Multiple Linear Regression - Estimated Regression Equation |
Relative_Advantage[t] = -1.16175 + 0.101195Perceived_Usefulness[t] + 0.121055Perceived_Ease_of_Use[t] + 0.121858Information_Quality[t] + 0.0616975System_Quality[t] + 1.3588groupB[t] -0.124953genderB[t] + e[t] |
Warning: you did not specify the column number of the endogenous series! The first column was selected by default. |
Multiple Linear Regression - Ordinary Least Squares | |||||
Variable | Parameter | S.D. | T-STAT H0: parameter = 0 | 2-tail p-value | 1-tail p-value |
(Intercept) | -1.162 | 0.9863 | -1.1780e+00 | 0.2405 | 0.1202 |
Perceived_Usefulness | +0.1012 | 0.07449 | +1.3590e+00 | 0.1761 | 0.08803 |
Perceived_Ease_of_Use | +0.1211 | 0.06728 | +1.7990e+00 | 0.07372 | 0.03686 |
Information_Quality | +0.1219 | 0.07484 | +1.6280e+00 | 0.1053 | 0.05265 |
System_Quality | +0.0617 | 0.03625 | +1.7020e+00 | 0.09053 | 0.04527 |
groupB | +1.359 | 0.2962 | +4.5870e+00 | 8.613e-06 | 4.307e-06 |
genderB | -0.125 | 0.26 | -4.8050e-01 | 0.6314 | 0.3157 |
Multiple Linear Regression - Regression Statistics | |
Multiple R | 0.6129 |
R-squared | 0.3756 |
Adjusted R-squared | 0.3538 |
F-TEST (value) | 17.24 |
F-TEST (DF numerator) | 6 |
F-TEST (DF denominator) | 172 |
p-value | 1.443e-15 |
Multiple Linear Regression - Residual Statistics | |
Residual Standard Deviation | 1.673 |
Sum Squared Residuals | 481.5 |
Menu of Residual Diagnostics | |
Description | Link |
Histogram | Compute |
Central Tendency | Compute |
QQ Plot | Compute |
Kernel Density Plot | Compute |
Skewness/Kurtosis Test | Compute |
Skewness-Kurtosis Plot | Compute |
Harrell-Davis Plot | Compute |
Bootstrap Plot -- Central Tendency | Compute |
Blocked Bootstrap Plot -- Central Tendency | Compute |
(Partial) Autocorrelation Plot | Compute |
Spectral Analysis | Compute |
Tukey lambda PPCC Plot | Compute |
Box-Cox Normality Plot | Compute |
Summary Statistics | Compute |
Multiple Linear Regression - Actuals, Interpolation, and Residuals | |||
Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error |
1 | 10 | 7.2 | 2.8 |
2 | 8 | 7.454 | 0.5461 |
3 | 6 | 7.089 | -1.089 |
4 | 10 | 8.149 | 1.851 |
5 | 8 | 6.186 | 1.814 |
6 | 10 | 8.897 | 1.103 |
7 | 7 | 8.125 | -1.125 |
8 | 10 | 8.284 | 1.716 |
9 | 6 | 5.206 | 0.7939 |
10 | 7 | 8.071 | -1.071 |
11 | 9 | 7.622 | 1.378 |
12 | 6 | 6.793 | -0.793 |
13 | 7 | 7.213 | -0.213 |
14 | 6 | 5.674 | 0.3256 |
15 | 4 | 7.072 | -3.072 |
16 | 6 | 7.659 | -1.659 |
17 | 8 | 8.978 | -0.9779 |
18 | 9 | 6.223 | 2.777 |
19 | 8 | 7.34 | 0.6597 |
20 | 6 | 6.4 | -0.3998 |
21 | 6 | 7.945 | -1.945 |
