Multiple Linear Regression - Estimated Regression Equation
ITHSUM[t] = + 14.7695 + 0.132034TVDCSUM[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)+14.77 1.919+7.6970e+00 1.117e-11 5.585e-12
TVDCSUM+0.132 0.1227+1.0760e+00 0.2845 0.1422


Multiple Linear Regression - Regression Statistics
Multiple R 0.1081
R-squared 0.01168
Adjusted R-squared 0.001596
F-TEST (value) 1.158
F-TEST (DF numerator)1
F-TEST (DF denominator)98
p-value 0.2845
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 2.278
Sum Squared Residuals 508.7


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
1 14 16.49-2.486
2 19 16.88 2.118
3 17 17.01-0.01409
4 20 16.88 3.118
5 15 17.01-2.014
6 19 17.01 1.986
7 20 16.88 3.118
8 18 16.62 1.382
9 15 16.88-1.882
10 14 17.01-3.014
11 16 16.88-0.8821
12 19 16.88 2.118
13 18 16.88 1.118
14 17 16.75 0.25
15 19 16.88 2.118
16 17 16.88 0.1179
17 19 16.75 2.25
18 20 17.01 2.986
19 19 16.49 2.514
20 16 17.01-1.014
21 16 16.62-0.618
22 18 16.62 1.382
23 16 17.15-1.146
24 17 17.01-0.01409
25 20 16.88 3.118
26 19 16.75 2.25
27 7 16.75-9.75
28 16 16.75-0.75
29 16 16.49-0.486
30 18 17.01 0.9859
31 17 16.22 0.7781
32 19 16.62 2.382
33 16 16.49-0.486
34 13 17.01-4.014
35 16 16.88-0.8821
36 12 17.01-5.014
37 17 16.88 0.1179
38 17 16.88 0.1179
39 17 16.88 0.1179
40 16 16.75-0.75
41 16 16.35-0.3539
42 14 17.01-3.014
43 16 16.62-0.618
44 13 16.62-3.618
45 16 16.88-0.8821
46 14 16.75-2.75
47 19 16.88 2.118
48 18 16.62 1.382
49 14 16.75-2.75
50 18 17.01 0.9859
51 15 16.09-1.09
52 17 17.01-0.01409
53 13 17.41-4.41
54 19 17.01 1.986
55 18 17.15 0.8539
56 15 17.01-2.014
57 15 16.62-1.618
58 20 17.01 2.986
59 19 17.01 1.986
60 18 16.88 1.118
61 15 17.15-2.146
62 20 17.15 2.854
63 17 16.88 0.1179
64 19 16.75 2.25
65 20 16.49 3.514
66 18 16.88 1.118
67 17 16.35 0.6461
68 18 16.88 1.118
69 17 16.88 0.1179
70 20 16.88 3.118
71 16 16.62-0.618
72 14 16.75-2.75
73 15 16.62-1.618
74 20 16.75 3.25
75 17 16.75 0.25
76 17 16.88 0.1179
77 18 16.22 1.778
78 20 17.15 2.854
79 16 16.22-0.2219
80 18 17.15 0.8539
81 15 16.75-1.75
82 18 17.28 0.7218
83 20 17.01 2.986
84 14 16.62-2.618
85 15 16.49-1.486
86 17 17.01-0.01409
87 18 16.62 1.382
88 20 17.28 2.722
89 17 16.62 0.382
90 16 16.88-0.8821
91 11 16.88-5.882
92 15 16.75-1.75
93 18 16.35 1.646
94 16 17.01-1.014
95 18 17.15 0.8539
96 15 16.75-1.75
97 17 17.15-0.1461
98 19 16.75 2.25
99 16 16.88-0.8821
100 14 16.88-2.882


