Multiple Linear Regression - Estimated Regression Equation
GWSUM[t] = + 14.7832 + 0.138622IK1[t] + 0.153261IK2[t] -0.485353IK3[t] + 0.0132018IK4[t] + 0.000686423KVDD1[t] + 0.225461KVDD2[t] -0.0870457KVDD3[t] -0.0388076KVDD4[t] -0.24919`SK/EOU1`[t] + 0.548165`SK/EOU2`[t] -0.0398974`SK/EOU3`[t] -0.15473`SK/EOU4`[t] -0.252386`SK/EOU5`[t] -0.400778`SK/EOU6`[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)+14.78 3.102+4.7650e+00 9.903e-06 4.952e-06
IK1+0.1386 0.3432+4.0380e-01 0.6876 0.3438
IK2+0.1533 0.2778+5.5160e-01 0.583 0.2915
IK3-0.4854 0.3445-1.4090e+00 0.1633 0.08163
IK4+0.0132 0.2966+4.4510e-02 0.9646 0.4823
KVDD1+0.0006864 0.2334+2.9410e-03 0.9977 0.4988
KVDD2+0.2255 0.1722+1.3090e+00 0.1947 0.09734
KVDD3-0.08705 0.2004-4.3440e-01 0.6653 0.3327
KVDD4-0.03881 0.2044-1.8980e-01 0.85 0.425
`SK/EOU1`-0.2492 0.2506-9.9440e-01 0.3234 0.1617
`SK/EOU2`+0.5482 0.2785+1.9680e+00 0.05301 0.02651
`SK/EOU3`-0.0399 0.2429-1.6420e-01 0.87 0.435
`SK/EOU4`-0.1547 0.3327-4.6510e-01 0.6433 0.3217
`SK/EOU5`-0.2524 0.2816-8.9630e-01 0.3731 0.1866
`SK/EOU6`-0.4008 0.2875-1.3940e+00 0.1678 0.08389


Multiple Linear Regression - Regression Statistics
Multiple R 0.3617
R-squared 0.1308
Adjusted R-squared-0.04304
F-TEST (value) 0.7524
F-TEST (DF numerator)14
F-TEST (DF denominator)70
p-value 0.7149
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.513
Sum Squared Residuals 160.2


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 10.74 0.2574
2 12 12.21-0.2068
3 12 11.48 0.5154
4 12 12.8-0.8044
5 11 11.44-0.4412
6 12 12.09-0.0936
7 12 12.3-0.2972
8 15 11.6 3.397
9 13 11.52 1.478
10 12 12.42-0.4191
11 11 11.05-0.05408
12 12 10.87 1.134
13 12 12.49-0.4892
14 12 12.34-0.3418
15 14 11.85 2.146
16 12 11.13 0.871
17 9 11.76-2.763
18 13 11.47 1.528
19 13 11.82 1.18
20 12 12.8-0.8034
21 12 11.66 0.3398
22 12 11.71 0.293
23 12 12.09-0.09445
24 12 11.93 0.06669
25 11 11.79-0.7907
26 13 12.56 0.4403
27 13 11.84 1.164
28 10 11.79-1.791
29 13 12.57 0.4298
30 5 10.7-5.696
31 10 12.66-2.655
32 12 12.45-0.4464
33 13 11.41 1.595
34 13 11.37 1.63
35 12 11.8 0.2002
36 12 11.89 0.1128
37 13 12.25 0.7505
38 14 12.01 1.994
39 12 12.75-0.7487
40 12 11.55 0.4547
41 10 11.89-1.886
42 12 11.7 0.3006
43 12 11.16 0.8386
44 12 11.87 0.1322
45 14 12.25 1.754
46 10 11.28-1.278
47 12 11.26 0.7438
48 11 12.02-1.025
49 12 11.64 0.3593
50 12 12.76-0.7583
51 13 11.84 1.161
52 12 11.69 0.3054
53 9 11.41-2.413
54 12 12.14-0.1356
55 14 11.06 2.942
56 11 11.27-0.267
57 12 11.18 0.8204
58 9 12.25-3.253
59 13 12.02 0.9766
60 10 11.51-1.507
61 14 12.37 1.628
62 10 12.48-2.485
63 12 11.8 0.2008
64 11 11.56-0.5586
65 14 12.58 1.416
66 13 12.66 0.3363
67 12 11.87 0.1321
68 10 11.37-1.372
69 12 11.96 0.03936
70 12 12.19-0.1927
71 15 12.91 2.093
72 12 11.96 0.03805
73 12 11.86 0.1405
74 12 12.11-0.109
75 12 11.97 0.03113
76 11 11.48-0.4798
77 13 11.98 1.017
78 13 13.08-0.08381
79 10 11.24-1.245
80 9 11.42-2.421
81 12 11.26 0.7431
82 10 10.82-0.8242
83 13 12.5 0.5032
84 12 11.9 0.1047
85 12 11.5 0.4952


