Multiple Linear Regression - Estimated Regression Equation |
TVSUM[t] = + 6.84284 + 0.499442SK1[t] + 1.12986SK2[t] + 0.335972SK4[t] + 0.181257SK5[t] + 0.24936ALG2[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) | +6.843 | 1.2 | +5.7010e+00 | 5.641e-08 | 2.821e-08 |
SK1 | +0.4994 | 0.1579 | +3.1620e+00 | 0.001875 | 0.0009373 |
SK2 | +1.13 | 0.1914 | +5.9030e+00 | 2.091e-08 | 1.045e-08 |
SK4 | +0.336 | 0.2072 | +1.6220e+00 | 0.1068 | 0.05342 |
SK5 | +0.1813 | 0.1802 | +1.0060e+00 | 0.3162 | 0.1581 |
ALG2 | +0.2494 | 0.2531 | +9.8510e-01 | 0.3261 | 0.163 |
Multiple Linear Regression - Regression Statistics | |
Multiple R | 0.56 |
R-squared | 0.3136 |
Adjusted R-squared | 0.292 |
F-TEST (value) | 14.53 |
F-TEST (DF numerator) | 5 |
F-TEST (DF denominator) | 159 |
p-value | 1.014e-11 |
Multiple Linear Regression - Residual Statistics | |
Residual Standard Deviation | 1.395 |
Sum Squared Residuals | 309.6 |
Multiple Linear Regression - Actuals, Interpolation, and Residuals | |||
Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error |
1 | 13 | 13.26 | -0.2639 |
2 | 16 | 15.23 | 0.7708 |
3 | 17 | 15.86 | 1.14 |
4 | 15 | 14.84 | 0.1571 |
5 | 16 | 15.86 | 0.1404 |
6 | 16 | 15.36 | 0.6398 |
7 | 17 | 14.66 | 2.338 |
8 | 16 | 15.18 | 0.8211 |
9 | 17 | 16.74 | 0.2599 |
10 | 17 | 16.99 | 0.01054 |
11 | 17 | 15.86 | 1.14 |
12 | 15 | 15.27 | -0.2743 |
13 | 16 | 15.09 | 0.907 |
14 | 14 | 14.05 | -0.04904 |
15 | 16 | 15.32 | 0.6842 |
16 | 17 | 15.18 | 1.821 |
17 | 16 | 15.18 | 0.8211 |
18 | 15 | 16.97 | -1.972 |
19 | 17 | 15.86 | 1.14 |
20 | 16 | 14.84 | 1.157 |
21 | 15 | 15.86 | -0.8596 |
22 | 16 | 15.68 | 0.3217 |
23 | 15 | 15.68 | -0.6783 |
24 | 17 | 15.68 | 1.322 |
25 | 15 | 15.18 | -0.1789 |
26 | 16 | 14.77 | 1.225 |
27 | 15 | 15.43 | -0.429 |
28 | 16 | 14.86 | 1.139 |
29 | 16 | 16.18 | -0.1778 |
30 | 13 | 14.55 | -1.548 |
31 | 15 | 16.74 | -1.74 |
32 | 17 | 16.18 | 0.8222 |
33 | 15 | 14.3 | 0.7009 |
34 | 13 | 13.73 | -0.7309 |
35 | 17 | 16.81 | 0.1918 |
36 | 15 | 15.18 | -0.1789 |
37 | 14 | 13.96 | 0.03686 |
38 | 14 | 14.3 | -0.2991 |
39 | 18 | 15.43 | 2.571 |
40 | 15 | 15.93 | -0.9284 |
41 | 17 | 16.99 | 0.01054 |
42 | 13 | 13.8 | -0.7997 |
43 | 16 | 17.15 | -1.153 |
44 | 15 | 15.59 | -0.5924 |
45 | 15 | 15.34 | -0.3424 |
46 | 16 | 15.68 | 0.3217 |
47 | 15 | 15.54 | -0.5422 |
