scholarly journals MODEL REGRESI TOBIT KONSUMSI SUSU CAIR PABRIK (Studi Kasus Rumah Tangga di Provinsi Bali)

2014 ◽  
Vol 3 (2) ◽  
pp. 75 ◽  
Author(s):  
I PUTU JERYANA ◽  
I PUTU EKA NILA KENCANA ◽  
G. K. GANDHIADI
Keyword(s):  
Regression Model ◽  
Sampling Technique ◽  
Information Criterion ◽  
Simple Random Sampling ◽  
Education Level ◽  
Tobit Regression ◽  
Response Variable ◽  
Independent Variables ◽  
Dairy Milk ◽  
Tobit Regression Model

Regression analysis is used to study the relationship between dependent (response) variable with one or more independent (causal) variables. While response data were censored, then Tobit regression model could be applied.  According to Greene (2003), censored data were data with incomplete observation or the dependent variable has a value of zero, while for the other observations have particular value.  This research aimed to model dairy milk’s consumption from households at Bali Province.  By using data from Survey SosialEkonomiNasional (SUSENAS) or Social Economy’s National Survey (SENS) for year 2012, 615 households were selected as sampling unit using simple random sampling technique, and found 123 households who consumed dairy milk.  The independent variables in our model were last education level completed by head of household’s (X1), head of household’s work (X2), age of head of household’s (X3),  amount of expenditure for food consumption’s (X4), number of household members (X5), and household income (X6), the response variable was budget for buying dairy milk (Y).  From six independent variables, is found only last education level by head household and amount of expenditure for food consumption had siginficant effect on Y’s.  The final Tobit regression model were obtained using AIC (Akaike Information Criterion) method is Y = -3314724 + 565429,7 X1 + 0,014278 X4 with pseudo R2 as much as 16.79 per cent.

ANALISA PENGARUH IKLIM ORGANISASI DAN KOMPETENSI TERHADAP KINERJA PEGAWAI PT HUTAMA KARYA

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Erik Wardhana, MM.
Keyword(s):  
Organizational Climate ◽  
Employee Performance ◽  
Sampling Technique ◽  
Simple Random Sampling ◽  
Free Variable ◽  
Independent Variables ◽  
Dependent Variables ◽  
Correlational Approach ◽  
Survey Research Methods ◽  
The Relationship

This study entitled "Analysis of Effect of Climate Organization and Competence Againt Employee PT. Hutama Karya ". The purpose of this study was to obtain information on the relationship between the free variable that organizational climate (X1) and competence (X2) with the dependent variable is employee performance (Y), either partially or simultaneously, This study used survey research methods with the correlational approach and predictive, which aims for the relationship and influence between independent and dependent variables. The sampling technique can be done randomly (simple random sampling) of 852 employees, which is considered to resprentatif is 89 people. And to solve problems, to analyze and examine the relationship and influence between the independent variables on the dependent variable used models kausalistik through regression analysis with SPSS 14.0

scholarly journals FAKTOR-FAKTOR YANG MEMPENGARUHI KEPATUHAN PELAPORAN WAJIB PAJAK ORANG PRIBADI

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Neni Marlina Br Purba ◽  
Dian Efriyenty
Keyword(s):  
Random Sampling ◽  
Descriptive Analysis ◽  
Sampling Technique ◽  
Simple Random Sampling ◽  
Test Results ◽  
Analysis Techniques ◽  
Independent Variables ◽  
The Government ◽  
Tax Payments ◽  
Tax Reporting

Tax reporting is the responsibility of taxpayers after making tax payments. The mismatch between the number of registered taxpayers and the number of taxpayers who report taxes is a separate problem for the government in managing the tax results received. Where the number who report taxes is very small compared to the number of registered taxpayers. Based on these problems, the purpose of this study is to determine the factors that influence individual taxpayer reporting compliance. The population used by all individual taxpayers who are registered at KPP Pratama Batam Selatan. While the sample was taken through simple random sampling technique with the Slovin formula so that the sample used was 100 respondents. Data analysis techniques are data instrument test, classical assumptions, descriptive analysis and hypothesis testing with the help of SPSS. The results obtained, partially the application of e-filling and tax sanctions has a significant effect on tax reporting compliance while tax awareness and understanding have no significant effect. The f test results show that all the independent variables simultaneously have a significant effect on the dependent variable.

scholarly journals Analysis of Severe Injury Accident Rates on Interstate Highways Using a Random Parameter Tobit Model

