EDUCATION LEVEL AND ADOPTION OF PROCESS INNOVATION IN THE DAIRY SECTOR OF ZIMBABWE

2021 ◽  
Vol 5 (9) ◽  
Author(s):  
Alpha Marlvin Manjera ◽  
Alpha Marlvin Manjera
Keyword(s):  
Logistic Regression ◽  
Change Management ◽  
Logistic Regression Model ◽  
Process Innovation ◽  
Education Level ◽  
Research Issue ◽  
Dairy Farmers ◽  
Level Of Education ◽  
Process Innovations ◽  
New Process

This paper, seek to assess factors that are hindering diffusion of process innovation under the Zimbabwean dairy industry. The paper utilized 400 smallmedium scale dairy farmers in Zimbabwe who are registered under Zimbabwe Association of Dairy Farmers (ZADF). The rationale behind the research issue is that Zimbabwean dairy sector has failed to adopt process innovations such as e-marketing and e-extension. The paper adopted a binomial logistic regression model and majority of the dairy farmers in Zimbabwe have not acquired any form of education and based on such evidence, level of education has affected the level of adoption of process innovations such as e-marketing and e-extension. The research recommends the dairy sector to introduce nonformal courses and trainings on change management and innovations prior to introduction of new process innovations.

IMPLEMENTASI MODEL REGRESI LOGISTIK MULTINOMIAL PADA PENGELOMPOKAN PENYAKIT TUBERKULOSIS

2021 ◽  
Vol 13 (1) ◽  
pp. 27
Author(s):  
Mella Apriyani ◽  
Jajang Jajang ◽  
Agustini Tripena Br. Sb.
Keyword(s):  
Logistic Regression Model ◽  
Basic Education ◽  
Multinomial Logistic Regression ◽  
Education Level ◽  
Dominant Factor ◽  
Multinomial Logistic Regression Model ◽  
Factor Level ◽  
Level Of Education ◽  
Pulmonary Tb ◽  
Extra Pulmonary

There are three types of  Tuberculosis (TB) patients at Banyumas Region Hospital, namely negative  pulmonary TB, positive pulmonary TB, and extra pulmonary TB. Types of TB generally caused by age, cae of history, gender, level of education, and domicile. One of the methods that used to find a correlation between types of TB with the affect is regression analysis. This study used multinomial logistic regession analysis because types of TB is categorical and the data is 156 TB’s patients recorded at 2018/2019. The result showed that the level of education be a dominant factor to affect TB. Here, we noted that patients with basic education level have a 5,843 time odds for getting positive pulmonary TB and 2,224 times for getting extra pulmonary TB. The multinomial logistic regression model is then given as probability for getting positive pulmonary TB with factor level of education is greather than negative pulmonary TB and extra pulmonary TB.

scholarly journals The Influence of Borrowers' Language Features on P2P Lending

2021 ◽  
Author(s):  
Zhengwei Ma ◽  
Dan Zhang
Keyword(s):  
Logistic Regression ◽  
Information Asymmetry ◽  
Logistic Regression Model ◽  
Risk Control ◽  
Education Level ◽  
Probability Of Default ◽  
Language Function ◽  
Linguistic Features ◽  
P2p Lending ◽  
Default Rate

Abstract Under the background of the reshuffle of the P2P market in China, this paper investigates the influence of four borrower's language features on their funding and default rate based on language function theories. In our study, we use a logistic regression model and the empirical results show that: the more redundant the borrower's language expression is, the more open and objective the content is, and the more attention is paid to the punctuation details, the easier it is to obtain the loan successfully. When the borrower's description is more redundant and more attention is paid to the punctuation details, the probability of default would become lower. Taking the education level into consideration, we find that the negative relating effect between the description redundancy and the default rate would be lower with the increase of the borrower’s education level. Therefore, we can conclude that the four linguistic features of borrowers which are defined in this paper can alleviate the information asymmetry problem of P2P lending to some extent and the borrower's linguistic features can be included into the risk control system.

scholarly journals Challenges of and Opportunities for Market Access by Pineapple Agripreneurs in Rangwe Sub County, Homa Bay County, Kenya

2021 ◽  
pp. 130-140
Author(s):  
Winstone Asugo Nyaguti ◽  
Job Kibiwot Lagat ◽  
Hillary Bett ◽  
Fredrick Onyango Ogutu
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Model ◽  
Market Access ◽  
Descriptive Analysis ◽  
Household Head ◽  
Education Level ◽  
Primary Data ◽  
Limited Attention ◽  
Survey Tool

