Multiple Linear Regression - Estimated Regression Equation |
Intention_to_Use[t] = -1.02844 + 0.328297Relative_Advantage[t] + 0.0917069Perceived_Usefulness[t] + 0.103309Perceived_Ease_of_Use[t] + 0.000830561Information_Quality[t] + 0.0876004System_Quality[t] + 0.895018groupB[t] + 0.188132genderB[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) | -1.028 | 0.7824 | -1.3140e+00 | 0.1905 | 0.09523 |
Relative_Advantage | +0.3283 | 0.06025 | +5.4490e+00 | 1.74e-07 | 8.701e-08 |
Perceived_Usefulness | +0.09171 | 0.05917 | +1.5500e+00 | 0.123 | 0.0615 |
Perceived_Ease_of_Use | +0.1033 | 0.05366 | +1.9250e+00 | 0.05584 | 0.02792 |
Information_Quality | +0.0008306 | 0.05958 | +1.3940e-02 | 0.9889 | 0.4944 |
System_Quality | +0.0876 | 0.02888 | +3.0330e+00 | 0.002796 | 0.001398 |
groupB | +0.895 | 0.2479 | +3.6100e+00 | 0.0004021 | 0.0002011 |
genderB | +0.1881 | 0.2056 | +9.1510e-01 | 0.3614 | 0.1807 |
Multiple Linear Regression - Regression Statistics | |
Multiple R | 0.7512 |
R-squared | 0.5644 |
Adjusted R-squared | 0.5465 |
F-TEST (value) | 31.65 |
F-TEST (DF numerator) | 7 |
F-TEST (DF denominator) | 171 |
p-value | 0 |
Multiple Linear Regression - Residual Statistics | |
Residual Standard Deviation | 1.322 |
Sum Squared Residuals | 298.9 |
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 | 8.271 | 1.729 |
2 | 8 | 7.878 | 0.1224 |
3 | 8 | 7.476 | 0.5238 |
4 | 9 | 9.502 | -0.5018 |
5 | 5 | 6.864 | -1.864 |
6 | 10 | 9.928 | 0.07174 |
7 | 8 | 8.381 | -0.3806 |
8 | 9 | 9.299 | -0.2985 |
9 | 8 | 6.057 | 1.943 |
10 | 7 | 8.248 | -1.248 |
11 | 10 | 8.647 | 1.353 |
12 | 10 | 7.156 | 2.844 |
13 | 9 | 7.877 | 1.123 |
14 | 4 | 6.325 | -2.325 |
15 | 4 | 6.899 | -2.899 |
16 | 8 | 7.753 | 0.2469 |
17 | 9 | 9.731 | -0.7311 |
18 | 10 | 8.022 | 1.978 |
19 | 8 | 8.089 | -0.08931 |
20 | 5 | 6.649 | -1.649 |
21 | 10 | 8.21 | 1.79 |
22 | 8 | 8.655 | -0.655 |
23 | 7 | 7.955 | -0.9552 |
24 | 8 | 8.501 | -0.5014 |
25 | 8 | 9.475 | -1.475 |
26 | 9 | 6.574 | 2.426 |
27 | 8 | 8.389 | -0.3887 |
28 | 6 | 7.42 | -1.42 |
29 | 8 | 8.436 | -0.4357 |
30 | 8 | 7.412 | 0.5883 |
31 | 5 | 6.731 | -1.731 |
32 | 9 | 8.584 | 0.4162 |
33 | 8 | 8.136 | -0.1356 |
34 | 8 | 6.439 | 1.561 |
35 | 8 | 8.619 | -0.6195 |
36 | 6 | 5.87 | 0.1301 |
37 | 6 | 6.486 | -0.4857 |
38 | 9 | 7.81 | 1.19 |
39 | 8 | 7.495 | 0.5049 |
40 | 9 | 9.267 | -0.2667 |
41 | 10 | 8.101 | 1.899 |
42 | 8 | 7.016 | 0.9836 |
43 | 8 | 7.779 | 0.221 |