48 | 13 | 15.86 | -2.86 |
49 | 17 | 16.56 | 0.4412 |
50 | 17 | 17.24 | -0.2395 |
51 | 17 | 17.06 | -0.05828 |
52 | 11 | 14.48 | -3.48 |
53 | 14 | 14.21 | -0.2125 |
54 | 13 | 15.68 | -2.678 |
55 | 15 | 14.66 | 0.3383 |
56 | 17 | 14.93 | 2.07 |
57 | 16 | 15.27 | 0.7257 |
58 | 15 | 15.86 | -0.8596 |
59 | 17 | 17.49 | -0.4889 |
60 | 16 | 14.61 | 1.389 |
61 | 16 | 15.86 | 0.1404 |
62 | 16 | 14.82 | 1.184 |
63 | 15 | 15.86 | -0.8596 |
64 | 12 | 13.42 | -1.419 |
65 | 17 | 15.52 | 1.476 |
66 | 14 | 15.52 | -1.524 |
67 | 14 | 15.66 | -1.661 |
68 | 16 | 15.02 | 0.9758 |
69 | 15 | 14.84 | 0.1571 |
70 | 15 | 17.33 | -2.325 |
71 | 13 | 15.68 | -2.678 |
72 | 13 | 15.43 | -2.429 |
73 | 17 | 16.2 | 0.8044 |
74 | 15 | 15.18 | -0.1789 |
75 | 16 | 15.86 | 0.1404 |
76 | 14 | 15.02 | -1.024 |
77 | 15 | 14.05 | 0.951 |
78 | 17 | 14.55 | 2.452 |
79 | 16 | 15.68 | 0.3217 |
80 | 12 | 14.05 | -2.049 |
81 | 16 | 15.86 | 0.1404 |
82 | 17 | 15.61 | 1.39 |
83 | 17 | 15.86 | 1.14 |
84 | 20 | 16.18 | 3.822 |
85 | 17 | 16.51 | 0.4862 |
86 | 18 | 15.86 | 2.14 |
87 | 15 | 15.18 | -0.1789 |
88 | 17 | 15.18 | 1.821 |
89 | 14 | 13.08 | 0.9174 |
90 | 15 | 15.43 | -0.429 |
91 | 17 | 15.68 | 1.322 |
92 | 16 | 15.86 | 0.1404 |
93 | 17 | 16.74 | 0.2599 |
94 | 15 | 15.02 | -0.02418 |
95 | 16 | 15.68 | 0.3217 |
96 | 18 | 16.18 | 1.822 |
97 | 18 | 16.51 | 1.486 |
98 | 16 | 16.99 | -0.9895 |
99 | 17 | 15.23 | 1.771 |
100 | 15 | 15.68 | -0.6783 |
101 | 13 | 16.18 | -3.178 |
102 | 15 | 14.84 | 0.1571 |
103 | 17 | 16.7 | 0.305 |
104 | 16 | 15.09 | 0.907 |
105 | 16 | 15.34 | 0.6576 |
106 | 15 | 15.68 | -0.6783 |
107 | 16 | 15.68 | 0.3217 |
108 | 16 | 15.36 | 0.6398 |
109 | 13 | 15.68 | -2.678 |
110 | 15 | 15.09 | -0.09301 |
111 | 12 | 13.89 | -1.894 |
112 | 19 | 15.34 | 3.658 |
113 | 16 | 15.18 | 0.8211 |
114 | 16 | 15.5 | 0.5029 |
115 | 17 | 16.7 | 0.305 |
116 | 16 | 16.11 | -0.1097 |
117 | 14 | 15.43 | -1.429 |
118 | 15 | 15.09 | -0.09301 |
119 | 14 | 14.84 | -0.8429 |
120 | 16 | 15.43 | 0.571 |
121 | 15 | 15.86 | -0.8596 |
122 | 17 | 16.81 | 0.1918 |
123 | 15 | 15.18 | -0.1789 |
124 | 16 | 15.34 | 0.6576 |
125 | 16 | 15.68 | 0.3217 |
126 | 15 | 14.59 | 0.4064 |
127 | 15 | 15.09 | -0.09301 |
128 | 11 | 12.25 | -1.247 |
129 | 16 | 15.27 | 0.7257 |
130 | 18 | 16.02 | 1.977 |
131 | 13 | 13.91 | -0.9129 |
132 | 11 | 14.05 | -3.049 |