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
5 0.7444 0.5111 0.2556
6 0.6299 0.7402 0.3701
7 0.6397 0.7207 0.3603
8 0.5621 0.8759 0.4379
9 0.5946 0.8108 0.4054
10 0.7209 0.5581 0.2791
11 0.6469 0.7062 0.3531
12 0.6142 0.7716 0.3858
13 0.5332 0.9336 0.4668
14 0.442 0.8839 0.558
15 0.4068 0.8135 0.5932
16 0.3271 0.6543 0.6729
17 0.3066 0.6132 0.6934
18 0.3177 0.6354 0.6823
19 0.3072 0.6144 0.6928
20 0.2734 0.5468 0.7266
21 0.2345 0.469 0.7655
22 0.1883 0.3767 0.8117
23 0.1613 0.3225 0.8387
24 0.1216 0.2432 0.8784
25 0.1433 0.2867 0.8567
26 0.1279 0.2558 0.8721
27 0.9711 0.05781 0.0289
28 0.9609 0.07816 0.03908
29 0.9467 0.1067 0.05333
30 0.9301 0.1397 0.06987
31 0.9093 0.1815 0.09074
32 0.9068 0.1865 0.09324
33 0.8812 0.2376 0.1188
34 0.9316 0.1367 0.06837
35 0.9132 0.1735 0.08677
36 0.97 0.0601 0.03005
37 0.9583 0.08333 0.04167
38 0.9434 0.1133 0.05663
39 0.9245 0.1509 0.07547
40 0.9042 0.1916 0.09582
41 0.879 0.2421 0.121
42 0.8959 0.2081 0.1041
43 0.8697 0.2605 0.1303
44 0.9106 0.1789 0.08944
45 0.8886 0.2228 0.1114
46 0.9003 0.1995 0.09975
47 0.8968 0.2064 0.1032
48 0.8786 0.2428 0.1214
49 0.8917 0.2166 0.1083
50 0.8685 0.263 0.1315
51 0.8444 0.3112 0.1556
52 0.8073 0.3855 0.1927
53 0.9024 0.1951 0.09757
54 0.8956 0.2089 0.1044
55 0.8709 0.2582 0.1291
56 0.8691 0.2619 0.1309
57 0.8548 0.2904 0.1452
58 0.8743 0.2514 0.1257
59 0.8642 0.2716 0.1358
60 0.8365 0.3271 0.1635
61 0.8409 0.3182 0.1591
62 0.8548 0.2904 0.1452
63 0.8173 0.3654 0.1827
64 0.8141 0.3717 0.1859
65 0.8718 0.2564 0.1282
66 0.8444 0.3112 0.1556
67 0.8116 0.3769 0.1884
68 0.7767 0.4465 0.2233
69 0.7261 0.5479 0.2739
70 0.7741 0.4519 0.2259
71 0.7241 0.5518 0.2759
72 0.7459 0.5082 0.2541
73 0.7154 0.5691 0.2846
74 0.7804 0.4392 0.2196
75 0.7274 0.5453 0.2726
76 0.6664 0.6672 0.3336
77 0.6843 0.6315 0.3157
78 0.7151 0.5699 0.2849
79 0.6631 0.6739 0.3369
80 0.6003 0.7994 0.3997
81 0.5513 0.8975 0.4487
82 0.4777 0.9553 0.5223
83 0.5569 0.8861 0.4431
84 0.5446 0.9107 0.4554
85 0.4797 0.9594 0.5203
86 0.3962 0.7923 0.6038
87 0.3611 0.7222 0.6389
88 0.4957 0.9915 0.5043
89 0.4081 0.8162 0.5919
90 0.3135 0.6269 0.6865
91 0.7706 0.4588 0.2294
92 0.7301 0.5397 0.2698
93 0.6771 0.6459 0.3229
94 0.5442 0.9116 0.4558
95 0.4277 0.8554 0.5723


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level00OK
10% type I error level40.043956OK


Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.42125, df1 = 2, df2 = 96, p-value = 0.6574
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.42125, df1 = 2, df2 = 96, p-value = 0.6574
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.42125, df1 = 2, df2 = 96, p-value = 0.6574