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Minho Park ◽  
Dongmin Lee
Keyword(s):  
Regression Model ◽  
Unobserved Heterogeneity ◽  
Random Parameter ◽  
Tobit Model ◽  
Severe Injury ◽  
Parameter Estimates ◽  
Random Parameters ◽  
Tobit Regression ◽  
Accident Rates ◽  
Tobit Regression Model

In this study, a random parameter Tobit regression model approach was used to account for the distinct censoring problem and unobserved heterogeneity in accident data. We used accident rate data (continuous data) instead of accident frequency data (discrete count data) to address the zero cell problems from data where roadway segments do not have any recorded accidents over the observed time period. The unobserved heterogeneity problem is also considered by using random parameters, which are parameter estimates that vary across observations instead of fixed parameters, which are parameter estimates that are fixed/constant over observations. Nine years (1999–2007) of panel data related to severe injury accidents in Washington State, USA, were used to develop the random parameter Tobit model. The results showed that the Tobit regression model with random parameters is a better approach to explore factors influencing severe injury accident rates on roadway segments under consideration of unobserved heterogeneity problems.

A STUDY ON PREVALENCE OF POSTPARTUM DEPRESSION AT TERTIARY HOSPITAL, WEST BENGAL.

2021 ◽  
pp. 33-36
Author(s):  
Chandrima Maity ◽  
Debasish Sanyal ◽  
Arati Biswas ◽  
Sudarsan Saha
Keyword(s):  
Logistic Regression ◽  
Postpartum Depression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Depression Scale ◽  
Sampling Technique ◽  
Simple Random Sampling ◽  
Background Information ◽  
Stepwise Logistic Regression ◽  
Postpartum Mothers

The investigators assessed the prevalence of Postpartum Depression (PPD), its clinical features and relationship of PPD with socio-demographical and obstetrical factors. The samples were selected from the OPD and IPD, of a Medical college in Kolkata.. Observational study was performed on 500(N=500) postpartum mothers who were selected by using Simple Random Sampling Technique within the six weeks of postpartum period. Data were collected by using the Structured Questionnaire for background information, Edinburgh Postnatal Depression Scale (Bengali Version of EPDS) for postpartum depression. Data analysis was performed using Descriptive Statistics, Chi-square, Logistic Regression and Decision Tree. A total of 112 (Prevalence Rate 22.4%) postpartum mothers had PPD. Stepwise logistic regression model correctly classied 92.2% of women who developed PPD. Using logistic regression model, postpartum depression is best predicted by: No. of Postpartum days p< 0.001***, Age of the mother p<0.024**, Religion p<0.003**, Type of family p<0.020**, Education of the mother p<0.001***, Monthly Income of the family p<0.001***, No of other living children p<0.001***, Pregnancy outcome p<0.033**, Any complication during pregnancy / delivery/ postpartum p< 0.001*** and Problems with family members p< 0.001***. The study recommends that evaluation should be carried out for Postpartum Depression and its risk factors to prevent and treat PPD in a timely manner.

Tobit model-based analysis on the influencing factors of wheat loss during harvesting by the combine harvester

2021 ◽  
pp. 1-14
Author(s):  
Hong Ji ◽  
Xun He ◽  
Li Ding ◽  
Zhe Qu ◽  
Wenkang Huang ◽  
...  
Keyword(s):  
Regression Model ◽  
Influencing Factors ◽  
Loss Rate ◽  
Henan Province ◽  
Normal Operation ◽  
Tobit Regression ◽  
Wheat Field ◽  
Combine Harvester ◽  
Main Factors ◽  
Tobit Regression Model

Based on the investigation data of wheat mechanized harvest in eight major wheat producing areas from the south to the north of Henan Province, the main factors affecting wheat mechanized harvest loss were identified and the influence of each factor was decomposed. In this article, the loss rate of wheat mechanical harvest was calculated by using the method of artificial measurement of wheat yield in the field, and the influencing factors of wheat mechanical harvest operation in 8 regions of Henan province were treated and analyzed by using Tobit regression model. In this paper, the loss rate of wheat mechanical harvest was calculated by using the method of wheat field artificial yield measurement and the influencing factors of wheat mechanical harvest operation in eight regions of Henan province were treated and analyzed by using Tobit regression model. The results show that the average harvest loss rate in the field amounts to 2.96%, the average harvest loss rate at the edge of field amounts to 3.06%, whereas the loss rate in the normal operation area amounts 2.86%. The main factors that caused the harvest loss of wheat field machinery were the maturity of wheat, the area of operation field, the diseases and pests, weather conditions and the accumulated working hours of harvester drivers in a single day. Therefore, the main technical measures to reduce the operation loss of wheat combine harvester were put forward to provide a theoretical basis for promoting the deep integration of agricultural machinery and agronomy.

