Multiple Linear Regression - Estimated Regression Equation |
ITHSUM[t] = + 9.42232 + 0.291481SK1[t] + 0.376166SK3[t] + 0.392351SK4[t] + 0.374714SK5[t] + 0.286452SK6[t] + e[t] |
Multiple Linear Regression - Ordinary Least Squares | |||||
Variable | Parameter | S.D. | T-STAT H0: parameter = 0 | 2-tail p-value | 1-tail p-value |
(Intercept) | +9.422 | 2.51 | +3.7540e+00 | 0.0002451 | 0.0001226 |
SK1 | +0.2915 | 0.2677 | +1.0890e+00 | 0.2779 | 0.1389 |
SK3 | +0.3762 | 0.2447 | +1.5370e+00 | 0.1263 | 0.06313 |
SK4 | +0.3923 | 0.334 | +1.1750e+00 | 0.2419 | 0.1209 |
SK5 | +0.3747 | 0.3185 | +1.1760e+00 | 0.2412 | 0.1206 |
SK6 | +0.2864 | 0.3304 | +8.6700e-01 | 0.3873 | 0.1936 |
Multiple Linear Regression - Regression Statistics | |
Multiple R | 0.2264 |
R-squared | 0.05127 |
Adjusted R-squared | 0.02086 |
F-TEST (value) | 1.686 |
F-TEST (DF numerator) | 5 |
F-TEST (DF denominator) | 156 |
p-value | 0.1411 |
Multiple Linear Regression - Residual Statistics | |
Residual Standard Deviation | 2.444 |
Sum Squared Residuals | 932.1 |
Multiple Linear Regression - Actuals, Interpolation, and Residuals | |||
Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error |
1 | 14 | 16.29 | -2.289 |
2 | 19 | 16.6 | 2.403 |
3 | 17 | 17.06 | -0.05786 |
4 | 17 | 15.25 | 1.753 |
5 | 15 | 17.06 | -2.058 |
6 | 20 | 16.68 | 3.323 |
7 | 15 | 15.25 | -0.2484 |
8 | 19 | 16.39 | 2.608 |
9 | 15 | 16.97 | -1.968 |
10 | 15 | 17.34 | -2.344 |
11 | 19 | 15.93 | 3.071 |
12 | 20 | 16.2 | 3.799 |
13 | 18 | 16.68 | 1.322 |
14 | 15 | 16.22 | -1.22 |
15 | 14 | 16.68 | -2.678 |
16 | 20 | 16.68 | 3.322 |
17 | 16 | 16.21 | -0.2061 |
18 | 16 | 16.68 | -0.6817 |
19 | 16 | 16.29 | -0.2858 |
20 | 10 | 16.97 | -6.968 |
21 | 19 | 16.97 | 2.03 |
22 | 19 | 16.68 | 2.317 |
23 | 16 | 16.97 | -0.9696 |
24 | 15 | 16.02 | -1.016 |
25 | 18 | 16.28 | 1.716 |
26 | 17 | 16.31 | 0.693 |
27 | 19 | 16.76 | 2.239 |
28 | 17 | 16.6 | 0.4015 |
29 | 19 | 17.34 | 1.656 |
30 | 20 | 17.26 | 2.739 |
31 | 5 | 17.34 | -12.34 |
32 | 19 | 16.47 | 2.525 |
33 | 16 | 15.55 | 0.4454 |
34 | 15 | 16.39 | -1.392 |
35 | 16 | 16.58 | -0.5772 |
36 | 18 | 15.93 | 2.069 |
37 | 16 | 16.68 | -0.6831 |
38 | 15 | 16.6 | -1.598 |
39 | 17 | 17.34 | -0.3443 |
40 | 20 | 17.24 | 2.757 |
41 | 19 | 16.58 | 2.418 |
42 | 7 | 16.2 | -9.201 |
43 | 13 | 16.31 | -3.307 |
44 | 16 | 15.62 | 0.3754 |