22 | 10 | 7.361 | 2.639 |
23 | 8 | 7.17 | 0.8304 |
24 | 8 | 7.761 | 0.2395 |
25 | 7 | 8.98 | -1.98 |
26 | 4 | 7.149 | -3.149 |
27 | 9 | 7.477 | 1.523 |
28 | 8 | 6.477 | 1.523 |
29 | 10 | 6.924 | 3.076 |
30 | 8 | 6.317 | 1.683 |
31 | 6 | 6.041 | -0.04114 |
32 | 7 | 8.426 | -1.426 |
33 | 8 | 7.397 | 0.6035 |
34 | 5 | 7.028 | -2.028 |
35 | 10 | 7.639 | 2.361 |
36 | 2 | 6.849 | -4.849 |
37 | 6 | 6.296 | -0.2963 |
38 | 7 | 7.273 | -0.2734 |
39 | 5 | 7.418 | -2.418 |
40 | 8 | 8.735 | -0.7353 |
41 | 7 | 7.758 | -0.7582 |
42 | 7 | 6.281 | 0.7194 |
43 | 10 | 6.174 | 3.826 |
44 | 7 | 6.249 | 0.7511 |
45 | 6 | 6.686 | -0.6862 |
46 | 10 | 7.216 | 2.784 |
47 | 6 | 6.353 | -0.3534 |
48 | 5 | 6.196 | -1.196 |
49 | 8 | 6.484 | 1.516 |
50 | 8 | 7.312 | 0.6879 |
51 | 5 | 6.037 | -1.037 |
52 | 8 | 7.768 | 0.2323 |
53 | 10 | 9.045 | 0.9547 |
54 | 7 | 6.746 | 0.2537 |
55 | 7 | 6.909 | 0.09061 |
56 | 7 | 7.337 | -0.3375 |
57 | 7 | 7.147 | -0.1466 |
58 | 2 | 4.146 | -2.146 |
59 | 4 | 6.643 | -2.643 |
60 | 6 | 7.699 | -1.699 |
61 | 7 | 6.911 | 0.08869 |
62 | 9 | 6.051 | 2.949 |
63 | 9 | 6.259 | 2.741 |
64 | 4 | 6.783 | -2.783 |
65 | 9 | 6.886 | 2.114 |
66 | 9 | 7.498 | 1.502 |
67 | 8 | 7.072 | 0.9285 |
68 | 7 | 6.749 | 0.2514 |
69 | 9 | 7.073 | 1.927 |
70 | 7 | 7.637 | -0.6372 |
71 | 6 | 9.004 | -3.004 |
72 | 7 | 5.468 | 1.532 |
73 | 2 | 2.46 | -0.4603 |
74 | 3 | 3.435 | -0.4351 |
75 | 4 | 3.759 | 0.2412 |
76 | 5 | 5.44 | -0.4399 |
77 | 2 | 3.663 | -1.663 |
78 | 6 | 4.454 | 1.546 |
79 | 8 | 7.946 | 0.05434 |
80 | 5 | 7.764 | -2.764 |
81 | 4 | 5.552 | -1.552 |
82 | 10 | 8.002 | 1.998 |
83 | 10 | 8.973 | 1.027 |
84 | 10 | 9.035 | 0.965 |
85 | 9 | 8.166 | 0.8336 |
86 | 5 | 6.784 | -1.784 |
87 | 5 | 6.355 | -1.355 |
88 | 7 | 6.909 | 0.09056 |
89 | 10 | 8.733 | 1.267 |
90 | 9 | 7.663 | 1.337 |
91 | 8 | 6.991 | 1.009 |
92 | 8 | 5.02 | 2.98 |
93 | 8 | 8.02 | -0.01989 |
94 | 8 | 6.624 | 1.376 |
95 | 8 | 6.379 | 1.621 |
96 | 7 | 7.38 | -0.38 |
97 | 6 | 5.859 | 0.1406 |
98 | 8 | 6.912 | 1.088 |
99 | 2 | 6.14 | -4.14 |
100 | 5 | 6.362 | -1.362 |
101 | 4 | 7.502 | -3.502 |
102 | 9 | 7.174 | 1.826 |
103 | 10 | 8.998 | 1.002 |
104 | 6 | 7.705 | -1.705 |
105 | 4 | 5.55 | -1.55 |
106 | 10 | 8.473 | 1.528 |
107 | 6 | 7.119 | -1.119 |
108 | 7 | 6.217 | 0.7826 |
109 | 7 | 6.606 | 0.3943 |
110 | 8 | 6.97 | 1.03 |
111 | 6 | 7.989 | -1.988 |
112 | 5 | 7.391 | -2.391 |
113 | 6 | 8.495 | -2.495 |
114 | 7 | 5.565 | 1.435 |
115 | 6 | 6.237 | -0.2369 |
116 | 9 | 7.457 | 1.543 |
117 | 9 | 8.246 | 0.7539 |
118 | 7 | 8.13 | -1.13 |
119 | 6 | 5.566 | 0.4344 |
120 | 7 | 6.502 | 0.498 |
121 | 7 | 7.416 | -0.4157 |