Pineapple farming is among the fastest-growing agricultural sub-sectors in Homa Bay County, Kenya specifically Rangwe sub-county. However, limited attention has been given to the market access of this produce. Evidenced by vast quantities of pineapples harvested from the farms and stacked along main highways without targeting a specific market. This result to a small portion being sold and the rest deteriorating, consequently reducing returns for pineapple agripreneurs. Therefore, this paper sought to determine those factors that influences access to formal market by pineapple agripreneurs and as well as find out challenges of and opportunities for accessing formal markets by the Rangwe pineapple agripreneurs. The survey was undertaken in Rangwe Sub-county, and multisampling method was used to select a sample of 183 pineapple agripreneurs from the study area, primary data was collected using a semi-structured survey tool. Data was analyzed by descriptive analysis and Logistic regression model. Results indicated that pineapple agripreneurs were faced with numerous challenges in accessing pineapple market, also there existed several market access opportunities for pineapple agripreneurs. The results of logistic regression analysis revealed extension contacts, education level and price of pineapple as factors that were positively and statistically significant in influencing access to formal markets. While those that were statistically and negatively influencing access to formal market comprised of; age of household head, household size, and type of road. The study recommends;  improvement of road infrastructure in pineapple producing areas so as to improve on their market access; development of policies that encourages capacity building of pineapple agripreneurs in Rangwe sub-county 

scholarly journals Modeling and Analyzing the Factors Affecting the Mortality of Pedestrians in Urban Accidents

2018 ◽  
Vol 14 (4) ◽  
pp. 10-21
Author(s):  
Ahmet Tortum ◽  
Alireza Motamadnia
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Motor Vehicle ◽  
Binary Logistic Regression ◽  
Education Level ◽  
Road Surface ◽  
Binary Logistic Regression Model ◽  
Factors Affecting ◽  
Surface Conditions

Abstract The nature of urban and rural accidents has been different from each other in some of the factors and even the severity of damage rate, mayhem, and death. In this research, using statistical methods and binary logistic regression model, we have addressed to analyze important parameters such as age, gender, education level, the color of the pedestrian dress, season of accident, time of accident, the speed of the vehicle colliding with pedestrians and road surface conditions at the time of accident on the way of death (at the scene of the incident or in the hospital) pedestrians who have been traumatized. After the creation of the binary logistic regression model, it was determined that only the parameters of speed and the accident time have been significant in the level less than 5%. And other parameters such as age, gender, the season of accident occurrence, the color of the pedestrian dress, road surface conditions and education level had no significant effect in terms of statistical on the incidence of mortality arising from a pedestrian accident with the motor vehicle. The results revealed that by adopting decisions related to the traffic calming, attention to passages lighting and brightness the mortality rate of a pedestrian due to the urban accidents can be reduced.

scholarly journals Determinan Keputusan Melakukan Migrasi Ulang-Alik

2018 ◽  
Vol 1 (3) ◽  
pp. 242-251
Author(s):  
Rifqi Nur Fahmy
Keyword(s):  
Logistic Regression ◽  
Marital Status ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Analysis ◽  
Analysis Method ◽  
Binary Logistic Regression Model ◽  
Level Of Education ◽  
Independent Variables ◽  
Variable Level

The aim of this research is to analyze the influence of dependent variable of family dependent, education level, age, marital status, and distance partially to workforce’s decision to migrate from Surakarta to Karanganyar Regency. This research used binary logistic regression analysis method. The sample in this research is 100 respondents. The result of binary logistic regression model analysis in this research shows that from five independent variables, there are two variables that have significant effect on workforce’s decision to do the commuter migration that is dependent variable of family and marital status. While the variable level of education, age, and distance have no effect on workforce’s decisions to do the commuter migration. Tujuan dari penelitian ini adalah untuk menganalisis pengaruh variabel dependen dependen keluarga, tingkat pendidikan, usia, status perkawinan, dan jarak secara parsial terhadap keputusan tenaga kerja untuk bermigrasi dari Surakarta ke Kabupaten Karanganyar. Penelitian ini menggunakan metode analisis regresi logistik biner. Sampel dalam penelitian ini adalah 100 responden. Hasil analisis model regresi logistik biner dalam penelitian ini menunjukkan bahwa dari lima variabel independen, ada dua variabel yang berpengaruh signifikan terhadap keputusan angkatan kerja untuk melakukan migrasi komuter yang merupakan variabel dependen keluarga dan status perkawinan. Sedangkan tingkat variabel pendidikan, usia, dan jarak tidak berpengaruh pada keputusan tenaga kerja untuk melakukan migrasi komuter.  