133 | 16 | 15.34 | 0.6576 |
134 | 18 | 17.24 | 0.7605 |
135 | 15 | 16.81 | -1.808 |
136 | 19 | 17.82 | 1.175 |
137 | 17 | 16.99 | 0.01054 |
138 | 13 | 15.09 | -2.093 |
139 | 14 | 15.11 | -1.111 |
140 | 16 | 15.68 | 0.3217 |
141 | 13 | 15.68 | -2.678 |
142 | 17 | 15.86 | 1.14 |
143 | 14 | 15.86 | -1.86 |
144 | 19 | 16.02 | 2.977 |
145 | 14 | 14.55 | -0.5485 |
146 | 16 | 15.68 | 0.3217 |
147 | 12 | 13.71 | -1.713 |
148 | 16 | 16.81 | -0.8082 |
149 | 16 | 15.34 | 0.6576 |
150 | 15 | 15.43 | -0.429 |
151 | 12 | 14.77 | -2.775 |
152 | 15 | 15.68 | -0.6783 |
153 | 17 | 16.11 | 0.8903 |
154 | 13 | 15.5 | -2.497 |
155 | 15 | 13.55 | 1.45 |
156 | 18 | 15.43 | 2.571 |
157 | 15 | 14.39 | 0.6062 |
158 | 18 | 15.43 | 2.571 |
159 | 15 | 17.14 | -2.144 |
160 | 15 | 16.18 | -1.178 |
161 | 16 | 15.84 | 0.1582 |
162 | 13 | 14.23 | -1.23 |
163 | 16 | 15.68 | 0.3217 |
164 | 13 | 15.86 | -2.86 |
165 | 16 | 14.07 | 1.933 |
Goldfeld-Quandt test for Heteroskedasticity | |||
p-values | Alternative Hypothesis | ||
breakpoint index | greater | 2-sided | less |
9 | 0.1405 | 0.281 | 0.8595 |
10 | 0.06798 | 0.136 | 0.932 |
11 | 0.04305 | 0.0861 | 0.9569 |
12 | 0.01743 | 0.03486 | 0.9826 |
13 | 0.00651 | 0.01302 | 0.9935 |
14 | 0.00923 | 0.01846 | 0.9908 |
15 | 0.0171 | 0.03419 | 0.9829 |
16 | 0.0182 | 0.03641 | 0.9818 |
17 | 0.009079 | 0.01816 | 0.9909 |
18 | 0.03233 | 0.06467 | 0.9677 |
19 | 0.02439 | 0.04877 | 0.9756 |
20 | 0.01649 | 0.03299 | 0.9835 |
21 | 0.01801 | 0.03601 | 0.982 |
22 | 0.01046 | 0.02093 | 0.9895 |
23 | 0.01037 | 0.02074 | 0.9896 |
24 | 0.008727 | 0.01745 | 0.9913 |
25 | 0.009399 | 0.0188 | 0.9906 |
26 | 0.006058 | 0.01212 | 0.9939 |
27 | 0.005386 | 0.01077 | 0.9946 |
28 | 0.003407 | 0.006815 | 0.9966 |
29 | 0.001927 | 0.003855 | 0.9981 |
30 | 0.004896 | 0.009792 | 0.9951 |
31 | 0.008914 | 0.01783 | 0.9911 |
32 | 0.008657 | 0.01731 | 0.9913 |
33 | 0.006065 | 0.01213 | 0.9939 |
34 | 0.009647 | 0.01929 | 0.9904 |
35 | 0.006218 | 0.01244 | 0.9938 |
36 | 0.004732 | 0.009464 | 0.9953 |
37 | 0.002988 | 0.005976 | 0.997 |
38 | 0.001911 | 0.003821 | 0.9981 |
39 | 0.007366 | 0.01473 | 0.9926 |
40 | 0.00607 | 0.01214 | 0.9939 |
41 | 0.003947 | 0.007894 | 0.9961 |
42 | 0.003949 | 0.007898 | 0.9961 |
43 | 0.003409 | 0.006818 | 0.9966 |
44 | 0.002311 | 0.004622 | 0.9977 |