44 | 7 | 7.177 | -0.1769 |
45 | 7 | 7.162 | -0.1622 |
46 | 10 | 9.211 | 0.7891 |
47 | 8 | 6.588 | 1.412 |
48 | 7 | 6.482 | 0.5182 |
49 | 10 | 7.635 | 2.365 |
50 | 7 | 8.244 | -1.244 |
51 | 7 | 5.909 | 1.091 |
52 | 9 | 8.578 | 0.4222 |
53 | 9 | 9.998 | -0.998 |
54 | 8 | 7.245 | 0.7547 |
55 | 6 | 7.444 | -1.444 |
56 | 8 | 7.431 | 0.5686 |
57 | 9 | 7.646 | 1.354 |
58 | 2 | 3.343 | -1.343 |
59 | 6 | 6.097 | -0.09686 |
60 | 8 | 7.757 | 0.2428 |
61 | 8 | 7.744 | 0.2557 |
62 | 7 | 7.243 | -0.2432 |
63 | 8 | 7.538 | 0.4616 |
64 | 6 | 5.935 | 0.06508 |
65 | 10 | 7.74 | 2.26 |
66 | 10 | 8.195 | 1.805 |
67 | 10 | 7.675 | 2.325 |
68 | 8 | 7.317 | 0.6828 |
69 | 8 | 8.336 | -0.3356 |
70 | 7 | 7.998 | -0.9979 |
71 | 10 | 9.025 | 0.9751 |
72 | 5 | 6.173 | -1.173 |
73 | 3 | 3 | -0.0003373 |
74 | 2 | 3.715 | -1.715 |
75 | 3 | 4.349 | -1.349 |
76 | 4 | 5.652 | -1.652 |
77 | 2 | 3.483 | -1.483 |
78 | 6 | 5.075 | 0.925 |
79 | 8 | 8.227 | -0.2265 |
80 | 8 | 7.225 | 0.7749 |
81 | 5 | 5.34 | -0.3398 |
82 | 10 | 9.107 | 0.8931 |
83 | 9 | 9.865 | -0.8646 |
84 | 8 | 9.952 | -1.952 |
85 | 9 | 9.113 | -0.1132 |
86 | 8 | 6.975 | 1.025 |
87 | 5 | 6.257 | -1.257 |
88 | 7 | 7.602 | -0.6021 |
89 | 9 | 9.832 | -0.8323 |
90 | 8 | 8.447 | -0.4466 |
91 | 4 | 8.011 | -4.011 |
92 | 7 | 6.711 | 0.2885 |
93 | 8 | 9.008 | -1.008 |
94 | 7 | 7.573 | -0.5728 |
95 | 7 | 7.297 | -0.2974 |
96 | 9 | 7.784 | 1.216 |
97 | 6 | 6.727 | -0.7275 |
98 | 7 | 7.844 | -0.8439 |
99 | 4 | 5.209 | -1.209 |
100 | 6 | 6.65 | -0.6497 |
101 | 10 | 6.8 | 3.2 |
102 | 9 | 8.427 | 0.5727 |
103 | 10 | 10.02 | -0.01996 |
104 | 8 | 7.538 | 0.4623 |
105 | 4 | 5.287 | -1.287 |
106 | 8 | 9.808 | -1.808 |
107 | 5 | 7.154 | -2.154 |
108 | 8 | 7.292 | 0.7078 |
109 | 9 | 7.636 | 1.364 |
110 | 8 | 7.685 | 0.3147 |
111 | 4 | 8.1 | -4.1 |
112 | 8 | 6.73 | 1.27 |
113 | 10 | 8.199 | 1.801 |
114 | 6 | 6.43 | -0.4297 |
115 | 7 | 6.47 | 0.53 |
116 | 10 | 8.812 | 1.188 |
117 | 9 | 9.398 | -0.3982 |
118 | 8 | 8.422 | -0.4219 |
119 | 3 | 5.69 | -2.69 |
120 | 8 | 6.986 | 1.014 |
121 | 7 | 7.542 | -0.5421 |
122 | 7 | 7.369 | -0.3687 |
123 | 8 | 6.686 | 1.314 |
124 | 8 | 8.429 | -0.4287 |
125 | 7 | 7.664 | -0.6636 |
126 | 7 | 5.628 | 1.372 |
127 | 9 | 10.27 | -1.273 |
128 | 9 | 8.19 | 0.8102 |
129 | 9 | 7.426 | 1.574 |
130 | 4 | 5.025 | -1.025 |
131 | 6 | 6.999 | -0.9989 |
132 | 6 | 6.054 | -0.05362 |
133 | 6 | 4.337 | 1.663 |
134 | 8 | 8.178 | -0.178 |
135 | 3 | 4.092 | -1.092 |