Revealing images as Facebook profile pictures: Influences of demographics and relationship status

2017 ◽  
Vol 45 (6) ◽  
pp. 987-997 ◽  
Author(s):  
Amir Hetsroni ◽  
Dror A. Guldin
Keyword(s):  
Logistic Regression ◽  
Romantic Relationship ◽  
Logistic Regression Model ◽  
Relationship Status ◽  
Multiple Logistic Regression ◽  
Self Presentation ◽  
Level Of Education ◽  
Younger Age ◽  
The Impact ◽  
Profile Pictures

In this study, we examined the impact of demographics and relationship status on posting a revealing picture of oneself as a profile picture on Facebook. We randomly sampled and coded 1,000 profiles of Israeli Facebook users ranging in age between 18 and 61 years. Over 40% of the profiles showed at least 1 picture of the user dressed in a skimpy outfit. Results of a multiple logistic regression model indicated that younger age, a lower level of education, and not being engaged in a committed romantic relationship were significant predictors of the posting of these pictures. Further, gender alone was not a significant predictor, but the interaction of gender and level of education was. Specifically, women with a high-schoollevel education posted revealing pictures of themselves more often than did men with a similar level of education. We analyzed our results in light of Goffman's theory of self-presentation.

scholarly journals Eksploitasi terhadap Anak yang Bekerja di Indonesia

2013 ◽  
Vol 13 (2) ◽  
pp. 177-195
Author(s):  
Beta Septi Iryani ◽  
D.S. Priyarsono
Keyword(s):  
Logistic Regression ◽  
Labor Force ◽  
Working Hours ◽  
Education Level ◽  
Analytical Tool ◽  
Influential Factor ◽  
Access To Education ◽  
Level Of Education ◽  
Working Children ◽  
Head Of Household

This study investigated the severity of exploitation of working children and factors determining exploitation of working children. This study used data resulted from National Labor Force Survey (Sakernas) 2011 and utilized logistic regression as the analytical tool. Based on the severity of exploitation, there are three provinces which are always of high value severity of exploitation, namely DKI Jakarta, Banten, and West Java. Education level of household is an influential factor of exploitation measured by working hours and access to education. The lower level of education of head of household, the greater chance the child to be exploited. As for the exploitation measured by wage, girls have a chance 2.357 times greater than that of boys to be for exploited measured by wages.AbstrakStudi ini bertujuan menganalisis tingkat keparahan eksploitasi terhadap anak yang bekerja dan faktor-faktor yang memengaruhi terjadinya eksploitasi. Data Sakernas 2011 dan regresi logistik digunakan sebagai alat analisis. Berdasarkan tingkat keparahan eksploitasi, terdapat tiga provinsi yang selalu tinggi nilai keparahan eksploitasinya, yaitu DKI Jakarta, Banten, dan Jawa Barat. Pendidikan kepala rumah tangga (KRT) merupakan salah satu faktor yang berpengaruh terhadap eksploitasi dari segi jam kerja dan akses pendidikan. Semakin rendah pendidikan KRT, semakin besar peluang anak untuk tereksploitasi. Sedangkan untuk eksploitasi dari segi upah, anak perempuan memiliki peluang 2,357 kali untuk tereksploitasi dari segi upah dibandingkan anak laki-laki.

scholarly journals The status of health promotion lifestyle and its related factors in Shandong Province, China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Qianqian Liu ◽  
Shusheng Huang ◽  
Xiaoyuan Qu ◽  
Aitian Yin
Keyword(s):  
Health Promotion ◽  
Logistic Regression ◽  
Behavioral Intention ◽  
Logistic Regression Model ◽  
Education Level ◽  
Shandong Province ◽  
Related Factors ◽  
Gender Education ◽  
The Status ◽  
Per Capita

