Multiple Linear Regression - Estimated Regression Equation
TVDCSUM[t] = + 2.77125 + 0.792291`SK/EOU1`[t] + 0.817175`SK/EOU2`[t] + 0.809125`SK/EOU4`[t] + 0.208463EC2[t] + 0.447436GW1[t] + 0.583635GW2[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)+2.771 1.471+1.8840e+00 0.06303 0.03151
`SK/EOU1`+0.7923 0.1928+4.1080e+00 9.217e-05 4.608e-05
`SK/EOU2`+0.8172 0.2313+3.5330e+00 0.0006698 0.0003349
`SK/EOU4`+0.8091 0.265+3.0530e+00 0.003032 0.001516
EC2+0.2085 0.1088+1.9170e+00 0.0587 0.02935
GW1+0.4474 0.221+2.0250e+00 0.04608 0.02304
GW2+0.5836 0.1506+3.8750e+00 0.0002105 0.0001053


Multiple Linear Regression - Regression Statistics
Multiple R 0.7208
R-squared 0.5196
Adjusted R-squared 0.4852
F-TEST (value) 15.14
F-TEST (DF numerator)6
F-TEST (DF denominator)84
p-value 1.135e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.308
Sum Squared Residuals 143.7


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 13.27-0.2733
2 16 14.45 1.548
3 17 15.72 1.283
4 16 15.13 0.867
5 17 16.33 0.6745
6 17 15.51 1.492
7 15 15.56-0.5636
8 16 15.91 0.09178
9 14 15.11-1.108
10 16 15.51 0.4917
11 17 15.16 1.837
12 16 14.32 1.676
13 16 16.51-0.5089
14 16 14.53 1.468
15 15 15.51-0.5083
16 16 15.75 0.2531
17 13 14.93-1.93
18 15 15.74-0.7419
19 17 16.68 0.3242
20 13 13.9-0.8987
21 17 16.53 0.466
22 14 14.47-0.4657
23 14 14.32-0.316
24 18 15.75 2.253
25 17 16.77 0.2271
26 13 13.9-0.8989
27 16 16.76-0.7561
28 15 16.49-1.492
29 15 16.38-1.381
30 13 15.93-2.925
31 17 17.77-0.7735
32 11 13.08-2.076
33 13 14.13-1.133
34 17 16.51 0.4913
35 16 15.94 0.06127
36 17 17.57-0.5652
37 16 15.09 0.909
38 16 16.54-0.5394
39 16 15.34 0.6586
40 17 14.91 2.092
41 14 15.7-1.7
42 14 15.28-1.283
43 16 14.87 1.134
44 15 14.92 0.07551
45 16 15.51 0.4917
46 14 13.32 0.6767
47 15 14.52 0.4758
48 17 15.76 1.236
49 17 15.88 1.117
50 20 17.47 2.532
51 17 16.81 0.1932
52 18 16.58 1.419
53 14 13.14 0.8629
54 17 16.37 0.6273
55 17 17.4-0.3983
56 16 15.75 0.2527
57 18 15.93 2.075
58 18 19.41-1.411
59 16 16.77-0.7729
60 13 15.72-2.717
61 16 16.37-0.3727
62 12 13.06-1.059
63 16 14.55 1.451
64 16 16.3-0.3004
65 16 16 0.0023
66 14 16.88-2.884
67 15 14.53 0.4675
68 14 14.56-0.5628
69 15 15.3-0.2999
70 15 16.16-1.164
71 16 16.01-0.01099
72 11 11.3-0.2967
73 18 16.56 1.436
74 11 14.32-3.316
75 18 18.33-0.3268
76 19 18.44 0.5617
77 17 16.77 0.2271
78 14 14.96-0.955
79 17 16.51 0.4911
80 14 15.3-1.3
81 19 17.08 1.924
82 16 17.53-1.535
83 16 15.19 0.8116
84 15 16.13-1.134
85 12 14.32-2.324
86 17 16.75 0.252
87 18 16.51 1.491
88 15 13.88 1.118
89 18 16.3 1.7
90 16 16.3-0.3004
91 16 13.99 2.012


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
10 0.1974 0.3947 0.8026
11 0.2259 0.4519 0.7741
12 0.1365 0.2729 0.8635
13 0.07034 0.1407 0.9297
14 0.03793 0.07587 0.9621
15 0.06892 0.1378 0.9311
16 0.04164 0.08329 0.9584
17 0.06216 0.1243 0.9378
18 0.2086 0.4172 0.7914
19 0.1579 0.3158 0.8421
20 0.2088 0.4177 0.7912
21 0.155 0.3101 0.845
22 0.1222 0.2443 0.8778
23 0.08584 0.1717 0.9142
24 0.1946 0.3892 0.8054
25 0.1489 0.2979 0.8511
26 0.1367 0.2735 0.8633
27 0.1358 0.2715 0.8642
28 0.1325 0.2649 0.8675
29 0.1358 0.2716 0.8642
30 0.3253 0.6506 0.6747
31 0.2714 0.5428 0.7286
32 0.4519 0.9038 0.5481
33 0.45 0.9 0.55
34 0.3938 0.7877 0.6062
35 0.3308 0.6615 0.6692
36 0.2744 0.5487 0.7256
37 0.2409 0.4818 0.7591
38 0.1955 0.391 0.8045
39 0.173 0.346 0.827
40 0.2282 0.4564 0.7718
41 0.2693 0.5386 0.7307
42 0.2839 0.5677 0.7161
43 0.2613 0.5226 0.7387
44 0.2143 0.4287 0.7857
45 0.1754 0.3508 0.8246
46 0.1506 0.3013 0.8494
47 0.1267 0.2535 0.8733
48 0.1471 0.2943 0.8529
49 0.1389 0.2778 0.8611
50 0.2488 0.4975 0.7512
51 0.2159 0.4318 0.7841
52 0.2225 0.445 0.7775
53 0.1968 0.3937 0.8032
54 0.1625 0.325 0.8375
55 0.1275 0.255 0.8725
56 0.0992 0.1984 0.9008
57 0.1332 0.2663 0.8668
58 0.1486 0.2973 0.8514
59 0.1196 0.2391 0.8804
60 0.2916 0.5832 0.7084
61 0.2424 0.4849 0.7576
62 0.2082 0.4163 0.7918
63 0.2678 0.5355 0.7322
64 0.2132 0.4264 0.7868
65 0.2089 0.4177 0.7911
66 0.4156 0.8312 0.5844
67 0.4008 0.8016 0.5992
68 0.3399 0.6798 0.6601
69 0.2708 0.5416 0.7292
70 0.2578 0.5157 0.7422
71 0.2637 0.5273 0.7363
72 0.2116 0.4232 0.7884
73 0.1688 0.3376 0.8312
74 0.6903 0.6194 0.3097
75 0.5949 0.8101 0.4051
76 0.6445 0.7111 0.3555
77 0.7948 0.4104 0.2052
78 0.7281 0.5437 0.2719
79 0.6085 0.7829 0.3915
80 0.5096 0.9808 0.4904
81 0.6382 0.7235 0.3618


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 level20.0277778OK


Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.78749, df1 = 2, df2 = 82, p-value = 0.4584
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.6041, df1 = 12, df2 = 72, p-value = 0.1097
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.1867, df1 = 2, df2 = 82, p-value = 0.3104


Variance Inflation Factors (Multicollinearity)
> vif
`SK/EOU1` `SK/EOU2` `SK/EOU4`       EC2       GW1       GW2 
 1.139783  1.238548  1.116313  1.091735  1.040507  1.074693