45 | 0.001591 | 0.003182 | 0.9984 |
46 | 0.001004 | 0.002008 | 0.999 |
47 | 0.0008791 | 0.001758 | 0.9991 |
48 | 0.005786 | 0.01157 | 0.9942 |
49 | 0.004057 | 0.008115 | 0.9959 |
50 | 0.002764 | 0.005528 | 0.9972 |
51 | 0.001825 | 0.003651 | 0.9982 |
52 | 0.01498 | 0.02996 | 0.985 |
53 | 0.01081 | 0.02161 | 0.9892 |
54 | 0.03283 | 0.06567 | 0.9672 |
55 | 0.0258 | 0.0516 | 0.9742 |
56 | 0.03363 | 0.06727 | 0.9664 |
57 | 0.02897 | 0.05795 | 0.971 |
58 | 0.02361 | 0.04722 | 0.9764 |
59 | 0.01786 | 0.03573 | 0.9821 |
60 | 0.01618 | 0.03237 | 0.9838 |
61 | 0.01203 | 0.02407 | 0.988 |
62 | 0.01087 | 0.02174 | 0.9891 |
63 | 0.008746 | 0.01749 | 0.9913 |
64 | 0.008582 | 0.01716 | 0.9914 |
65 | 0.00994 | 0.01988 | 0.9901 |
66 | 0.01101 | 0.02202 | 0.989 |
67 | 0.01212 | 0.02424 | 0.9879 |
68 | 0.01006 | 0.02013 | 0.9899 |
69 | 0.007615 | 0.01523 | 0.9924 |
70 | 0.01336 | 0.02672 | 0.9866 |
71 | 0.02977 | 0.05954 | 0.9702 |
72 | 0.05237 | 0.1047 | 0.9476 |
73 | 0.04968 | 0.09937 | 0.9503 |
74 | 0.04019 | 0.08039 | 0.9598 |
75 | 0.03175 | 0.06351 | 0.9682 |
76 | 0.03034 | 0.06068 | 0.9697 |
77 | 0.0265 | 0.053 | 0.9735 |
78 | 0.05206 | 0.1041 | 0.9479 |
79 | 0.04176 | 0.08351 | 0.9582 |
80 | 0.05906 | 0.1181 | 0.9409 |
81 | 0.04741 | 0.09483 | 0.9526 |
82 | 0.04879 | 0.09759 | 0.9512 |
83 | 0.04674 | 0.09347 | 0.9533 |
84 | 0.1977 | 0.3954 | 0.8023 |
85 | 0.1727 | 0.3454 | 0.8273 |
86 | 0.2156 | 0.4311 | 0.7844 |
87 | 0.1869 | 0.3738 | 0.8131 |
88 | 0.2181 | 0.4362 | 0.7819 |
89 | 0.1983 | 0.3965 | 0.8017 |
90 | 0.1704 | 0.3407 | 0.8296 |
91 | 0.1699 | 0.3399 | 0.8301 |
92 | 0.1427 | 0.2853 | 0.8573 |
93 | 0.1191 | 0.2382 | 0.8809 |
94 | 0.09843 | 0.1969 | 0.9016 |
95 | 0.0814 | 0.1628 | 0.9186 |
96 | 0.09435 | 0.1887 | 0.9056 |
97 | 0.09828 | 0.1966 | 0.9017 |
98 | 0.0888 | 0.1776 | 0.9112 |
99 | 0.1 | 0.2 | 0.9 |
100 | 0.08478 | 0.1696 | 0.9152 |
101 | 0.1817 | 0.3634 | 0.8183 |
102 | 0.1552 | 0.3104 | 0.8448 |
103 | 0.1297 | 0.2594 | 0.8703 |
104 | 0.1155 | 0.2311 | 0.8845 |
105 | 0.09923 | 0.1985 | 0.9008 |
106 | 0.08344 | 0.1669 | 0.9166 |
107 | 0.06834 | 0.1367 | 0.9317 |
108 | 0.05873 | 0.1175 | 0.9413 |
109 | 0.09857 | 0.1971 | 0.9014 |
110 | 0.07926 | 0.1585 | 0.9207 |
111 | 0.09083 | 0.1817 | 0.9092 |
112 | 0.273 | 0.546 | 0.727 |
113 | 0.2663 | 0.5325 | 0.7337 |
114 | 0.248 | 0.4959 | 0.752 |