Abstract Objectives This study aims to explore the status of Shandong Province, China residents’ health promotion lifestyle and its influencing factors, especially to explore how health attitude affects health promotion lifestyle, thus can make targeted recommendations for health promotion in China and similar areas. Methods 1800 adults were selected from urban and rural areas of Shandong Province, China, using multistage stratified, cluster random sampling method. A survey was conducted face-to-face from March to May, 2018, using Health Promotion Lifestyle Profile and Health Attitude Questionnaire. The between-group measured data were compared by One-way ANOVA or t-tests. The correlation between the health attitude and health promotion lifestyle was examined by Pearson correlation. Logistic regression model was used to examine the related factors influencing health promotion lifestyle. Health promotion lifestyle is the dependent variable, and gender, education level, annual family per capita income and health attitude are the independent variables. Results The mean (SD) of HPLP-IICR total score of the participants was 82.12(16.63). 54.50% of the participants had poor or average health promotion lifestyle, while 45.50% had good or excellent health promotion lifestyle. Significant differences existed in health promotion lifestyle among different gender, education level, income level, marital status, and health attitude (Ps < 0.001). Multivariable Logistic regression model found that male (OR = 0.35, 95% CI: 0.12–0.34), high school education level (OR = 0.57, 95% CI:0.17–0.41), junior middle school & below (OR = 0.42; 95% CI:0.12–0.33), annual family per capita income with < 10,000 CNY (OR = 2.53, 95% CI:1.24–2.06; OR = 2.14, 95% CI:1.08–3.12), low health affection (OR = 0.39, 95% CI:2.15–4.22), and low health behavioral intention (OR = 0.21; 95% CI: 2.33–5.29) were statistically significant correlates of average or poor health promotion lifestyle. Conclusions The health lifestyle needs to be further promoted in Shandong Province, China. The government and social sectors are encouraged to make more efforts to improve the accessibility and quality of health services. Meanwhile, individual responsibility cannot be ignored as well. More affective factors and operable measures should be added to enhance health affection and health behavioral intention, so as to enhance health promotion lifestyle.

scholarly journals The determinants of poverty in the Mexican states of the US–Mexico border

2015 ◽  
Vol 17 (33) ◽  
pp. 141-167 ◽  
Author(s):  
Jorge Garza–Rodríguez
Keyword(s):  
Logistic Regression ◽  
Logistic Regression Model ◽  
Household Head ◽  
The United States ◽  
Education Level ◽  
Domestic Service ◽  
Support Worker ◽  
Household Location ◽  
The Us ◽  
Mexico Border

This study examines the determinants or correlates of poverty in the Mexican states bordering with the United States. The data used in the paper come from the 2008 National Survey of Income and Expenditures of Households. A logistic regression model was estimated to determine which variables might be important in explaining poverty in this region. It was found that the variables which are positively correlated with the probability of being poor are: living in Coahuila, Tamau­lipas or Chihuahua, size of the household, being an ambulatory worker or working in an agricultural occupation, and being a manufacturing, transportation, sales, domestic service or support worker. Variables that are negatively correlated with the probability of being poor are living in Baja California, the education level of the household head and his/her age. Gender of the household head and household location were not statistically significant in the logistic regression analysis.

scholarly journals FENOMENA MIGRASI SIRKULER DI KOTA MATARAM

GANEC SWARA ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 557
Author(s):  
NI PUTU FEBBY SUNDARI ◽  
PUTU KARISMAWAN ◽  
EMY SALMAH
Keyword(s):  
Distance Education ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Marital Status ◽  
Logistic Regression Analysis ◽  
Education Level ◽  
Level Of Education ◽  
Working Age ◽  
Labor Resources ◽  
The City

Theoretically, development of the city depends on labor resources. Generally, labor resources of the city come from periphery area. Developed infrastructure is a factor that mobilized labor resources from periphery to the city. There are two alternatives to the workers who come from periphery area; first they can stay in the city by renting apartment or the second as commuter. Many of the workers choose as commuter, so it is quite interesting to know what factors influence workers make choice as commuter. It is more specific whether factors such as income, length of work, age, distance, education level, and marital status influence the interest of workers to conduct circular migration. The analytical tool used is logistic regression analysis, Binary Regression Logistic. This research is a descriptive study that uses quantitative analysis using 1 dependent variable, namely interest in migration and six independent variables, namely income, length of working, age, distance, education level, and marital status. To achieve these objectives in this research logistic regression analysis was used, namely Binary Regression using primary data as many as 80 respondents. The results show that above variables; income, length of working, age, distance, level of education and marital status simultaneously and significantly influence on interest of workers in doing circulation migration to the Mataram City. Factors that partially influence significantly on the interest of workers doing migration to the Mataram City with a significance value with an alpha level of 5% are income, length of working, distance and level of education. While the age variable and marital status is not significant in effect.

Sign in / Sign up

Export Citation Format

Share Document