45 | 16 | 16.68 | -0.6817 |
46 | 18 | 16.68 | 1.317 |
47 | 18 | 16.22 | 1.779 |
48 | 16 | 16.97 | -0.9746 |
49 | 17 | 17.34 | -0.3443 |
50 | 19 | 16.2 | 2.799 |
51 | 16 | 16.68 | -0.6831 |
52 | 19 | 15.25 | 3.752 |
53 | 13 | 15.73 | -2.729 |
54 | 16 | 16.29 | -0.2893 |
55 | 13 | 16.68 | -3.682 |
56 | 12 | 16.88 | -4.883 |
57 | 17 | 16.39 | 0.6148 |
58 | 17 | 16.97 | 0.03185 |
59 | 17 | 15.27 | 1.734 |
60 | 16 | 17.34 | -1.344 |
61 | 16 | 16.31 | -0.307 |
62 | 14 | 16 | -2.003 |
63 | 16 | 16.29 | -0.2893 |
64 | 13 | 16.49 | -3.494 |
65 | 16 | 16.28 | -0.2843 |
66 | 14 | 15.91 | -1.91 |
67 | 20 | 17.45 | 2.55 |
68 | 12 | 15.93 | -3.931 |
69 | 13 | 16.31 | -3.307 |
70 | 18 | 17.07 | 0.926 |
71 | 14 | 15.64 | -1.639 |
72 | 19 | 16.68 | 2.318 |
73 | 18 | 16.66 | 1.34 |
74 | 14 | 16.68 | -2.678 |
75 | 18 | 16.68 | 1.317 |
76 | 19 | 16.97 | 2.03 |
77 | 15 | 15.64 | -0.6393 |
78 | 14 | 16.68 | -2.682 |
79 | 17 | 16.59 | 0.408 |
80 | 19 | 16.97 | 2.032 |
81 | 13 | 16.6 | -3.598 |
82 | 19 | 16.9 | 2.099 |
83 | 18 | 17.34 | 0.6557 |
84 | 20 | 16.68 | 3.322 |
85 | 15 | 16.02 | -1.016 |
86 | 15 | 15.54 | -0.5385 |
87 | 15 | 16.4 | -1.397 |
88 | 20 | 16.97 | 3.03 |
89 | 15 | 16.68 | -1.682 |
90 | 19 | 16.4 | 2.605 |
91 | 18 | 16.28 | 1.716 |
92 | 18 | 16.97 | 1.03 |
93 | 15 | 16.51 | -1.509 |
94 | 20 | 17.65 | 2.347 |
95 | 17 | 16.97 | 0.03185 |
96 | 12 | 16.68 | -4.678 |
97 | 18 | 17.26 | 0.7404 |
98 | 19 | 16.97 | 2.03 |
99 | 20 | 16.88 | 3.115 |
100 | 17 | 17.65 | -0.652 |
101 | 15 | 16.95 | -1.952 |
102 | 16 | 15.25 | 0.748 |
103 | 18 | 16.68 | 1.317 |
104 | 18 | 16.68 | 1.317 |
105 | 14 | 16.48 | -2.48 |
106 | 15 | 16.31 | -1.307 |
107 | 12 | 16.2 | -4.201 |
108 | 17 | 16.28 | 0.7157 |
109 | 14 | 15.91 | -1.915 |
110 | 18 | 16.39 | 1.608 |
111 | 17 | 16.31 | 0.6916 |
112 | 17 | 16.46 | 0.5413 |
113 | 20 | 17.64 | 2.364 |
114 | 16 | 16.02 | -0.02053 |
115 | 14 | 16.29 | -2.291 |
116 | 15 | 15.91 | -0.9096 |
117 | 18 | 16.31 | 1.693 |
118 | 20 | 16.68 | 3.318 |
119 | 17 | 15.93 | 1.069 |
120 | 17 | 16.02 | 0.9845 |
121 | 17 | 15.91 | 1.085 |
122 | 17 | 16.59 | 0.4066 |
123 | 15 | 15.25 | -0.247 |
124 | 17 | 15.63 | 1.372 |
125 | 18 | 15.23 | 2.769 |
126 | 17 | 16.29 | 0.7107 |
127 | 20 | 16.58 | 3.419 |
128 | 15 | 15.24 | -0.2434 |
129 | 16 | 16.02 | -0.0155 |
130 | 15 | 15.91 | -0.9146 |
