Multiple Linear Regression - Estimated Regression Equation
TVDSUM[t] = + 16.2633 -0.299347EP3[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)+16.26 0.5002+3.2510e+01 2.612e-55 1.306e-55
EP3-0.2994 0.177-1.6920e+00 0.0938 0.0469


Multiple Linear Regression - Regression Statistics
Multiple R 0.166
R-squared 0.02755
Adjusted R-squared 0.01792
F-TEST (value) 2.862
F-TEST (DF numerator)1
F-TEST (DF denominator)101
p-value 0.0938
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 1.856
Sum Squared Residuals 347.8


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
1 13 15.07-2.066
2 16 15.66 0.3354
3 17 15.37 1.635
4 16 15.37 0.6347
5 17 15.07 1.934
6 17 15.66 1.335
7 15 15.66-0.6646
8 16 15.37 0.6347
9 14 15.66-1.665
10 16 15.37 0.6347
11 17 15.66 1.335
12 16 15.66 0.3354
13 16 15.96 0.03602
14 16 15.66 0.3354
15 15 15.37-0.3653
16 16 14.77 1.233
17 16 15.66 0.3354
18 13 14.77-1.767
19 15 15.66-0.6646
20 17 15.66 1.335
21 13 15.96-2.964
22 17 15.66 1.335
23 14 15.37-1.365
24 14 15.66-1.665
25 18 15.37 2.635
26 17 15.37 1.635
27 13 15.07-2.066
28 16 15.07 0.9341
29 15 15.37-0.3653
30 15 15.66-0.6646
31 15 15.96-0.964
32 13 15.07-2.066
33 17 15.37 1.635
34 11 15.66-4.665
35 14 15.66-1.665
36 13 15.37-2.365
37 17 15.66 1.335
38 16 15.37 0.6347
39 17 15.66 1.335
40 16 15.66 0.3354
41 16 15.07 0.9341
42 16 15.07 0.9341
43 15 15.37-0.3653
44 12 15.07-3.066
45 17 15.07 1.934
46 14 15.07-1.066
47 14 15.07-1.066
48 16 14.77 1.233
49 15 15.37-0.3653
50 16 15.07 0.9341
51 14 15.66-1.665
52 15 14.77 0.2334
53 17 15.96 1.036
54 10 15.37-5.365
55 17 15.66 1.335
56 20 15.66 4.335
57 17 15.96 1.036
58 18 15.66 2.335
59 17 15.66 1.335
60 14 15.66-1.665
61 17 15.66 1.335
62 17 15.96 1.036
63 16 15.66 0.3354
64 18 15.37 2.635
65 18 15.96 2.036
66 16 15.66 0.3354
67 15 15.66-0.6646
68 13 15.66-2.665
69 16 15.37 0.6347
70 12 15.37-3.365
71 16 15.37 0.6347
72 16 15.66 0.3354
73 16 15.96 0.03602
74 14 15.66-1.665
75 15 15.07-0.06594
76 14 15.37-1.365
77 15 15.66-0.6646
78 15 15.37-0.3653
79 16 15.37 0.6347
80 11 15.37-4.365
81 18 15.66 2.335
82 11 15.96-4.964
83 18 15.37 2.635
84 15 15.37-0.3653
85 19 15.96 3.036
86 17 15.96 1.036
87 14 15.96-1.964
88 13 15.37-2.365
89 17 15.66 1.335
90 14 15.66-1.665
91 19 15.07 3.934
92 14 15.66-1.665
93 16 15.37 0.6347
94 16 15.07 0.9341
95 15 15.37-0.3653
96 12 15.07-3.066
97 17 15.07 1.934
98 18 15.96 2.036
99 15 15.07-0.06594
100 18 15.37 2.635
101 15 15.66-0.6646
102 16 15.37 0.6347
103 16 15.66 0.3354