122 | 8 | 6.748 | 1.252 |
123 | 7 | 6.462 | 0.5384 |
124 | 8 | 7.636 | 0.3636 |
125 | 7 | 7.526 | -0.5257 |
126 | 4 | 5.63 | -1.63 |
127 | 10 | 9.349 | 0.6511 |
128 | 8 | 7.395 | 0.6054 |
129 | 8 | 6.725 | 1.275 |
130 | 2 | 5.667 | -3.667 |
131 | 6 | 7.095 | -1.095 |
132 | 4 | 6.015 | -2.015 |
133 | 4 | 4.129 | -0.129 |
134 | 9 | 7.195 | 1.805 |
135 | 2 | 4.939 | -2.939 |
136 | 6 | 5.733 | 0.2666 |
137 | 7 | 5.917 | 1.083 |
138 | 4 | 4.066 | -0.06576 |
139 | 10 | 8.09 | 1.91 |
140 | 3 | 4.52 | -1.52 |
141 | 7 | 6.279 | 0.7209 |
142 | 4 | 5.302 | -1.302 |
143 | 8 | 6.873 | 1.127 |
144 | 4 | 4.652 | -0.6518 |
145 | 5 | 4.127 | 0.8733 |
146 | 6 | 6.546 | -0.546 |
147 | 5 | 5.386 | -0.3862 |
148 | 9 | 6.26 | 2.74 |
149 | 6 | 6.247 | -0.2466 |
150 | 8 | 5.506 | 2.494 |
151 | 4 | 6.788 | -2.788 |
152 | 4 | 4.471 | -0.4705 |
153 | 8 | 6.281 | 1.719 |
154 | 4 | 2.811 | 1.189 |
155 | 10 | 7.438 | 2.562 |
156 | 8 | 7.556 | 0.4437 |
157 | 5 | 6.465 | -1.465 |
158 | 3 | 5.057 | -2.057 |
159 | 7 | 5.689 | 1.311 |
160 | 6 | 7.388 | -1.388 |
161 | 5 | 6.423 | -1.423 |
162 | 5 | 5.771 | -0.7705 |
163 | 9 | 8.796 | 0.2041 |
164 | 2 | 5.737 | -3.737 |
165 | 7 | 4.584 | 2.416 |
166 | 7 | 6.541 | 0.4592 |
167 | 5 | 6.952 | -1.952 |
168 | 9 | 9.468 | -0.4677 |
169 | 4 | 6.547 | -2.547 |
170 | 5 | 4.47 | 0.5303 |
171 | 9 | 6.4 | 2.6 |
172 | 7 | 7.049 | -0.04852 |
173 | 6 | 7.318 | -1.318 |
174 | 8 | 6.908 | 1.092 |
175 | 7 | 5.5 | 1.5 |
176 | 6 | 7.217 | -1.217 |
177 | 8 | 8.351 | -0.3507 |
178 | 6 | 6.709 | -0.7095 |
179 | 7 | 6.339 | 0.6607 |
Goldfeld-Quandt test for Heteroskedasticity | |||
p-values | Alternative Hypothesis | ||
breakpoint index | greater | 2-sided | less |
10 | 0.7277 | 0.5446 | 0.2723 |
11 | 0.6656 | 0.6688 | 0.3344 |
12 | 0.5828 | 0.8345 | 0.4172 |
13 | 0.4583 | 0.9165 | 0.5417 |
14 | 0.3411 | 0.6823 | 0.6589 |
15 | 0.3701 | 0.7402 | 0.6299 |
16 | 0.318 | 0.6359 | 0.682 |
17 | 0.2514 | 0.5028 | 0.7486 |
18 | 0.5351 | 0.9298 | 0.4649 |
19 | 0.4689 | 0.9378 | 0.5311 |
20 | 0.4103 | 0.8206 | 0.5897 |
21 | 0.3795 | 0.759 | 0.6205 |
22 | 0.365 | 0.7301 | 0.635 |
23 | 0.2997 | 0.5993 | 0.7003 |
24 | 0.2364 | 0.4728 | 0.7636 |
25 | 0.1956 | 0.3913 | 0.8044 |
26 | 0.3014 | 0.6028 | 0.6986 |
27 | 0.3324 | 0.6648 | 0.6676 |
28 | 0.3357 | 0.6715 | 0.6643 |
29 | 0.4344 | 0.8688 | 0.5656 |
30 | 0.4422 | 0.8843 | 0.5578 |
31 | 0.3795 | 0.7591 | 0.6205 |
32 | 0.3835 | 0.7669 | 0.6165 |
33 | 0.331 | 0.662 | 0.669 |
34 | 0.4267 | 0.8533 | 0.5733 |
35 | 0.4258 | 0.8517 | 0.5742 |
36 | 0.8359 | 0.3281 | 0.1641 |
37 | 0.7996 | 0.4007 | 0.2004 |
38 | 0.7587 | 0.4827 | 0.2413 |