scholarly journals Correlates of Supervisory Strategies and Quality Education in Secondary Schools in Oyo State, Nigeria

2012 ◽  
Vol 2 (3) ◽  
pp. 164
Author(s):  
Bada, Ibrahim Adegboyega
Keyword(s):  
Public Schools ◽  
Secondary Schools ◽  
Sampling Technique ◽  
Simple Random Sampling ◽  
Quality Education ◽  
Public And Private ◽  
Independent Variables ◽  
Service Commission ◽  
The Government ◽  
Joint Contribution

This study examined supervisory strategies as correlates of quality education among secondary schools in Ibadan southwest local government area of Oyo State. Simple random sampling technique was used to select thirty schools (public and private) and three hundred respondents consisting of 200 teachers from public schools, 80 teachers from private schools and 20 supervisors from Teaching Service Commission (TESCOM).Pearson Product Moment Correlation (PPMC) was used to test the four hypotheses(P < 0.05)  Multiple Regression was used to determine the contribution of independent variables (Supervisory strategies) both jointly and individually to dependent variable (quality education).Results revealed that two of the supervisory strategies (companionable and synergistic) have positive and significant correlation  on quality education (r =0.853) and (r = 0.783) while authoritarian strategy had negative  correlation, laissez faire had no significant relationship on quality education (r = -0.522 and r = -0.200)respectively. The joint contribution of the four supervisory strategies to the prediction of quality  education in secondary schools was also significant (F (4,295) = 261.225).The paper concludes  with some recommendations to the Teaching Service Commission (TESCOM) and the Government. Key words and Phrase: Supervisory Roles, Quality Education, Supervisory Strategies.

Operating Efficiency Analysis of Energy Industries in Taiwan

2013 ◽  
Vol 869-870 ◽  
pp. 612-620 ◽  
Author(s):  
Nattanin Ueasin ◽  
Anupong Wongchai
Keyword(s):  
Regression Model ◽  
Stock Exchange ◽  
Efficiency Score ◽  
Operating Efficiency ◽  
Data Envelopment ◽  
Tobit Regression ◽  
Factors Affecting ◽  
Taiwan Stock Exchange ◽  
Energy Companies ◽  
Tobit Regression Model

The energy business has played an important role in an economic growth of Taiwan because the market share is in the high value that can make a significant contribution towards regional and local employment. However, Taiwan is lack of energy resources, making the country highly relies on an import for more than 98 percent of its all energy. At present, a top priority of the countrys policy is to develop clean, sustainable, independent, and efficient energy in order to eliminate the vulnerability from external disruption. Therefore, this research aims to assess the operating efficiency and to analyze factors affecting the efficiency scores of the registered energy companies in the Taiwan Stock Exchange (TWSE) recorded during 2003-2012. The super-efficiency data envelopment analysis (SE-DEA) was initially applied to reveal the additional efficiency scores, followed by the Tobit regression model used to analyze what factors determine the efficiency scores. The empirical results showed that seven DMUs performed efficiently, ranking from 7.29 to 1.02. The company with the best operating performance was Taiwan Cogeneration Corporation (TCC), while the Great Taipei Gas Corporation (GTG) revealed the worst efficiency score. Furthermore, the Tobit regression model explained that the higher number of the local employees, the greater the efficiency scores were. Besides, the lower number of the shareholders, the greater the efficiency scores were. As a result, the Taiwans government is supposed to encourage all energy companies to have a higher number of local employees and shareholders to increase their efficiency scores.

scholarly journals An Experimental Investigation of Consumer Willingness to Pay for Non-GM Foods When an Organic Option Is Present

2006 ◽  
Vol 35 (2) ◽  
pp. 374-385 ◽  
Author(s):  
John C. Bernard ◽  
Chao Zhang ◽  
Katie Gifford
Keyword(s):  
Experimental Investigation ◽  
Willingness To Pay ◽  
Regression Model ◽  
Tobit Regression ◽  
Milk Chocolate ◽  
Gm Foods ◽  
Consumer Demographics ◽  
Tortilla Chips ◽  
Marginal Effects ◽  
Tobit Regression Model

This research compared bids that consumers placed on non genetically modified (GM), organic, and conventional versions of food products in order to determine if the organic market well serves those seeking to avoid GM foods. Auction experiments using potato chips, tortilla chips, and milk chocolate were conducted with 79 subjects. Bids were modeled as a function of consumer demographics using a heteroskedastic tobit regression model. Results with the non-GM attribute nested into the organic characteristic showed that the latter's marginal effects were insignificant. This suggested the potential to further develop non-GM products for consumers not willing to pay extra for the remaining organic attributes.

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