Multiple Linear Regression - Estimated Regression Equation
TVDC[t] = + 6.02325 + 0.649713SK1[t] + 1.20144SK2[t] -0.0140394SK3[t] + 0.462548SK4[t] + 0.161402SK5[t] -0.022023SK6[t] -0.137479G[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)+6.023 2.149+2.8030e+00 0.006156 0.003078
SK1+0.6497 0.2152+3.0190e+00 0.003265 0.001633
SK2+1.201 0.2391+5.0250e+00 2.392e-06 1.196e-06
SK3-0.01404 0.2044-6.8670e-02 0.9454 0.4727
SK4+0.4626 0.2886+1.6030e+00 0.1124 0.05619
SK5+0.1614 0.2425+6.6550e-01 0.5074 0.2537
SK6-0.02202 0.2629-8.3780e-02 0.9334 0.4667
G-0.1375 0.3141-4.3780e-01 0.6626 0.3313


Multiple Linear Regression - Regression Statistics
Multiple R 0.6155
R-squared 0.3788
Adjusted R-squared 0.3326
F-TEST (value) 8.19
F-TEST (DF numerator)7
F-TEST (DF denominator)94
p-value 9.169e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.532
Sum Squared Residuals 220.7


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
1 13 13.08-0.07539
2 16 15.27 0.7343
3 17 15.79 1.211
4 16 15.13 0.8684
5 17 17.11-0.1062
6 17 15.97 1.031
7 15 15.46-0.4642
8 16 15.29 0.7052
9 14 13.89 0.1078
10 16 15.42 0.5794
11 17 14.96 2.044
12 16 14.49 1.506
13 16 15.61 0.3941
14 16 14.81 1.193
15 15 15.78-0.7794
16 16 14.61 1.395
17 16 16.43-0.4291
18 13 14.56-1.564
19 15 16.97-1.969
20 17 16.39 0.6069
21 13 13.29-0.2884
22 17 17.01-0.008941
23 14 13.94 0.05811
24 14 14.59-0.592
25 18 15.77 2.235
26 17 16.97 0.03128
27 13 13.79-0.7908
28 16 17.16-1.156
29 15 15.82-0.8151
30 15 15.16-0.1574
31 15 15.56-0.5557
32 13 15.8-2.803
33 17 16.83 0.1707
34 11 14.57-3.566
35 14 14.09-0.09341
36 13 15.63-2.628
37 17 15.15 1.848
38 16 15.34 0.6592
39 17 17.65-0.6465
40 16 14.48 1.518
41 16 15.78 0.2187
42 16 14.67 1.331
43 15 15.9-0.9048
44 12 13.24-1.239
45 17 15.5 1.5
46 14 15.34-1.341
47 14 15.77-1.769
48 16 14.67 1.331
49 15 15.01-0.006267
50 16 15.94 0.05918
51 14 14.65-0.655
52 15 13.89 1.108
53 17 14.43 2.574
54 10 13.94-3.942
55 17 15.93 1.067
56 20 16.29 3.708
57 17 16.75 0.2538
58 18 15.77 2.233
59 14 12.79 1.209
60 17 15.74 1.257
61 17 17.16-0.1643
62 16 15.61 0.3941
63 18 16.28 1.716
64 18 16.86 1.144
65 16 16.98-0.9828
66 15 15.74-0.7434
67 13 16.27-3.27
68 16 15.77 0.2346
69 12 13.61-1.605
70 16 14.98 1.022
71 16 15.6 0.396
72 16 16.42-0.417
73 14 15.8-1.801
74 15 15.3-0.3028
75 14 14.65-0.6451
76 15 15.8-0.8033
77 15 14.99 0.007773
78 16 15.18 0.8206
79 11 11.66-0.6642
80 18 16.13 1.872
81 11 13.93-2.928
82 18 17.79 0.208
83 15 16.83-1.829
84 19 18.24 0.7595
85 17 17.11-0.1062
86 14 15.28-1.277
87 13 15.81-2.808
88 17 15.8 1.205
89 14 15.78-1.781
90 19 15.98 3.024
91 14 14.43-0.4265
92 16 16.99-0.9888
93 16 15.32 0.6831
94 15 15.62-0.6199
95 12 14.65-2.655
96 17 16.44 0.561
97 18 15.76 2.243
98 15 14.12 0.8827
99 18 15.8 2.199
100 15 17.43-2.429
101 16 15.62 0.3801
102 16 13.9 2.098