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
18 0.6436 0.7128 0.3564
19 0.533 0.934 0.467
20 0.6444 0.7113 0.3556
21 0.5836 0.8327 0.4164
22 0.4605 0.9209 0.5395
23 0.3569 0.7139 0.6431
24 0.2861 0.5722 0.7139
25 0.2138 0.4276 0.7862
26 0.2068 0.4136 0.7932
27 0.2541 0.5082 0.7459
28 0.2454 0.4909 0.7546
29 0.1893 0.3785 0.8107
30 0.9526 0.09489 0.04745
31 0.9708 0.05842 0.02921
32 0.963 0.074 0.037
33 0.9567 0.08651 0.04326
34 0.9543 0.09136 0.04568
35 0.9322 0.1356 0.06781
36 0.9073 0.1854 0.09268
37 0.8753 0.2494 0.1247
38 0.8856 0.2288 0.1144
39 0.8504 0.2993 0.1496
40 0.8119 0.3762 0.1881
41 0.8424 0.3153 0.1576
42 0.7926 0.4148 0.2074
43 0.7506 0.4988 0.2494
44 0.6899 0.6202 0.3101
45 0.7168 0.5663 0.2832
46 0.7144 0.5711 0.2856
47 0.669 0.662 0.331
48 0.7764 0.4472 0.2236
49 0.724 0.5521 0.276
50 0.6551 0.6898 0.3449
51 0.6916 0.6168 0.3084
52 0.627 0.7459 0.373
53 0.8423 0.3154 0.1577
54 0.8159 0.3681 0.184
55 0.9351 0.1297 0.06485
56 0.9175 0.1651 0.08254
57 0.9683 0.06331 0.03165
58 0.9968 0.00637 0.003185
59 0.9931 0.01387 0.006934
60 0.9874 0.02528 0.01264
61 0.9833 0.03349 0.01675
62 0.9921 0.0158 0.007898
63 0.9999 0.0001463 7.317e-05
64 0.9996 0.0007894 0.0003947
65 0.9982 0.003617 0.001808
66 0.9948 0.01039 0.005197
67 0.9765 0.04692 0.02346


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level4 0.08NOK
5% type I error level100.2NOK
10% type I error level160.32NOK


Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 2.7706, df1 = 2, df2 = 68, p-value = 0.0697
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.8361, df1 = 28, df2 = 42, p-value = 0.6874
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.10566, df1 = 2, df2 = 68, p-value = 0.8999


Variance Inflation Factors (Multicollinearity)
> vif
      IK1       IK2       IK3       IK4     KVDD1     KVDD2     KVDD3     KVDD4 
 1.542067  1.319036  1.705432  1.290774  1.168808  1.128414  1.220570  1.261831 
`SK/EOU1` `SK/EOU2` `SK/EOU3` `SK/EOU4` `SK/EOU5` `SK/EOU6` 
 1.360240  1.302373  1.145574  1.175605  1.143655  1.116096