Statistical modeling to women self-help groups of Odisha based on socio-economic developmental parameters in aquaculture
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| 024 | 7 | 0 | |a 10.1007/s10499-014-9839-6 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10499-014-9839-6 | ||
| 245 | 0 | 0 | |a Statistical modeling to women self-help groups of Odisha based on socio-economic developmental parameters in aquaculture |h [Elektronische Daten] |c [Nirupama Panda, A. Mahapatra, D. Rath] |
| 520 | 3 | |a Multiple linear regression analysis was performed to estimate the relative effect of nine independent variables, i.e., age of the women self-help groups (WSHGs), number of group members, monthly savings of the group, pond area, fish production, number of literate members, number of technical trained members, bank loan and profit on average financial contribution of a WSHG (the dependent variable), a primary factor for socioeconomic development of women. Socioeconomic data on 90 WSHGs were collected from 27 blocks of eight districts from four physiographic zones, namely Northern Plateau, Central River Basin, Eastern Hills and Coastal Plains of Odisha during 2008-2009. It is revealed from the study that in all the four physiographic zones of Odisha, bank loan and profit have significant positive effect on average financial contribution of a WSHG (p<0.05). On the other hand, number of group members is a negative contributing factor (p<0.05) which explains that with increase in members, amount of bank loan and profit is proportionately reduced per member. Three factors such as monthly group savings, pond area and number of literate members are significantly contributing to average financial contribution of a group in Coastal Plains (p<0.05). As the WSHGs in Coastal Plains were more developed than the WSHGs in other three physiographic zones, the multiple linear regression model of Coastal Plains may be considered as a best fit model. The regression equation is expressed as $$\hat{Y}$$ Y ^ =1,746.876−125.763members+1.295mogrsav+313.730area+449.373literate+0.023loan (p<0.05). | |
| 540 | |a Springer International Publishing Switzerland, 2014 | ||
| 690 | 7 | |a Women self-help groups |2 nationallicence | |
| 690 | 7 | |a Aquaculture |2 nationallicence | |
| 690 | 7 | |a Multiple linear regression model |2 nationallicence | |
| 700 | 1 | |a Panda |D Nirupama |u Central Institute of Freshwater Aquaculture, 751002, Bhubaneswar, Odisha, India |4 aut | |
| 700 | 1 | |a Mahapatra |D A. |u Central Institute of Freshwater Aquaculture, 751002, Bhubaneswar, Odisha, India |4 aut | |
| 700 | 1 | |a Rath |D D. |u Central Institute of Freshwater Aquaculture, 751002, Bhubaneswar, Odisha, India |4 aut | |
| 773 | 0 | |t Aquaculture International |d Springer International Publishing |g 23/2(2015-04-01), 613-637 |x 0967-6120 |q 23:2<613 |1 2015 |2 23 |o 10499 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10499-014-9839-6 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s10499-014-9839-6 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Panda |D Nirupama |u Central Institute of Freshwater Aquaculture, 751002, Bhubaneswar, Odisha, India |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Mahapatra |D A. |u Central Institute of Freshwater Aquaculture, 751002, Bhubaneswar, Odisha, India |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Rath |D D. |u Central Institute of Freshwater Aquaculture, 751002, Bhubaneswar, Odisha, India |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Aquaculture International |d Springer International Publishing |g 23/2(2015-04-01), 613-637 |x 0967-6120 |q 23:2<613 |1 2015 |2 23 |o 10499 | ||