Multiple Linear Regression - Estimated Regression Equation
TevrSUM[t] = + 14.9975 -0.416161Imago1[t] + 0.141125Imago2[t] + 0.15563Imago3[t] + 0.230593Imago4[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)+15 1.401+1.0700e+01 5.905e-18 2.952e-18
Imago1-0.4162 0.1796-2.3180e+00 0.02265 0.01132
Imago2+0.1411 0.1666+8.4710e-01 0.3991 0.1995
Imago3+0.1556 0.1893+8.2200e-01 0.4131 0.2066
Imago4+0.2306 0.2475+9.3180e-01 0.3538 0.1769


Multiple Linear Regression - Regression Statistics
Multiple R 0.258
R-squared 0.06656
Adjusted R-squared 0.02684
F-TEST (value) 1.676
F-TEST (DF numerator)4
F-TEST (DF denominator)94
p-value 0.1621
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.843
Sum Squared Residuals 319.3


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
1 13 15.84-2.837
2 16 14.69 1.307
3 17 15.32 1.684
4 16 15.22 0.7795
5 17 15.29 1.713
6 17 15.68 1.319
7 15 14.62 0.3818
8 16 15.35 0.6472
9 14 15.03-1.026
10 16 15.16 0.84
11 17 16.71 0.2947
12 16 15.17 0.8327
13 16 16.42-0.423
14 16 14.64 1.36
15 15 15.33-0.3311
16 16 15.41 0.5876
17 16 15.58 0.4238
18 13 14.74-1.744
19 15 16.12-1.119
20 17 16.25 0.7471
21 13 14.77-1.774
22 17 15.29 1.713
23 14 15.27-1.271
24 14 15.29-1.287
25 17 15.76 1.238
26 13 15.7-2.703
27 16 16.17-0.1722
28 15 15.58-0.5762
29 15 15.49-0.4867
30 13 15.44-2.442
31 17 15.44 1.558
32 11 14.43-3.433
33 14 15.35-1.346
34 13 15.28-2.279
35 17 15.29 1.713
36 16 14.84 1.16
37 17 15 1.996
38 16 15.3 0.6988
39 16 15.09 0.9149
40 16 15.13 0.869
41 15 14.91 0.08505
42 12 15.84-3.837
43 17 15.06 1.944
44 14 15.58-1.583
45 14 15.29-1.287
46 16 15.3 0.6988
47 15 15.24-0.2422
48 16 15.29 0.7133
49 14 15.26-1.257
50 15 14.72 0.2794
51 17 15.98 1.022
52 10 15.13-5.131
53 17 15.78 1.222
54 20 15.99 4.008
55 17 14.45 2.553
56 17 16.27 0.7254
57 14 15.29-1.287
58 17 15.7 1.297
59 17 15.21 1.788
60 16 15.7 0.2972
61 18 15.67 2.327
62 18 14.74 3.262
63 16 15.86 0.1415
64 15 15.95-0.9479
65 13 15.63-2.628
66 16 15.58 0.4166
67 12 15.07-3.071
68 16 14.86 1.137
69 16 16.12-0.119
70 16 14.43 1.567
71 14 15.44-1.442
72 15 15.33-0.3311
73 14 15.13-1.131
74 15 15.16-0.16
75 15 16.27-1.275
76 16 15.6 0.4021
77 11 15.12-4.115
78 18 14.67 3.331
79 11 14.74-3.744
80 18 16.16 1.845
81 15 15.81-0.8134
82 19 16.52 2.48
83 17 15.59 1.408
84 14 15.6-1.598
85 13 16.09-3.089
86 17 15.86 1.142
87 14 15.43-1.428
88 19 15.37 3.633
89 14 15.05-1.047
90 16 15.58 0.4166
91 16 15.17 0.8254
92 15 15.98-0.9779
93 12 14.82-2.819
94 18 15.43 2.572
95 15 16.04-1.044
96 18 15.76 2.237
97 15 15.49-0.4867
98 16 15.15 0.8545
99 16 15.27 0.7293


