Multiple Linear Regression - Estimated Regression Equation
EP1[t] = + 4.18795 + 0.12653EP3[t] -0.0021116`TVDSUM\r`[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)+4.188 0.7248+5.7780e+00 8.768e-08 4.384e-08
EP3+0.1265 0.0774+1.6350e+00 0.1053 0.05264
`TVDSUM\r`-0.002112 0.04241-4.9800e-02 0.9604 0.4802


Multiple Linear Regression - Regression Statistics
Multiple R 0.1654
R-squared 0.02736
Adjusted R-squared 0.007715
F-TEST (value) 1.393
F-TEST (DF numerator)2
F-TEST (DF denominator)99
p-value 0.2532
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 0.7898
Sum Squared Residuals 61.75


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
1 5 4.667 0.3334
2 3 4.407-1.407
3 5 4.532 0.4684
4 5 4.534 0.4662
5 5 4.658 0.3418
6 4 4.405-0.4051
7 2 4.409-2.409
8 5 4.534 0.4662
9 4 4.411-0.4114
10 5 4.534 0.4662
11 4 4.405-0.4051
12 4 4.407-0.4072
13 5 4.281 0.7193
14 5 4.407 0.5928
15 5 4.536 0.4641
16 5 4.787 0.2132
17 5 4.407 0.5928
18 5 4.793 0.2069
19 5 4.409 0.5907
20 5 4.405 0.5949
21 5 4.287 0.713
22 4 4.405-0.4051
23 5 4.538 0.462
24 5 4.411 0.5886
25 5 4.53 0.4705
26 5 4.532 0.4684
27 5 4.667 0.3334
28 5 4.66 0.3397
29 5 4.536 0.4641
30 5 4.409 0.5907
31 4 4.667-0.6666
32 5 4.532 0.4684
33 3 4.418-1.418
34 4 4.411-0.4114
35 3 4.54-1.54
36 5 4.405 0.5949
37 5 4.534 0.4662
38 5 4.405 0.5949
39 5 4.407 0.5928
40 5 4.66 0.3397
41 5 4.66 0.3397
42 4 4.536-0.5359
43 5 4.669 0.3313
44 4 4.658-0.6582
45 5 4.665 0.3355
46 2 4.665-2.665
47 4 4.787-0.7868
48 5 4.536 0.4641
49 3 4.66-1.66
50 4 4.411-0.4114
51 4 4.789-0.7889
52 5 4.279 0.7214
53 4 4.546-0.5464
54 5 4.405 0.5949
55 4 4.399-0.3988
56 5 4.279 0.7214
57 5 4.403 0.597
58 4 4.405-0.4051
59 4 4.411-0.4114
60 3 4.405-1.405
61 5 4.279 0.7214
62 4 4.407-0.4072
63 5 4.53 0.4705
64 2 4.276-2.276
65 5 4.407 0.5928
66 3 4.409-1.409
67 5 4.414 0.5864
68 5 4.534 0.4662
69 5 4.542 0.4578
70 5 4.534 0.4662
71 5 4.407 0.5928
72 5 4.281 0.7193
73 5 4.411 0.5886
74 5 4.662 0.3376
75 5 4.538 0.462
76 5 4.409 0.5907
77 5 4.536 0.4641
78 4 4.534-0.5338
79 5 4.544 0.4557
80 4 4.403-0.403
81 4 4.291-0.2913
82 4 4.53-0.5295
83 4 4.536-0.5359
84 5 4.274 0.7256
85 2 4.279-2.279
86 4 4.285-0.2849
87 5 4.54 0.4599
88 4 4.405-0.4051
89 5 4.411 0.5886
90 4 4.654-0.654
91 5 4.411 0.5886
92 4 4.534-0.5338
93 5 4.66 0.3397
94 5 4.536 0.4641
95 5 4.669 0.3313
96 5 4.658 0.3418
97 3 4.276-1.276
98 5 4.662 0.3376
99 5 4.53 0.4705
100 5 4.409 0.5907
101 5 4.534 0.4662
102 5 4.407 0.5928