136 | 8 | 6.032 | 1.968 |
137 | 8 | 7.366 | 0.6342 |
138 | 6 | 4.612 | 1.388 |
139 | 10 | 9.244 | 0.7557 |
140 | 2 | 4.321 | -2.321 |
141 | 9 | 7.38 | 1.62 |
142 | 6 | 5.577 | 0.4232 |
143 | 6 | 7.705 | -1.705 |
144 | 5 | 4.458 | 0.542 |
145 | 4 | 4.593 | -0.593 |
146 | 7 | 6.786 | 0.2136 |
147 | 5 | 5.697 | -0.6968 |
148 | 8 | 7.922 | 0.07773 |
149 | 6 | 6.725 | -0.7253 |
150 | 9 | 6.868 | 2.132 |
151 | 6 | 6.336 | -0.3365 |
152 | 4 | 4.979 | -0.9792 |
153 | 7 | 7.242 | -0.2422 |
154 | 2 | 3.83 | -1.83 |
155 | 8 | 9.148 | -1.148 |
156 | 9 | 8.504 | 0.4961 |
157 | 6 | 6.378 | -0.378 |
158 | 5 | 4.435 | 0.5646 |
159 | 7 | 6.731 | 0.269 |
160 | 8 | 7.247 | 0.7527 |
161 | 4 | 6.302 | -2.302 |
162 | 9 | 6.174 | 2.826 |
163 | 9 | 9.608 | -0.6076 |
164 | 9 | 5.226 | 3.774 |
165 | 7 | 5.916 | 1.084 |
166 | 5 | 7.251 | -2.251 |
167 | 7 | 6.709 | 0.2907 |
168 | 9 | 10.13 | -1.135 |
169 | 8 | 6.584 | 1.416 |
170 | 6 | 5.41 | 0.59 |
171 | 9 | 7.779 | 1.221 |
172 | 8 | 7.903 | 0.0971 |
173 | 7 | 7.906 | -0.906 |
174 | 7 | 7.598 | -0.5977 |
175 | 7 | 6.563 | 0.4372 |
176 | 8 | 7.258 | 0.7425 |
177 | 10 | 8.771 | 1.229 |
178 | 6 | 6.953 | -0.9526 |
179 | 6 | 6.755 | -0.7553 |
Goldfeld-Quandt test for Heteroskedasticity | |||
p-values | Alternative Hypothesis | ||
breakpoint index | greater | 2-sided | less |
11 | 0.7583 | 0.4835 | 0.2417 |
12 | 0.8911 | 0.2178 | 0.1089 |
13 | 0.8352 | 0.3296 | 0.1648 |
14 | 0.9764 | 0.04726 | 0.02363 |
15 | 0.9825 | 0.03509 | 0.01754 |
16 | 0.9777 | 0.04462 | 0.02231 |
17 | 0.9675 | 0.06494 | 0.03247 |
18 | 0.96 | 0.08007 | 0.04004 |
19 | 0.9421 | 0.1157 | 0.05787 |
20 | 0.94 | 0.12 | 0.06002 |
21 | 0.9557 | 0.08866 | 0.04433 |
22 | 0.9483 | 0.1033 | 0.05166 |
23 | 0.9328 | 0.1345 | 0.06725 |
24 | 0.9093 | 0.1813 | 0.09066 |
25 | 0.9142 | 0.1716 | 0.08581 |
26 | 0.972 | 0.05599 | 0.028 |
27 | 0.9629 | 0.07428 | 0.03714 |
28 | 0.9607 | 0.07859 | 0.03929 |
29 | 0.9465 | 0.107 | 0.05349 |
30 | 0.9282 | 0.1436 | 0.07182 |
31 | 0.9126 | 0.1749 | 0.08744 |
32 | 0.89 | 0.2199 | 0.11 |
33 | 0.8623 | 0.2755 | 0.1377 |
34 | 0.8742 | 0.2516 | 0.1258 |
35 | 0.8509 | 0.2981 | 0.1491 |
36 | 0.8195 | 0.3611 | 0.1805 |
37 | 0.7874 | 0.4252 | 0.2126 |
38 | 0.7858 | 0.4284 | 0.2142 |
39 | 0.7473 | 0.5055 | 0.2527 |
40 | 0.7015 | 0.597 | 0.2985 |
41 | 0.7472 | 0.5056 | 0.2528 |
42 | 0.7594 | 0.4813 | 0.2406 |
43 | 0.7169 | 0.5662 | 0.2831 |
44 | 0.6709 | 0.6583 | 0.3291 |
