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Table 2.
Estimation results of the logistic regression model.
| Variables | Coefficient | Sth Dv | P Value |
|---|---|---|---|
| Constant | −1,507 | 3,052 | ,621 |
| Age | ,042 | ,048 | ,378 |
| Education | ,060 | ,111 | ,589 |
| Num of family member | −,436 | ,242 | ,072*** |
| Agr. Experience | −,021 | ,041 | ,610 |
| Non Ag. Job | ,246 | ,742 | ,741 |
| Living place | 1,523 | ,638 | ,017** |
| Mem. of an org. | ,038 | ,316 | ,904 |
| Attitude towards innovations | −1,206 | ,570 | ,034** |
| Number of tractor | ,273 | ,814 | ,738 |
| Land size | ,019 | ,033 | ,568 |
| Total revenue | ,000 | ,000 | ,727 |
| Number of cattle | −,053 | ,039 | ,173 |
| Nagelkerke R Square = 0,52 | |||
| −2 Log likelihood = 79,74a | |||
| X2 = 9,374 p = 0,312 (Hosmer and Lemeshow test) | |||
| X2 = 42,632 P = 0,000 (Omnibus test) | |||
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