39 | 0.7863 | 0.4275 | 0.2137 |
40 | 0.7492 | 0.5017 | 0.2508 |
41 | 0.7149 | 0.5702 | 0.2851 |
42 | 0.6823 | 0.6355 | 0.3177 |
43 | 0.7795 | 0.4411 | 0.2205 |
44 | 0.76 | 0.4799 | 0.24 |
45 | 0.7463 | 0.5074 | 0.2537 |
46 | 0.8422 | 0.3157 | 0.1578 |
47 | 0.8146 | 0.3707 | 0.1853 |
48 | 0.8181 | 0.3639 | 0.1819 |
49 | 0.8032 | 0.3937 | 0.1968 |
50 | 0.7878 | 0.4244 | 0.2122 |
51 | 0.8205 | 0.359 | 0.1795 |
52 | 0.7939 | 0.4121 | 0.2061 |
53 | 0.7758 | 0.4485 | 0.2242 |
54 | 0.7383 | 0.5235 | 0.2617 |
55 | 0.6984 | 0.6033 | 0.3016 |
56 | 0.6694 | 0.6611 | 0.3306 |
57 | 0.6251 | 0.7498 | 0.3749 |
58 | 0.667 | 0.6661 | 0.333 |
59 | 0.7265 | 0.5471 | 0.2735 |
60 | 0.7304 | 0.5392 | 0.2696 |
61 | 0.6979 | 0.6041 | 0.3021 |
62 | 0.7684 | 0.4632 | 0.2316 |
63 | 0.8258 | 0.3485 | 0.1742 |
64 | 0.8702 | 0.2596 | 0.1298 |
65 | 0.88 | 0.24 | 0.12 |
66 | 0.8727 | 0.2546 | 0.1273 |
67 | 0.8542 | 0.2916 | 0.1458 |
68 | 0.8286 | 0.3427 | 0.1714 |
69 | 0.8339 | 0.3322 | 0.1661 |
70 | 0.8093 | 0.3813 | 0.1907 |
71 | 0.8714 | 0.2573 | 0.1286 |
72 | 0.8622 | 0.2756 | 0.1378 |
73 | 0.842 | 0.316 | 0.158 |
74 | 0.8201 | 0.3599 | 0.1799 |
75 | 0.791 | 0.418 | 0.209 |
76 | 0.7594 | 0.4811 | 0.2406 |
77 | 0.7607 | 0.4786 | 0.2393 |
78 | 0.7574 | 0.4852 | 0.2426 |
79 | 0.7281 | 0.5439 | 0.2719 |
80 | 0.8048 | 0.3904 | 0.1952 |
81 | 0.8091 | 0.3818 | 0.1909 |
82 | 0.8248 | 0.3505 | 0.1752 |
83 | 0.8081 | 0.3838 | 0.1919 |
84 | 0.7885 | 0.423 | 0.2115 |
85 | 0.7641 | 0.4717 | 0.2359 |
86 | 0.7683 | 0.4633 | 0.2317 |
87 | 0.7573 | 0.4855 | 0.2427 |
88 | 0.7217 | 0.5566 | 0.2783 |
89 | 0.7063 | 0.5875 | 0.2937 |
90 | 0.6965 | 0.607 | 0.3035 |
91 | 0.6738 | 0.6525 | 0.3262 |
92 | 0.7612 | 0.4776 | 0.2388 |
93 | 0.7388 | 0.5225 | 0.2612 |
94 | 0.7259 | 0.5481 | 0.2741 |
95 | 0.7438 | 0.5124 | 0.2562 |
96 | 0.7091 | 0.5819 | 0.2909 |
97 | 0.6832 | 0.6336 | 0.3168 |
98 | 0.6592 | 0.6816 | 0.3408 |
99 | 0.8372 | 0.3256 | 0.1628 |
100 | 0.8245 | 0.351 | 0.1755 |
101 | 0.9072 | 0.1856 | 0.09281 |
102 | 0.9226 | 0.1549 | 0.07745 |
103 | 0.9136 | 0.1727 | 0.08637 |
104 | 0.9124 | 0.1753 | 0.08764 |
105 | 0.9114 | 0.1772 | 0.08862 |
106 | 0.9115 | 0.1769 | 0.08846 |
107 | 0.8989 | 0.2021 | 0.1011 |
108 | 0.8822 | 0.2357 | 0.1178 |
109 | 0.8592 | 0.2817 | 0.1408 |
110 | 0.8415 | 0.317 | 0.1585 |
111 | 0.8453 | 0.3095 | 0.1547 |
112 | 0.8698 | 0.2604 | 0.1302 |
113 | 0.8833 | 0.2334 | 0.1167 |
114 | 0.8749 | 0.2502 | 0.1251 |
115 | 0.8495 | 0.3011 | 0.1505 |
116 | 0.8498 | 0.3003 | 0.1502 |
117 | 0.8269 | 0.3462 | 0.1731 |
118 | 0.8031 | 0.3938 | 0.1969 |
119 | 0.7743 | 0.4515 | 0.2257 |