45 | 0.6225 | 0.7549 | 0.3775 |
46 | 0.5873 | 0.8253 | 0.4127 |
47 | 0.5873 | 0.8253 | 0.4127 |
48 | 0.5399 | 0.9203 | 0.4601 |
49 | 0.6316 | 0.7367 | 0.3684 |
50 | 0.6257 | 0.7487 | 0.3743 |
51 | 0.5942 | 0.8117 | 0.4058 |
52 | 0.5535 | 0.8931 | 0.4465 |
53 | 0.5196 | 0.9609 | 0.4804 |
54 | 0.4798 | 0.9596 | 0.5202 |
55 | 0.4889 | 0.9779 | 0.5111 |
56 | 0.4502 | 0.9003 | 0.5498 |
57 | 0.441 | 0.882 | 0.559 |
58 | 0.4306 | 0.8612 | 0.5694 |
59 | 0.3865 | 0.7729 | 0.6135 |
60 | 0.3423 | 0.6845 | 0.6577 |
61 | 0.3182 | 0.6363 | 0.6818 |
62 | 0.2772 | 0.5543 | 0.7228 |
63 | 0.2433 | 0.4866 | 0.7567 |
64 | 0.2078 | 0.4156 | 0.7922 |
65 | 0.2744 | 0.5488 | 0.7256 |
66 | 0.3091 | 0.6182 | 0.6909 |
67 | 0.3918 | 0.7836 | 0.6082 |
68 | 0.359 | 0.7181 | 0.641 |
69 | 0.3215 | 0.6431 | 0.6785 |
70 | 0.3072 | 0.6145 | 0.6928 |
71 | 0.2898 | 0.5796 | 0.7102 |
72 | 0.2713 | 0.5425 | 0.7287 |
73 | 0.2354 | 0.4709 | 0.7646 |
74 | 0.2426 | 0.4852 | 0.7574 |
75 | 0.2276 | 0.4552 | 0.7724 |
76 | 0.23 | 0.4599 | 0.77 |
77 | 0.2223 | 0.4447 | 0.7777 |
78 | 0.2193 | 0.4386 | 0.7807 |
79 | 0.1923 | 0.3845 | 0.8077 |
80 | 0.1715 | 0.343 | 0.8285 |
81 | 0.1453 | 0.2907 | 0.8547 |
82 | 0.1373 | 0.2747 | 0.8627 |
83 | 0.125 | 0.25 | 0.875 |
84 | 0.1544 | 0.3087 | 0.8456 |
85 | 0.1295 | 0.2591 | 0.8705 |
86 | 0.1219 | 0.2438 | 0.8781 |
87 | 0.1257 | 0.2514 | 0.8743 |
88 | 0.1086 | 0.2171 | 0.8914 |
89 | 0.09603 | 0.1921 | 0.904 |
90 | 0.08163 | 0.1633 | 0.9184 |
91 | 0.3212 | 0.6424 | 0.6788 |
92 | 0.285 | 0.5701 | 0.715 |
93 | 0.2675 | 0.5351 | 0.7325 |
94 | 0.2403 | 0.4807 | 0.7597 |
95 | 0.2088 | 0.4176 | 0.7912 |
96 | 0.208 | 0.4159 | 0.792 |
97 | 0.1872 | 0.3743 | 0.8128 |
98 | 0.1698 | 0.3396 | 0.8302 |
99 | 0.1668 | 0.3336 | 0.8332 |
100 | 0.1467 | 0.2934 | 0.8533 |
101 | 0.3209 | 0.6418 | 0.6791 |
102 | 0.2879 | 0.5759 | 0.7121 |
103 | 0.2511 | 0.5022 | 0.7489 |
104 | 0.2259 | 0.4519 | 0.7741 |
105 | 0.2146 | 0.4292 | 0.7854 |
106 | 0.2425 | 0.485 | 0.7575 |
107 | 0.2926 | 0.5851 | 0.7074 |
108 | 0.2794 | 0.5588 | 0.7206 |
109 | 0.29 | 0.58 | 0.71 |
110 | 0.257 | 0.5139 | 0.743 |
111 | 0.636 | 0.728 | 0.364 |
112 | 0.6422 | 0.7156 | 0.3578 |
113 | 0.6657 | 0.6685 | 0.3343 |
114 | 0.6259 | 0.7482 | 0.3741 |
115 | 0.5982 | 0.8036 | 0.4018 |
116 | 0.585 | 0.83 | 0.415 |
117 | 0.5434 | 0.9132 | 0.4566 |
118 | 0.5072 | 0.9856 | 0.4928 |
119 | 0.6489 | 0.7022 | 0.3511 |
120 | 0.6509 | 0.6982 | 0.3491 |