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
6 0.3663 0.7325 0.6337
7 0.6183 0.7635 0.3817
8 0.5573 0.8853 0.4427
9 0.637 0.726 0.363
10 0.5633 0.8734 0.4367
11 0.4668 0.9336 0.5332
12 0.3892 0.7783 0.6108
13 0.7608 0.4784 0.2392
14 0.7669 0.4662 0.2331
15 0.7198 0.5605 0.2802
16 0.6746 0.6507 0.3253
17 0.6633 0.6735 0.3367
18 0.5881 0.8238 0.4119
19 0.5837 0.8327 0.4163
20 0.5452 0.9095 0.4548
21 0.5773 0.8455 0.4227
22 0.523 0.9541 0.477
23 0.465 0.9301 0.535
24 0.4249 0.8498 0.5751
25 0.3761 0.7522 0.6239
26 0.326 0.6521 0.674
27 0.27 0.5399 0.73
28 0.2204 0.4407 0.7796
29 0.1826 0.3652 0.8174
30 0.1602 0.3205 0.8398
31 0.1716 0.3432 0.8284
32 0.1411 0.2821 0.8589
33 0.2136 0.4271 0.7864
34 0.1781 0.3562 0.8219
35 0.296 0.592 0.704
36 0.264 0.528 0.736
37 0.2269 0.4538 0.7731
38 0.1995 0.399 0.8005
39 0.1783 0.3567 0.8217
40 0.1463 0.2926 0.8537
41 0.119 0.2379 0.881
42 0.1061 0.2122 0.8939
43 0.09212 0.1842 0.9079
44 0.103 0.206 0.897
45 0.0835 0.167 0.9165
46 0.5587 0.8827 0.4413
47 0.565 0.87 0.435
48 0.5272 0.9456 0.4728
49 0.7263 0.5473 0.2737
50 0.6902 0.6197 0.3098
51 0.7063 0.5874 0.2937
52 0.7037 0.5926 0.2963
53 0.7112 0.5776 0.2888
54 0.6916 0.6168 0.3084
55 0.6751 0.6499 0.3249
56 0.691 0.618 0.309
57 0.6884 0.6232 0.3116
58 0.6516 0.6969 0.3484
59 0.616 0.7681 0.384
60 0.7338 0.5324 0.2662
61 0.7647 0.4707 0.2353
62 0.7279 0.5441 0.2721
63 0.7022 0.5956 0.2978
64 0.9348 0.1305 0.06523
65 0.929 0.1419 0.07095
66 0.9728 0.05443 0.02722
67 0.9663 0.06739 0.03369
68 0.9568 0.08645 0.04322
69 0.9438 0.1125 0.05624
70 0.9293 0.1414 0.07069
71 0.9231 0.1539 0.07693
72 0.9364 0.1273 0.06364
73 0.9263 0.1473 0.07366
74 0.9012 0.1976 0.09882
75 0.8736 0.2528 0.1264
76 0.8646 0.2708 0.1354
77 0.8335 0.3329 0.1665
78 0.813 0.374 0.187
79 0.7665 0.467 0.2335
80 0.7107 0.5786 0.2893
81 0.6664 0.6672 0.3336
82 0.6134 0.7732 0.3866
83 0.6013 0.7974 0.3987
84 0.8174 0.3652 0.1826
85 0.9864 0.02713 0.01356
86 0.9822 0.03565 0.01782
87 0.9703 0.05939 0.0297
88 0.9537 0.0926 0.0463
89 0.9262 0.1477 0.07383
90 0.9108 0.1783 0.08915
91 0.8772 0.2456 0.1228
92 0.8919 0.2163 0.1081
93 0.8219 0.3563 0.1781
94 0.7203 0.5595 0.2797
95 0.7184 0.5632 0.2816
96 0.556 0.888 0.444


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level20.021978OK
10% type I error level70.0769231OK


Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.0035, df1 = 2, df2 = 97, p-value = 0.3704
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.90583, df1 = 4, df2 = 95, p-value = 0.4639
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.81257, df1 = 2, df2 = 97, p-value = 0.4467


Variance Inflation Factors (Multicollinearity)
> vif
        EP3 `TVDSUM\\r` 
   1.030526    1.030526