120 | 0.74 | 0.52 | 0.26 |
121 | 0.7009 | 0.5982 | 0.2991 |
122 | 0.7084 | 0.5833 | 0.2916 |
123 | 0.7015 | 0.597 | 0.2985 |
124 | 0.6648 | 0.6703 | 0.3352 |
125 | 0.6207 | 0.7587 | 0.3793 |
126 | 0.6002 | 0.7996 | 0.3998 |
127 | 0.5564 | 0.8872 | 0.4436 |
128 | 0.5142 | 0.9716 | 0.4858 |
129 | 0.495 | 0.99 | 0.505 |
130 | 0.7037 | 0.5926 | 0.2963 |
131 | 0.665 | 0.6701 | 0.335 |
132 | 0.6648 | 0.6703 | 0.3352 |
133 | 0.6158 | 0.7683 | 0.3842 |
134 | 0.6478 | 0.7044 | 0.3522 |
135 | 0.7789 | 0.4421 | 0.2211 |
136 | 0.7382 | 0.5237 | 0.2618 |
137 | 0.7145 | 0.5709 | 0.2855 |
138 | 0.6667 | 0.6667 | 0.3333 |
139 | 0.6889 | 0.6223 | 0.3111 |
140 | 0.652 | 0.696 | 0.348 |
141 | 0.6032 | 0.7937 | 0.3968 |
142 | 0.6698 | 0.6603 | 0.3301 |
143 | 0.624 | 0.752 | 0.376 |
144 | 0.5682 | 0.8635 | 0.4318 |
145 | 0.5146 | 0.9709 | 0.4854 |
146 | 0.486 | 0.9719 | 0.514 |
147 | 0.4327 | 0.8654 | 0.5673 |
148 | 0.5361 | 0.9277 | 0.4639 |
149 | 0.4736 | 0.9472 | 0.5264 |
150 | 0.596 | 0.808 | 0.404 |
151 | 0.619 | 0.7621 | 0.381 |
152 | 0.5546 | 0.8909 | 0.4454 |
153 | 0.5506 | 0.8988 | 0.4494 |
154 | 0.5035 | 0.9931 | 0.4965 |
155 | 0.5884 | 0.8232 | 0.4116 |
156 | 0.5634 | 0.8733 | 0.4366 |
157 | 0.4992 | 0.9984 | 0.5008 |
158 | 0.6504 | 0.6992 | 0.3496 |
159 | 0.6466 | 0.7069 | 0.3534 |
160 | 0.6033 | 0.7934 | 0.3967 |
161 | 0.6368 | 0.7263 | 0.3632 |
162 | 0.5988 | 0.8024 | 0.4012 |
163 | 0.5017 | 0.9965 | 0.4983 |
164 | 0.9203 | 0.1595 | 0.07973 |
165 | 0.9436 | 0.1128 | 0.05638 |
166 | 0.8974 | 0.2052 | 0.1026 |
167 | 0.847 | 0.3059 | 0.153 |
168 | 0.7982 | 0.4037 | 0.2018 |
169 | 0.9909 | 0.0183 | 0.009149 |
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity | |||
Description | # significant tests | % significant tests | OK/NOK |
1% type I error level | 0 | 0 | OK |
5% type I error level | 1 | 0.00625 | OK |
10% type I error level | 1 | 0.00625 | OK |
Ramsey RESET F-Test for powers (2 and 3) of fitted values |
> reset_test_fitted RESET test data: mylm RESET = 0.08381, df1 = 2, df2 = 170, p-value = 0.9196 |
Ramsey RESET F-Test for powers (2 and 3) of regressors |
> reset_test_regressors RESET test data: mylm RESET = 0.59736, df1 = 12, df2 = 160, p-value = 0.8421 |
Ramsey RESET F-Test for powers (2 and 3) of principal components |
> reset_test_principal_components RESET test data: mylm RESET = 0.65921, df1 = 2, df2 = 170, p-value = 0.5186 |
Variance Inflation Factors (Multicollinearity) |
> vif Perceived_Usefulness Perceived_Ease_of_Use Information_Quality 1.844573 2.364299 2.683707 System_Quality groupB genderB 1.764786 1.115235 1.080453 |