Multiple Linear Regression - Estimated Regression Equation
GW[t] = + 12.4001 -0.154457Imago[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)+12.4 0.535+2.3180e+01 4.188e-41 2.094e-41
Imago-0.1545 0.1431-1.0790e+00 0.2833 0.1417


Multiple Linear Regression - Regression Statistics
Multiple R 0.1095
R-squared 0.01198
Adjusted R-squared 0.00169
F-TEST (value) 1.164
F-TEST (DF numerator)1
F-TEST (DF denominator)96
p-value 0.2833
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.514
Sum Squared Residuals 220


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
1 11 12.09-1.091
2 9 11.78-2.782
3 12 11.78 0.2177
4 12 11.78 0.2177
5 12 12.25-0.2457
6 11 11.78-0.7823
7 12 12.09-0.09123
8 12 11.78 0.2177
9 15 11.78 3.218
10 13 11.63 1.372
11 12 11.78 0.2177
12 11 12.25-1.246
13 9 11.78-2.782
14 11 11.63-0.6279
15 12 12.25-0.2457
16 12 11.63 0.3721
17 12 11.94 0.06322
18 14 11.63 2.372
19 12 11.63 0.3721
20 9 12.09-3.091
21 13 12.25 0.7543
22 13 11.78 1.218
23 12 11.78 0.2177
24 12 11.63 0.3721
25 12 11.78 0.2177
26 12 12.09-0.09123
27 11 11.94-0.9368
28 13 12.25 0.7543
29 13 11.94 1.063
30 13 11.78 1.218
31 10 11.78-1.782
32 13 11.78 1.218
33 5 11.63-6.628
34 10 11.94-1.937
35 15 11.78 3.218
36 13 11.78 1.218
37 12 11.78 0.2177
38 13 11.78 1.218
39 13 11.78 1.218
40 11 11.78-0.7823
41 12 11.78 0.2177
42 12 11.78 0.2177
43 13 11.78 1.218
44 14 11.78 2.218
45 12 11.63 0.3721
46 12 11.78 0.2177
47 10 11.63-1.628
48 12 11.63 0.3721
49 12 12.09-0.09123
50 12 11.78 0.2177
51 13 11.94 1.063
52 14 12.09 1.909
53 10 11.63-1.628
54 12 11.94 0.06322
55 13 12.09 0.9088
56 11 11.78-0.7823
57 12 11.94 0.06322
58 12 11.78 0.2177
59 13 11.78 1.218
60 12 11.94 0.06322
61 9 11.78-2.782
62 12 11.94 0.06322
63 14 11.78 2.218
64 11 11.94-0.9368
65 12 11.78 0.2177
66 12 11.78 0.2177
67 9 11.78-2.782
68 13 12.09 0.9088
69 10 11.63-1.628
70 14 11.78 2.218
71 10 11.94-1.937
72 12 11.78 0.2177
73 11 11.78-0.7823
74 14 12.09 1.909
75 13 11.78 1.218
76 12 11.78 0.2177
77 10 11.94-1.937
78 12 11.63 0.3721
79 12 11.63 0.3721
80 15 11.94 3.063
81 12 12.09-0.09123
82 12 12.09-0.09123
83 10 11.78-1.782
84 12 11.78 0.2177
85 12 11.63 0.3721
86 12 11.94 0.06322
87 11 11.78-0.7823
88 13 11.78 1.218
89 13 11.78 1.218
90 11 11.78-0.7823
91 10 12.09-2.091
92 9 11.63-2.628
93 12 11.78 0.2177
94 13 11.78 1.218
95 10 12.09-2.091
96 13 11.94 1.063
97 12 11.78 0.2177
98 12 11.94 0.06322


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
5 0.5589 0.8823 0.4411
6 0.3889 0.7777 0.6111
7 0.2645 0.529 0.7355
8 0.2035 0.4069 0.7965
9 0.6968 0.6063 0.3032
10 0.638 0.7241 0.362
11 0.5367 0.9266 0.4633
12 0.4512 0.9024 0.5488
13 0.6686 0.6629 0.3314
14 0.602 0.796 0.398
15 0.5252 0.9495 0.4748
16 0.4446 0.8893 0.5554
17 0.368 0.736 0.632
18 0.4582 0.9163 0.5418
19 0.3827 0.7654 0.6173
20 0.54 0.9199 0.46
21 0.5454 0.9092 0.4546
22 0.5168 0.9665 0.4832
23 0.446 0.8921 0.554
24 0.3798 0.7596 0.6202
25 0.3158 0.6317 0.6842
26 0.2617 0.5235 0.7383
27 0.2255 0.451 0.7745
28 0.2149 0.4298 0.7851
29 0.1951 0.3903 0.8049
30 0.1768 0.3537 0.8232
31 0.2048 0.4096 0.7952
32 0.1873 0.3746 0.8127
33 0.97 0.05998 0.02999
34 0.9747 0.05059 0.0253
35 0.9933 0.01349 0.006745
36 0.9922 0.01565 0.007823
37 0.9884 0.02315 0.01157
38 0.9867 0.02662 0.01331
39 0.9847 0.03054 0.01527
40 0.9799 0.04011 0.02006
41 0.9718 0.05643 0.02821
42 0.9611 0.07788 0.03894
43 0.9565 0.0871 0.04355
44 0.9701 0.05987 0.02993
45 0.9601 0.07971 0.03986
46 0.9463 0.1075 0.05373
47 0.9473 0.1055 0.05274
48 0.9317 0.1365 0.06826
49 0.911 0.1779 0.08896
50 0.8858 0.2284 0.1142
51 0.8698 0.2604 0.1302
52 0.8836 0.2328 0.1164
53 0.8852 0.2296 0.1148
54 0.8537 0.2926 0.1463
55 0.8302 0.3396 0.1698
56 0.8003 0.3995 0.1997
57 0.7558 0.4884 0.2442
58 0.7076 0.5848 0.2924
59 0.6909 0.6181 0.3091
60 0.6359 0.7282 0.3641
61 0.7561 0.4878 0.2439
62 0.7054 0.5892 0.2946
63 0.7674 0.4652 0.2326
64 0.7356 0.5288 0.2644
65 0.6835 0.633 0.3165
66 0.6272 0.7455 0.3728
67 0.7555 0.4891 0.2445
68 0.7229 0.5542 0.2771
69 0.7379 0.5242 0.2621
70 0.7978 0.4043 0.2022
71 0.8268 0.3464 0.1732
72 0.78 0.44 0.22
73 0.7419 0.5161 0.2581
74 0.7943 0.4114 0.2057
75 0.7778 0.4445 0.2222
76 0.721 0.558 0.279
77 0.7509 0.4982 0.2491
78 0.6888 0.6223 0.3112
79 0.6203 0.7594 0.3797
80 0.8662 0.2675 0.1338
81 0.8213 0.3574 0.1787
82 0.7705 0.4589 0.2295
83 0.7961 0.4078 0.2039
84 0.7277 0.5447 0.2723
85 0.6451 0.7099 0.3549
86 0.5613 0.8774 0.4387
87 0.4833 0.9666 0.5167
88 0.4546 0.9092 0.5454
89 0.4459 0.8917 0.5541
90 0.3411 0.6822 0.6589
91 0.3352 0.6704 0.6648
92 0.8442 0.3116 0.1558
93 0.7555 0.4889 0.2445


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level60.0674157NOK
10% type I error level130.146067NOK


Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.7487, df1 = 2, df2 = 94, p-value = 0.1796
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.7487, df1 = 2, df2 = 94, p-value = 0.1796
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.7487, df1 = 2, df2 = 94, p-value = 0.1796