Multiple Linear Regression - Estimated Regression Equation
GWSUM[t] = + 12.8778 -0.0308809IKSUM[t] + 0.0149087KVDDSUM[t] -0.0283485SKEOUSUM[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)+12.88 2.786+4.6230e+00 1.413e-05 7.064e-06
IKSUM-0.03088 0.09343-3.3050e-01 0.7419 0.3709
KVDDSUM+0.01491 0.07842+1.9010e-01 0.8497 0.4248
SKEOUSUM-0.02835 0.09038-3.1370e-01 0.7546 0.3773


Multiple Linear Regression - Regression Statistics
Multiple R 0.05693
R-squared 0.003241
Adjusted R-squared-0.03368
F-TEST (value) 0.0878
F-TEST (DF numerator)3
F-TEST (DF denominator)81
p-value 0.9665
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.506
Sum Squared Residuals 183.7


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 11 11.95-0.9517
2 12 11.76 0.2362
3 12 11.81 0.194
4 12 11.9 0.09551
5 11 11.65-0.6454
6 12 11.99 0.01271
7 12 11.91 0.08538
8 15 11.91 3.09
9 13 11.89 1.106
10 12 11.93 0.07301
11 11 11.82-0.822
12 12 11.76 0.2362
13 12 11.82 0.1806
14 12 11.96 0.04212
15 14 11.84 2.162
16 12 11.94 0.0581
17 9 11.8-2.798
18 13 11.75 1.251
19 13 11.88 1.122
20 12 11.85 0.1533
21 12 11.91 0.08898
22 12 11.92 0.07594
23 12 11.9 0.1014
24 12 11.75 0.246
25 11 12.02-1.016
26 13 11.75 1.247
27 13 11.87 1.133
28 10 11.91-1.914
29 13 11.73 1.267
30 5 11.78-6.778
31 10 11.9-1.898
32 12 11.77 0.2337
33 13 11.79 1.209
34 13 11.81 1.191
35 12 12.02-0.01817
36 12 11.89 0.105
37 13 11.91 1.086
38 14 11.97 2.027
39 12 11.98 0.01737
40 12 11.76 0.2387
41 10 11.85-1.851
42 12 11.8 0.1965
43 12 11.79 0.2104
44 12 11.87 0.1312
45 14 11.9 2.101
46 10 11.89-1.885
47 12 11.7 0.2965
48 11 12.06-1.056
49 12 11.86 0.1446
50 12 11.82 0.178
51 13 11.83 1.167
52 12 11.75 0.2532
53 9 11.84-2.837
54 12 11.86 0.141
55 14 11.85 2.146
56 11 11.86-0.8575
57 12 11.99 0.01378
58 9 11.94-2.942
59 13 11.76 1.241
60 10 11.72-1.72
61 14 11.88 2.121
62 10 11.87-1.866
63 12 11.85 0.1522
64 11 11.84-0.8358
65 14 11.89 2.106
66 13 11.94 1.061
67 12 11.93 0.07154
68 10 11.96-1.963
69 12 11.91 0.08645
70 12 12.03-0.03054
71 15 11.9 3.095
72 12 11.71 0.2914
73 12 11.84 0.1566
74 12 11.79 0.2089
75 12 11.88 0.1163
76 11 11.74-0.7351
77 13 11.85 1.146
78 13 11.78 1.225
79 10 11.88-1.884
80 9 11.78-2.776
81 12 11.72 0.2805
82 10 11.94-1.942
83 13 11.89 1.106
84 12 11.93 0.0726
85 12 11.86 0.1362


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
7 0.05528 0.1106 0.9447
8 0.42 0.8399 0.58
9 0.2998 0.5996 0.7002
10 0.1973 0.3946 0.8027
11 0.1515 0.303 0.8485
12 0.1017 0.2033 0.8983
13 0.0596 0.1192 0.9404
14 0.03443 0.06887 0.9656
15 0.0946 0.1892 0.9054
16 0.0649 0.1298 0.9351
17 0.2483 0.4966 0.7517
18 0.2371 0.4742 0.7629
19 0.1858 0.3715 0.8142
20 0.1427 0.2854 0.8573
21 0.1015 0.2031 0.8985
22 0.06975 0.1395 0.9303
23 0.0464 0.0928 0.9536
24 0.03349 0.06698 0.9665
25 0.02771 0.05541 0.9723
26 0.02656 0.05311 0.9734
27 0.01994 0.03987 0.9801
28 0.03201 0.06402 0.968
29 0.02615 0.05231 0.9738
30 0.9294 0.1411 0.07055
31 0.9362 0.1275 0.06377
32 0.9138 0.1725 0.08624
33 0.899 0.2021 0.101
34 0.8882 0.2236 0.1118
35 0.8536 0.2927 0.1464
36 0.813 0.374 0.187
37 0.7937 0.4125 0.2063
38 0.8281 0.3439 0.1719
39 0.7845 0.4311 0.2155
40 0.735 0.5301 0.265
41 0.7548 0.4904 0.2452
42 0.7015 0.5969 0.2985
43 0.6453 0.7094 0.3547
44 0.5839 0.8321 0.4161
45 0.6414 0.7172 0.3586
46 0.6689 0.6623 0.3311
47 0.6105 0.779 0.3895
48 0.5812 0.8375 0.4188
49 0.5167 0.9666 0.4833
50 0.4519 0.9038 0.5481
51 0.4243 0.8485 0.5757
52 0.3626 0.7252 0.6374
53 0.5143 0.9714 0.4857
54 0.4474 0.8947 0.5526
55 0.5088 0.9823 0.4912
56 0.46 0.92 0.54
57 0.3917 0.7834 0.6083
58 0.582 0.8359 0.418
59 0.5853 0.8293 0.4147
60 0.611 0.778 0.389
61 0.7032 0.5936 0.2968
62 0.7292 0.5415 0.2708
63 0.6703 0.6593 0.3297
64 0.6255 0.749 0.3745
65 0.7332 0.5336 0.2668
66 0.7185 0.5631 0.2815
67 0.6441 0.7119 0.3559
68 0.6076 0.7849 0.3924
69 0.5196 0.9608 0.4804
70 0.4274 0.8548 0.5726
71 0.7616 0.4768 0.2384
72 0.673 0.6541 0.327
73 0.5724 0.8552 0.4276
74 0.4589 0.9179 0.5411
75 0.3883 0.7767 0.6117
76 0.356 0.712 0.644
77 0.4022 0.8044 0.5978
78 0.2625 0.5249 0.7375


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level10.0138889OK
10% type I error level80.111111NOK


Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.61606, df1 = 2, df2 = 79, p-value = 0.5426
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.93052, df1 = 6, df2 = 75, p-value = 0.4782
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.84446, df1 = 2, df2 = 79, p-value = 0.4336


Variance Inflation Factors (Multicollinearity)
> vif
   IKSUM  KVDDSUM SKEOUSUM 
1.036213 1.022608 1.018379