131 | 18 | 16.97 | 1.027 |
132 | 15 | 16.68 | -1.683 |
133 | 18 | 17.74 | 0.2583 |
134 | 20 | 17.34 | 2.656 |
135 | 19 | 16.2 | 2.799 |
136 | 14 | 16.77 | -2.766 |
137 | 16 | 16.68 | -0.6831 |
138 | 15 | 15.55 | -0.5546 |
139 | 17 | 16.59 | 0.408 |
140 | 18 | 16.97 | 1.032 |
141 | 20 | 16.96 | 3.043 |
142 | 17 | 16.68 | 0.3169 |
143 | 18 | 16.68 | 1.317 |
144 | 15 | 14.87 | 0.1292 |
145 | 16 | 16.4 | -0.3967 |
146 | 11 | 15.91 | -4.915 |
147 | 15 | 16.59 | -1.593 |
148 | 18 | 16.66 | 1.34 |
149 | 17 | 16.97 | 0.0304 |
150 | 16 | 17.35 | -1.349 |
151 | 12 | 16.31 | -4.308 |
152 | 19 | 16.1 | 2.9 |
153 | 18 | 16.59 | 1.407 |
154 | 15 | 16.58 | -1.576 |
155 | 17 | 16.02 | 0.9795 |
156 | 19 | 17.08 | 1.925 |
157 | 18 | 16.22 | 1.778 |
158 | 19 | 16.21 | 2.794 |
159 | 16 | 15.55 | 0.4518 |
160 | 16 | 16.59 | -0.5934 |
161 | 16 | 16.68 | -0.6817 |
162 | 14 | 16.49 | -2.491 |
Goldfeld-Quandt test for Heteroskedasticity | |||
p-values | Alternative Hypothesis | ||
breakpoint index | greater | 2-sided | less |
9 | 0.2115 | 0.423 | 0.7885 |
10 | 0.129 | 0.2579 | 0.871 |
11 | 0.1801 | 0.3601 | 0.8199 |
12 | 0.6002 | 0.7997 | 0.3998 |
13 | 0.485 | 0.97 | 0.515 |
14 | 0.4765 | 0.9529 | 0.5235 |
15 | 0.5313 | 0.9373 | 0.4687 |
16 | 0.5751 | 0.8498 | 0.4249 |
17 | 0.4981 | 0.9962 | 0.5019 |
18 | 0.4243 | 0.8486 | 0.5757 |
19 | 0.3414 | 0.6828 | 0.6586 |
20 | 0.8273 | 0.3453 | 0.1727 |
21 | 0.8392 | 0.3216 | 0.1608 |
22 | 0.8292 | 0.3416 | 0.1708 |
23 | 0.7811 | 0.4378 | 0.2189 |
24 | 0.7812 | 0.4376 | 0.2188 |
25 | 0.7481 | 0.5038 | 0.2519 |
26 | 0.6916 | 0.6167 | 0.3084 |
27 | 0.6563 | 0.6874 | 0.3437 |
28 | 0.5996 | 0.8009 | 0.4004 |
29 | 0.5925 | 0.815 | 0.4075 |
30 | 0.6391 | 0.7218 | 0.3609 |
31 | 0.9991 | 0.001732 | 0.0008659 |
32 | 0.9989 | 0.002123 | 0.001061 |
33 | 0.9986 | 0.002863 | 0.001431 |
34 | 0.9982 | 0.003534 | 0.001767 |
35 | 0.9973 | 0.005398 | 0.002699 |
36 | 0.9964 | 0.007129 | 0.003564 |
37 | 0.9948 | 0.01042 | 0.005208 |
38 | 0.9931 | 0.01374 | 0.006872 |
39 | 0.9906 | 0.01884 | 0.009419 |
40 | 0.9936 | 0.01288 | 0.006441 |
41 | 0.9931 | 0.01387 | 0.006937 |
42 | 1 | 7.514e-05 | 3.757e-05 |
43 | 1 | 4.917e-05 | 2.458e-05 |
44 | 1 | 8.146e-05 | 4.073e-05 |
45 | 0.9999 | 0.0001308 | 6.54e-05 |
46 | 0.9999 | 0.0001923 | 9.616e-05 |
47 | 0.9999 | 0.0002456 | 0.0001228 |
