Table 6 Least squares linear regressions run for all values of \(I_i\) on \(B_i\), baseline and modified values considered. \(\beta\) increases as more dimensions are considered
From: The diffusion of goods with multiple characteristics and price premiums: an agent-based model
Estimate | Std. error | t value | Pr(\(>|\)t|) | |
|---|---|---|---|---|
(a) purchases1D \(\sim\) intention1D | ||||
y-intercept | 61.1155 | 0.0642 | 951.45 | 0.0000 |
\(\beta\) | 0.3504 | 0.0009 | 398.02 | 0.0000 |
(b) purchases2D \(\sim\) intention2D | ||||
y-intercept | 44.0634 | 0.0680 | 648.45 | 0.0000 |
\(\beta\) | 0.4823 | 0.0011 | 423.06 | 0.0000 |
(c) purchases3D \(\sim\) intention3D | ||||
y-intercept | 28.4439 | 0.0671 | 423.98 | 0.0000 |
\(\beta\) | 0.6630 | 0.0016 | 423.69 | 0.0000 |
(d) purchases4D \(\sim\) intention4D | ||||
y-intercept | 15.8966 | 0.0686 | 231.62 | 0.0000 |
\(\beta\) | 0.8887 | 0.0026 | 346.55 | 0.0000 |