415-0.4153
43 4 4.027-0.02722
44 4 3.799 0.201
45 5 4.345 0.6549
46 4 4.108-0.1078
47 4 4.264-0.2636
48 5 4.264 0.7364
49 5 4.385 0.6153
50 4 4.359-0.3589
51 5 4.39 0.6104
52 5 4.352 0.6475
53 5 4.722 0.2783
54 2 3.092-1.092
55 5 4.466 0.5338
56 5 4.452 0.5481
57 4 4.332-0.3319
58 4 4.271-0.2713
59 4 4.008-0.007927
60 4 4.829-0.8285
61 5 4.827 0.1729
62 4 4.628-0.6284
63 4 4.238-0.2383
64 3 3.583-0.5828
65 4 3.646 0.3544
66 4 4.275-0.2755
67 4 4.566-0.5658
68 4 4.099-0.09901
69 4 3.976 0.02411
70 4 3.776 0.2243
71 4 4.176-0.176
72 4 4.206-0.2058
73 4 4.106-0.106
74 5 3.941 1.059
75 3 2.98 0.01999
76 4 4.085-0.08502
77 3 3.839-0.8386
78 5 5.048-0.04805
79 5 5.136-0.1359
80 5 4.841 0.1592
81 4 4.026-0.02632
82 4 4.404-0.4035
83 4 4.027-0.02662
84 5 4.383 0.6173
85 4 4.454-0.4543
86 5 4.564 0.436
87 4 4.52-0.5202
88 4 3.906 0.09374
89 4 4.127-0.1266
90 3 3.566-0.5658
91 5 4.722 0.2783
92 4 3.919 0.08051
93 4 4.257-0.2571
94 4 4.132-0.1317


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
9 0.3699 0.7397 0.6301
10 0.2148 0.4296 0.7852
11 0.2963 0.5925 0.7037
12 0.758 0.484 0.242
13 0.7036 0.5928 0.2964
14 0.6149 0.7702 0.3851
15 0.5186 0.9627 0.4814
16 0.6974 0.6051 0.3026
17 0.6281 0.7438 0.3719
18 0.553 0.8941 0.447
19 0.7438 0.5123 0.2562
20 0.8313 0.3375 0.1687
21 0.9048 0.1904 0.0952
22 0.877 0.2461 0.123
23 0.9398 0.1203 0.06016
24 0.919 0.162 0.081
25 0.8904 0.2193 0.1096
26 0.8814 0.2371 0.1186
27 0.8607 0.2786 0.1393
28 0.8913 0.2173 0.1087
29 0.8639 0.2721 0.1361
30 0.8254 0.3493 0.1746
31 0.9124 0.1751 0.08755
32 0.8878 0.2243 0.1122
33 0.896 0.208 0.104
34 0.8701 0.2599 0.1299
35 0.8566 0.2868 0.1434
36 0.8172 0.3657 0.1828
37 0.7735 0.453 0.2265
38 0.7264 0.5472 0.2736
39 0.7557 0.4885 0.2443
40 0.7334 0.5332 0.2666
41 0.9394 0.1211 0.06056
42 0.9347 0.1306 0.06528
43 0.9129 0.1742 0.08711
44 0.8916 0.2168 0.1084
45 0.9114 0.1771 0.08857
46 0.8844 0.2312 0.1156
47 0.8588 0.2824 0.1412
48 0.9 0.1999 0.09997
49 0.9186 0.1628 0.08141
50 0.9028 0.1944 0.09721
51 0.9189 0.1622 0.08109
52 0.9352 0.1297 0.06485
53 0.9222 0.1557 0.07783
54 0.9766 0.04681 0.02341
55 0.9825 0.03505 0.01753
56 0.9902 0.01964 0.009818
57 0.9873 0.02547 0.01274
58 0.985 0.03007 0.01503
59 0.9777 0.04467 0.02233
60 0.983 0.03403 0.01701
61 0.9763 0.04735 0.02368
62 0.973 0.05393 0.02697
63 0.9639 0.07212 0.03606
64 0.9758 0.04848 0.02424
65 0.9711 0.0578 0.0289
66 0.9614 0.0772 0.0386
67 0.9708 0.05845 0.02923
68 0.9622 0.07557 0.03779
69 0.9475 0.105 0.05249
70 0.9258 0.1485 0.07425
71 0.896 0.2081 0.104
72 0.887 0.2261 0.113
73 0.8621 0.2759 0.1379
74 0.9825 0.03505 0.01753
75 0.9784 0.0432 0.0216
76 0.9825 0.03507 0.01754
77 0.9841 0.03175 0.01588
78 0.9734 0.05325 0.02662
79 0.9547 0.0906 0.0453
80 0.9191 0.1618 0.08088
81 0.8656 0.2687 0.1344
82 0.7853 0.4294 0.2147
83 0.6694 0.6612 0.3306
84 0.7959 0.4083 0.2041
85 0.6454 0.7092 0.3546


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level130.168831NOK
10% type I error level210.272727NOK


Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.3958, df1 = 2, df2 = 86, p-value = 0.2532
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.90558, df1 = 10, df2 = 78, p-value = 0.5323
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.15397, df1 = 2, df2 = 86, p-value = 0.8575


Variance Inflation Factors (Multicollinearity)
> vif
SKEOUSUM    GWSUM    KVDD4      EP4      EC3 
1.021859 1.019252 1.021890 1.009794 1.014053