48 | 0.9998 | 0.0003711 | 0.0001856 |
49 | 0.9997 | 0.0005674 | 0.0002837 |
50 | 0.9997 | 0.0005051 | 0.0002526 |
51 | 0.9996 | 0.0007621 | 0.0003811 |
52 | 0.9997 | 0.0005368 | 0.0002684 |
53 | 0.9998 | 0.0004048 | 0.0002024 |
54 | 0.9997 | 0.0006243 | 0.0003122 |
55 | 0.9998 | 0.000383 | 0.0001915 |
56 | 0.9999 | 0.0001084 | 5.42e-05 |
57 | 0.9999 | 0.0001713 | 8.566e-05 |
58 | 0.9999 | 0.0002632 | 0.0001316 |
59 | 0.9999 | 0.0002858 | 0.0001429 |
60 | 0.9998 | 0.0003695 | 0.0001848 |
61 | 0.9997 | 0.0005728 | 0.0002864 |
62 | 0.9997 | 0.0006181 | 0.000309 |
63 | 0.9995 | 0.0009254 | 0.0004627 |
64 | 0.9997 | 0.0006332 | 0.0003166 |
65 | 0.9995 | 0.0009514 | 0.0004757 |
66 | 0.9995 | 0.001078 | 0.000539 |
67 | 0.9995 | 0.001011 | 0.0005054 |
68 | 0.9998 | 0.0004958 | 0.0002479 |
69 | 0.9998 | 0.0003319 | 0.000166 |
70 | 0.9998 | 0.0004824 | 0.0002412 |
71 | 0.9997 | 0.0006046 | 0.0003023 |
72 | 0.9997 | 0.0006331 | 0.0003166 |
73 | 0.9996 | 0.0008567 | 0.0004283 |
74 | 0.9996 | 0.0007675 | 0.0003838 |
75 | 0.9995 | 0.001033 | 0.0005163 |
76 | 0.9994 | 0.001147 | 0.0005736 |
77 | 0.9992 | 0.001672 | 0.0008361 |
78 | 0.9993 | 0.001405 | 0.0007026 |
79 | 0.999 | 0.002013 | 0.001007 |
80 | 0.9989 | 0.002286 | 0.001143 |
81 | 0.9994 | 0.001202 | 0.0006009 |
82 | 0.9993 | 0.001352 | 0.0006761 |
83 | 0.999 | 0.001955 | 0.0009774 |
84 | 0.9994 | 0.001241 | 0.0006204 |
85 | 0.9991 | 0.001745 | 0.0008724 |
86 | 0.9987 | 0.002506 | 0.001253 |
87 | 0.9984 | 0.003182 | 0.001591 |
88 | 0.9988 | 0.002436 | 0.001218 |
89 | 0.9987 | 0.002688 | 0.001344 |
90 | 0.9987 | 0.002669 | 0.001335 |
91 | 0.9984 | 0.003146 | 0.001573 |
92 | 0.9979 | 0.004265 | 0.002132 |
93 | 0.9977 | 0.004573 | 0.002286 |
94 | 0.9975 | 0.00491 | 0.002455 |
95 | 0.9965 | 0.007065 | 0.003533 |
96 | 0.9988 | 0.002435 | 0.001217 |
97 | 0.9983 | 0.003498 | 0.001749 |
98 | 0.998 | 0.003906 | 0.001953 |
99 | 0.9984 | 0.003191 | 0.001596 |
100 | 0.9979 | 0.004225 | 0.002112 |
101 | 0.9979 | 0.004232 | 0.002116 |
102 | 0.997 | 0.006028 | 0.003014 |
103 | 0.9961 | 0.007805 | 0.003903 |
104 | 0.995 | 0.009996 | 0.004998 |
105 | 0.9957 | 0.008558 | 0.004279 |
106 | 0.9946 | 0.0108 | 0.005399 |
107 | 0.9981 | 0.003719 | 0.001859 |
108 | 0.9973 | 0.005497 | 0.002749 |
109 | 0.9972 | 0.00557 | 0.002785 |
110 | 0.9968 | 0.006412 | 0.003206 |