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
11 0.06445 0.1289 0.9355
12 0.02019 0.04038 0.9798
13 0.009389 0.01878 0.9906
14 0.004536 0.009073 0.9955
15 0.007748 0.0155 0.9923
16 0.004115 0.008231 0.9959
17 0.001465 0.00293 0.9985
18 0.001613 0.003226 0.9984
19 0.02516 0.05033 0.9748
20 0.03425 0.0685 0.9658
21 0.02479 0.04959 0.9752
22 0.01585 0.0317 0.9841
23 0.01155 0.0231 0.9885
24 0.008174 0.01635 0.9918
25 0.03833 0.07666 0.9617
26 0.02447 0.04894 0.9755
27 0.02147 0.04293 0.9785
28 0.01894 0.03788 0.9811
29 0.01207 0.02414 0.9879
30 0.007696 0.01539 0.9923
31 0.00474 0.00948 0.9953
32 0.01601 0.03202 0.984
33 0.01147 0.02294 0.9885
34 0.07345 0.1469 0.9265
35 0.05384 0.1077 0.9462
36 0.08348 0.167 0.9165
37 0.09137 0.1827 0.9086
38 0.07602 0.152 0.924
39 0.05637 0.1127 0.9436
40 0.05169 0.1034 0.9483
41 0.03824 0.07649 0.9618
42 0.03692 0.07385 0.9631
43 0.02961 0.05923 0.9704
44 0.02724 0.05448 0.9728
45 0.02545 0.0509 0.9746
46 0.02437 0.04875 0.9756
47 0.02709 0.05418 0.9729
48 0.02597 0.05194 0.974
49 0.02146 0.04292 0.9785
50 0.01462 0.02923 0.9854
51 0.01105 0.02211 0.9889
52 0.008848 0.0177 0.9912
53 0.02908 0.05816 0.9709
54 0.1932 0.3864 0.8068
55 0.175 0.3501 0.825
56 0.4311 0.8621 0.5689
57 0.3742 0.7485 0.6258
58 0.4402 0.8804 0.5598
59 0.407 0.814 0.593
60 0.3898 0.7797 0.6102
61 0.332 0.6641 0.668
62 0.2869 0.5739 0.7131
63 0.307 0.6141 0.693
64 0.2791 0.5582 0.7209
65 0.2403 0.4806 0.7597
66 0.1997 0.3993 0.8003
67 0.369 0.738 0.631
68 0.3096 0.6191 0.6904
69 0.3098 0.6195 0.6902
70 0.334 0.6679 0.666
71 0.2928 0.5857 0.7072
72 0.2656 0.5312 0.7344
73 0.281 0.562 0.719
74 0.2259 0.4518 0.7741
75 0.1836 0.3671 0.8164
76 0.1554 0.3109 0.8446
77 0.1458 0.2915 0.8542
78 0.1451 0.2902 0.8549
79 0.1097 0.2194 0.8903
80 0.09164 0.1833 0.9084
81 0.2464 0.4928 0.7536
82 0.186 0.3721 0.814
83 0.1598 0.3196 0.8402
84 0.1144 0.2288 0.8856
85 0.07585 0.1517 0.9242
86 0.07106 0.1421 0.9289
87 0.4654 0.9309 0.5346
88 0.3566 0.7132 0.6434
89 0.485 0.9699 0.515
90 0.7895 0.4211 0.2105
91 0.6334 0.7332 0.3666


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level5 0.06173NOK
5% type I error level240.296296NOK
10% type I error level350.432099NOK


Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.74779, df1 = 2, df2 = 92, p-value = 0.4763
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.97753, df1 = 14, df2 = 80, p-value = 0.4835
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.1855, df1 = 2, df2 = 92, p-value = 0.3102


Variance Inflation Factors (Multicollinearity)
> vif
     SK1      SK2      SK3      SK4      SK5      SK6        G 
1.076742 1.173460 1.060510 1.076882 1.041520 1.074715 1.067413