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
5 0.5641 0.8717 0.4359
6 0.4006 0.8012 0.5994
7 0.3527 0.7055 0.6473
8 0.2349 0.4697 0.7651
9 0.2715 0.5431 0.7285
10 0.1855 0.3709 0.8145
11 0.1467 0.2935 0.8533
12 0.09297 0.1859 0.907
13 0.05683 0.1137 0.9432
14 0.03316 0.06633 0.9668
15 0.02117 0.04233 0.9788
16 0.01318 0.02635 0.9868
17 0.007077 0.01415 0.9929
18 0.01293 0.02586 0.9871
19 0.008974 0.01795 0.991
20 0.006772 0.01354 0.9932
21 0.02996 0.05992 0.97
22 0.02527 0.05055 0.9747
23 0.02349 0.04697 0.9765
24 0.02373 0.04747 0.9763
25 0.04095 0.0819 0.959
26 0.03726 0.07452 0.9627
27 0.05038 0.1008 0.9496
28 0.03772 0.07544 0.9623
29 0.02643 0.05286 0.9736
30 0.01886 0.03771 0.9811
31 0.01383 0.02766 0.9862
32 0.01807 0.03613 0.9819
33 0.01734 0.03469 0.9827
34 0.1179 0.2358 0.8821
35 0.1092 0.2183 0.8908
36 0.128 0.256 0.872
37 0.1187 0.2374 0.8813
38 0.09492 0.1898 0.9051
39 0.08638 0.1728 0.9136
40 0.06616 0.1323 0.9338
41 0.05278 0.1056 0.9472
42 0.04152 0.08303 0.9585
43 0.0303 0.0606 0.9697
44 0.05595 0.1119 0.944
45 0.05795 0.1159 0.9421
46 0.04778 0.09555 0.9522
47 0.03896 0.07792 0.961
48 0.03241 0.06481 0.9676
49 0.02346 0.04692 0.9765
50 0.0181 0.03619 0.9819
51 0.01655 0.03311 0.9834
52 0.01154 0.02307 0.9885
53 0.009296 0.01859 0.9907
54 0.09755 0.1951 0.9024
55 0.08726 0.1745 0.9127
56 0.2329 0.4659 0.7671
57 0.2039 0.4078 0.7961
58 0.2259 0.4517 0.7741
59 0.2047 0.4094 0.7953
60 0.1954 0.3909 0.8046
61 0.1759 0.3519 0.8241
62 0.1519 0.3037 0.8481
63 0.1213 0.2425 0.8787
64 0.15 0.3 0.85
65 0.1604 0.3209 0.8396
66 0.129 0.2581 0.871
67 0.1031 0.2063 0.8969
68 0.1268 0.2537 0.8732
69 0.1012 0.2024 0.8988
70 0.1687 0.3374 0.8313
71 0.1363 0.2725 0.8637
72 0.1068 0.2136 0.8932
73 0.08179 0.1636 0.9182
74 0.07418 0.1484 0.9258
75 0.05493 0.1099 0.9451
76 0.04722 0.09444 0.9528
77 0.03469 0.06937 0.9653
78 0.02455 0.0491 0.9754
79 0.01706 0.03411 0.9829
80 0.07789 0.1558 0.9221
81 0.08711 0.1742 0.9129
82 0.3429 0.6857 0.6571
83 0.3782 0.7564 0.6218
84 0.317 0.6339 0.683
85 0.4195 0.839 0.5805
86 0.3802 0.7604 0.6198
87 0.3651 0.7303 0.6349
88 0.4457 0.8915 0.5543
89 0.3922 0.7844 0.6078
90 0.3908 0.7817 0.6092
91 0.6434 0.7131 0.3566
92 0.6997 0.6006 0.3003
93 0.6011 0.7977 0.3989
94 0.522 0.9559 0.478
95 0.4191 0.8382 0.5809
96 0.7637 0.4725 0.2363
97 0.7032 0.5937 0.2968
98 0.6394 0.7212 0.3606


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level190.202128NOK
10% type I error level330.351064NOK


Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.052744, df1 = 2, df2 = 99, p-value = 0.9486
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.052744, df1 = 2, df2 = 99, p-value = 0.9486
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.052744, df1 = 2, df2 = 99, p-value = 0.9486