111 | 0.9957 | 0.00869 | 0.004345 |
112 | 0.9948 | 0.01048 | 0.005238 |
113 | 0.9938 | 0.0124 | 0.006198 |
114 | 0.991 | 0.01803 | 0.009016 |
115 | 0.9924 | 0.01519 | 0.007596 |
116 | 0.99 | 0.01994 | 0.00997 |
117 | 0.9882 | 0.02359 | 0.0118 |
118 | 0.991 | 0.01802 | 0.009008 |
119 | 0.988 | 0.02397 | 0.01198 |
120 | 0.9849 | 0.03022 | 0.01511 |
121 | 0.9791 | 0.04179 | 0.02089 |
122 | 0.9708 | 0.05833 | 0.02916 |
123 | 0.9601 | 0.07985 | 0.03993 |
124 | 0.9491 | 0.1018 | 0.05092 |
125 | 0.9497 | 0.1005 | 0.05026 |
126 | 0.9324 | 0.1352 | 0.0676 |
127 | 0.9387 | 0.1226 | 0.06132 |
128 | 0.9184 | 0.1633 | 0.08164 |
129 | 0.8936 | 0.2127 | 0.1064 |
130 | 0.8669 | 0.2662 | 0.1331 |
131 | 0.8312 | 0.3376 | 0.1688 |
132 | 0.8079 | 0.3843 | 0.1921 |
133 | 0.7597 | 0.4807 | 0.2403 |
134 | 0.7533 | 0.4934 | 0.2467 |
135 | 0.7656 | 0.4687 | 0.2344 |
136 | 0.7869 | 0.4263 | 0.2131 |
137 | 0.7352 | 0.5296 | 0.2648 |
138 | 0.6736 | 0.6528 | 0.3264 |
139 | 0.604 | 0.792 | 0.396 |
140 | 0.536 | 0.9281 | 0.4641 |
141 | 0.5511 | 0.8978 | 0.4489 |
142 | 0.4742 | 0.9484 | 0.5258 |
143 | 0.4241 | 0.8482 | 0.5759 |
144 | 0.345 | 0.69 | 0.655 |
145 | 0.2692 | 0.5383 | 0.7308 |
146 | 0.5417 | 0.9166 | 0.4583 |
147 | 0.4748 | 0.9495 | 0.5252 |
148 | 0.4054 | 0.8107 | 0.5946 |
149 | 0.3113 | 0.6226 | 0.6887 |
150 | 0.2325 | 0.4651 | 0.7675 |
151 | 0.8912 | 0.2177 | 0.1088 |
152 | 0.9127 | 0.1746 | 0.08732 |
153 | 0.8306 | 0.3388 | 0.1694 |
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity | |||
Description | # significant tests | % significant tests | OK/NOK |
1% type I error level | 75 | 0.5172 | NOK |
5% type I error level | 91 | 0.627586 | NOK |
10% type I error level | 93 | 0.641379 | NOK |
Ramsey RESET F-Test for powers (2 and 3) of fitted values |
> reset_test_fitted RESET test data: mylm RESET = 0.8606, df1 = 2, df2 = 154, p-value = 0.4249 |
Ramsey RESET F-Test for powers (2 and 3) of regressors |
> reset_test_regressors RESET test data: mylm RESET = 0.78783, df1 = 10, df2 = 146, p-value = 0.6405 |
Ramsey RESET F-Test for powers (2 and 3) of principal components |
> reset_test_principal_components RESET test data: mylm RESET = 1.0393, df1 = 2, df2 = 154, p-value = 0.3562 |
Variance Inflation Factors (Multicollinearity) |
> vif SK1 SK3 SK4 SK5 SK6 1.019622 1.033103 1.019654 1.046954 1.045610 |