121 | 0.6101 | 0.7798 | 0.3899 |
122 | 0.5665 | 0.867 | 0.4335 |
123 | 0.5797 | 0.8405 | 0.4203 |
124 | 0.5379 | 0.9243 | 0.4621 |
125 | 0.4965 | 0.9929 | 0.5035 |
126 | 0.4908 | 0.9817 | 0.5092 |
127 | 0.4759 | 0.9518 | 0.5241 |
128 | 0.4425 | 0.8851 | 0.5575 |
129 | 0.5287 | 0.9426 | 0.4713 |
130 | 0.5406 | 0.9188 | 0.4594 |
131 | 0.5028 | 0.9944 | 0.4972 |
132 | 0.451 | 0.9021 | 0.549 |
133 | 0.5219 | 0.9561 | 0.4781 |
134 | 0.4817 | 0.9634 | 0.5183 |
135 | 0.4576 | 0.9152 | 0.5424 |
136 | 0.5436 | 0.9127 | 0.4564 |
137 | 0.497 | 0.9941 | 0.503 |
138 | 0.4923 | 0.9846 | 0.5077 |
139 | 0.5117 | 0.9766 | 0.4883 |
140 | 0.6708 | 0.6584 | 0.3292 |
141 | 0.6702 | 0.6596 | 0.3298 |
142 | 0.6179 | 0.7642 | 0.3821 |
143 | 0.6396 | 0.7207 | 0.3604 |
144 | 0.596 | 0.808 | 0.404 |
145 | 0.5393 | 0.9214 | 0.4607 |
146 | 0.5023 | 0.9954 | 0.4977 |
147 | 0.5232 | 0.9536 | 0.4768 |
148 | 0.4686 | 0.9373 | 0.5314 |
149 | 0.465 | 0.9301 | 0.535 |
150 | 0.5536 | 0.8929 | 0.4464 |
151 | 0.4869 | 0.9738 | 0.5131 |
152 | 0.4985 | 0.997 | 0.5015 |
153 | 0.4284 | 0.8567 | 0.5716 |
154 | 0.5979 | 0.8043 | 0.4021 |
155 | 0.5463 | 0.9073 | 0.4537 |
156 | 0.488 | 0.976 | 0.512 |
157 | 0.4123 | 0.8246 | 0.5877 |
158 | 0.3894 | 0.7789 | 0.6106 |
159 | 0.3286 | 0.6572 | 0.6714 |
160 | 0.2822 | 0.5644 | 0.7178 |
161 | 0.3539 | 0.7078 | 0.6461 |
162 | 0.4083 | 0.8167 | 0.5917 |
163 | 0.4903 | 0.9807 | 0.5097 |
164 | 0.5346 | 0.9307 | 0.4654 |
165 | 0.4199 | 0.8398 | 0.5801 |
166 | 0.9452 | 0.1097 | 0.05484 |
167 | 0.9247 | 0.1507 | 0.07535 |
168 | 0.8286 | 0.3427 | 0.1714 |
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 | 3 | 0.0189873 | OK |
10% type I error level | 9 | 0.056962 | OK |
Ramsey RESET F-Test for powers (2 and 3) of fitted values |
> reset_test_fitted RESET test data: mylm RESET = 6.1098, df1 = 2, df2 = 169, p-value = 0.002742 |
Ramsey RESET F-Test for powers (2 and 3) of regressors |
> reset_test_regressors RESET test data: mylm RESET = 0.75864, df1 = 14, df2 = 157, p-value = 0.7122 |
Ramsey RESET F-Test for powers (2 and 3) of principal components |
> reset_test_principal_components RESET test data: mylm RESET = 4.3565, df1 = 2, df2 = 169, p-value = 0.01429 |
Variance Inflation Factors (Multicollinearity) |
> vif Relative_Advantage Perceived_Usefulness Perceived_Ease_of_Use 1.601567 1.864367 2.408801 Information_Quality System_Quality groupB 2.725076 1.794514 1.251689 genderB 1.081904 |