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
8 0.5804 0.8393 0.4196
9 0.6439 0.7121 0.3561
10 0.5066 0.9867 0.4934
11 0.4098 0.8197 0.5902
12 0.295 0.5901 0.705
13 0.2021 0.4042 0.7979
14 0.1391 0.2782 0.8609
15 0.09315 0.1863 0.9068
16 0.05773 0.1155 0.9423
17 0.03383 0.06765 0.9662
18 0.05557 0.1111 0.9444
19 0.04977 0.09954 0.9502
20 0.03554 0.07107 0.9645
21 0.04043 0.08085 0.9596
22 0.03267 0.06534 0.9673
23 0.03002 0.06004 0.97
24 0.03194 0.06389 0.9681
25 0.02746 0.05492 0.9725
26 0.0615 0.123 0.9385
27 0.04183 0.08367 0.9582
28 0.02885 0.0577 0.9711
29 0.01954 0.03907 0.9805
30 0.02955 0.05909 0.9705
31 0.02846 0.05692 0.9715
32 0.07344 0.1469 0.9266
33 0.06185 0.1237 0.9382
34 0.06601 0.132 0.934
35 0.06315 0.1263 0.9369
36 0.05172 0.1034 0.9483
37 0.06362 0.1272 0.9364
38 0.04862 0.09723 0.9514
39 0.03815 0.07629 0.9619
40 0.02852 0.05705 0.9715
41 0.01963 0.03926 0.9804
42 0.06465 0.1293 0.9354
43 0.06354 0.1271 0.9365
44 0.06355 0.1271 0.9365
45 0.05479 0.1096 0.9452
46 0.04204 0.08408 0.958
47 0.03011 0.06021 0.9699
48 0.02235 0.04471 0.9776
49 0.01748 0.03496 0.9825
50 0.01278 0.02555 0.9872
51 0.009733 0.01947 0.9903
52 0.09388 0.1878 0.9061
53 0.08297 0.1659 0.917
54 0.1904 0.3809 0.8096
55 0.2271 0.4542 0.7729
56 0.1895 0.379 0.8105
57 0.1697 0.3394 0.8303
58 0.1487 0.2973 0.8513
59 0.1428 0.2857 0.8572
60 0.1115 0.2229 0.8885
61 0.1264 0.2528 0.8736
62 0.2041 0.4082 0.7959
63 0.1629 0.3258 0.8371
64 0.1351 0.2703 0.8649
65 0.1661 0.3322 0.8339
66 0.1306 0.2611 0.8694
67 0.1845 0.3691 0.8155
68 0.1645 0.3291 0.8355
69 0.1272 0.2543 0.8728
70 0.1321 0.2642 0.8679
71 0.1179 0.2358 0.8821
72 0.08845 0.1769 0.9115
73 0.0686 0.1372 0.9314
74 0.04904 0.09809 0.951
75 0.04186 0.08372 0.9581
76 0.02858 0.05717 0.9714
77 0.07687 0.1537 0.9231
78 0.1823 0.3647 0.8177
79 0.2682 0.5364 0.7318
80 0.242 0.4841 0.758
81 0.2072 0.4144 0.7928
82 0.2789 0.5578 0.7211
83 0.22 0.44 0.78
84 0.1834 0.3668 0.8166
85 0.3409 0.6817 0.6591
86 0.2563 0.5127 0.7437
87 0.2819 0.5638 0.7181
88 0.4251 0.8501 0.5749
89 0.428 0.856 0.572
90 0.3277 0.6554 0.6723
91 0.8573 0.2855 0.1427


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level60.0714286NOK
10% type I error level260.309524NOK


Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.31852, df1 = 2, df2 = 92, p-value = 0.728
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.4997, df1 = 8, df2 = 86, p-value = 0.1692
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.38035, df1 = 2, df2 = 92, p-value = 0.6847


Variance Inflation Factors (Multicollinearity)
> vif
  Imago1   Imago2   Imago3   Imago4 
1.032480 1.038649 1.063048 1.055761