115 | 0.2116 | 0.4231 | 0.7884 |
116 | 0.1862 | 0.3725 | 0.8138 |
117 | 0.1886 | 0.3771 | 0.8114 |
118 | 0.1564 | 0.3127 | 0.8436 |
119 | 0.1333 | 0.2665 | 0.8668 |
120 | 0.1104 | 0.2208 | 0.8896 |
121 | 0.0962 | 0.1924 | 0.9038 |
122 | 0.08068 | 0.1614 | 0.9193 |
123 | 0.06675 | 0.1335 | 0.9332 |
124 | 0.05934 | 0.1187 | 0.9407 |
125 | 0.04818 | 0.09635 | 0.9518 |
126 | 0.04043 | 0.08085 | 0.9596 |
127 | 0.03002 | 0.06003 | 0.97 |
128 | 0.02547 | 0.05094 | 0.9745 |
129 | 0.01923 | 0.03845 | 0.9808 |
130 | 0.02249 | 0.04497 | 0.9775 |
131 | 0.01945 | 0.0389 | 0.9805 |
132 | 0.04421 | 0.08842 | 0.9558 |
133 | 0.04333 | 0.08665 | 0.9567 |
134 | 0.033 | 0.06599 | 0.967 |
135 | 0.02782 | 0.05563 | 0.9722 |
136 | 0.02283 | 0.04566 | 0.9772 |
137 | 0.01937 | 0.03874 | 0.9806 |
138 | 0.02417 | 0.04835 | 0.9758 |
139 | 0.02025 | 0.04049 | 0.9798 |
140 | 0.0151 | 0.0302 | 0.9849 |
141 | 0.02412 | 0.04825 | 0.9759 |
142 | 0.0233 | 0.04659 | 0.9767 |
143 | 0.02272 | 0.04543 | 0.9773 |
144 | 0.1141 | 0.2282 | 0.8859 |
145 | 0.1063 | 0.2126 | 0.8937 |
146 | 0.08278 | 0.1656 | 0.9172 |
147 | 0.0982 | 0.1964 | 0.9018 |
148 | 0.1158 | 0.2316 | 0.8842 |
149 | 0.1891 | 0.3781 | 0.8109 |
150 | 0.22 | 0.4401 | 0.78 |
151 | 0.4395 | 0.8789 | 0.5605 |
152 | 0.3399 | 0.6797 | 0.6601 |
153 | 0.2616 | 0.5232 | 0.7384 |
154 | 0.5667 | 0.8666 | 0.4333 |
155 | 0.6792 | 0.6416 | 0.3208 |
156 | 0.5206 | 0.9587 | 0.4794 |
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity | |||
Description | # significant tests | % significant tests | OK/NOK |
1% type I error level | 16 | 0.1081 | NOK |
5% type I error level | 65 | 0.439189 | NOK |
10% type I error level | 89 | 0.601351 | NOK |
Ramsey RESET F-Test for powers (2 and 3) of fitted values |
> reset_test_fitted RESET test data: mylm RESET = 2.0596, df1 = 2, df2 = 157, p-value = 0.1309 |
Ramsey RESET F-Test for powers (2 and 3) of regressors |
> reset_test_regressors RESET test data: mylm RESET = 1.3373, df1 = 10, df2 = 149, p-value = 0.2156 |
Ramsey RESET F-Test for powers (2 and 3) of principal components |
> reset_test_principal_components RESET test data: mylm RESET = 0.57315, df1 = 2, df2 = 157, p-value = 0.5649 |
Variance Inflation Factors (Multicollinearity) |
> vif SK1 SK2 SK4 SK5 ALG2 1.098982 1.121640 1.097277 1.028088 1.046235 |