scholarly journals Multivariate Model for the Usage of Renewable Energies in a Rural Area

2020 ◽  
Vol 9 (1) ◽  
pp. 19-22
Author(s):  
Bernadett Nagy ◽  
Bernadett Horváthné Kovács ◽  
Ádám Csuvár ◽  
Alexander Titov
Keyword(s):  
Rural Area ◽  
Logistic Regression Model ◽  
Sampling Method ◽  
Multivariate Model ◽  
Significant Variable ◽  
Indicator Variable ◽  
Rural Region ◽  
Explanatory Variables ◽  
Residential Heating ◽  
Heating Technology

AbstractWithin Hungary, the Koppányvölgye rural area was chosen due to its unique natural circumstances with its broad green nature, concerning the inhabitants’ habit for the usage and knowledge of renewable energy for residential heating. Through quota-based sampling method we collected the demographic, social and economic variables to examine their level of influence on wood for residential heating usage. We received the Likert scale values through the questionnaire, which had to be recoded for the binomial logistic regression model that we chose to use because of the indicator variable’s trait, and in aim to examine the explanatory variables’ significance. As a result, for the wood indicator variable, the age of the respondent turned out to be a significant variable, the higher age compared to lower age is a chance decreasing category for wood usage, employed compared to unemployed increased the likelihood, thereby rejected the energy ladder phenomenon, as well as more people in one household increased the chance for wood usage. The higher education, environmental awareness and insulation level of a house turned out to be non-significant for wood usage. Therefore, we strengthened those statements from the earlier studies, that in this rural region, the change of the residential heating technology is more likely to be supply driven than demand driven.

scholarly journals Multivariate Model for Natural Gas use as a Residential Heating Source in Rural Area

2021 ◽  
Vol 78 (2) ◽  
pp. 123
Author(s):  
Bernadett NAGY ◽  
Bernadett HORVÁTHNÉ KOVÁCS ◽  
Ádám CSUVÁR ◽  
Alexander TITOV
Keyword(s):  
Natural Gas ◽  
Rural Area ◽  
Rural Areas ◽  
Environmental Benefits ◽  
Sampling System ◽  
Explanatory Variables ◽  
Heating Source ◽  
Equipment Replacement ◽  
Residential Heating ◽  
The Individual

We selected the rural region of Koppány Valley in Hungary to investigate the residents’ natural gas use practices. Natural gas can be a feasible alternative for improving the quality of life in rural areas. The study’s aims were to look at the social, economic, and environmental facets of residential gas use in order to assist regional planning decisions in our selected rural area that would encourage efficiency and energy source switchover. The variables were collected using a quota-based sampling system survey. We chose to use binomial logistic regression model to ex-amine the explanatory variables’ significance. The higher the settlement scale in our data, the more likely it is that gas will be used. Residents who do not trust their mayor have a lower chance of using gas. When compared to insulated homes, non-insulated houses are less likely to use gas. Higher education level, pensioner category, and whether the individual accepts that bio-gas has environmental benefits are not significant categories. Therefore, residential heating technology is more likely to be supply-driven, than demand-driven. We would suggest the application of subsidies for heating equipment replacement, in combination with educational campaigns, in addition to establishing a higher degree of trust in their mayors.

scholarly journals Selected Robust Logistic Regression Specification for Classification of Multi‑dimensional Functional Data in Presence of Outlier

2018 ◽  
Vol 2 (334) ◽  
Author(s):  
Mirosław Krzyśko ◽  
Łukasz Smaga
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Functional Data ◽  
Logistic Regression Model ◽  
Binary Classification ◽  
Classification Problem ◽  
Classification Rule ◽  
Unknown Parameters ◽  
Explanatory Variables

In this paper, the binary classification problem of multi‑dimensional functional data is considered. To solve this problem a regression technique based on functional logistic regression model is used. This model is re‑expressed as a particular logistic regression model by using the basis expansions of functional coefficients and explanatory variables. Based on re‑expressed model, a classification rule is proposed. To handle with outlying observations, robust methods of estimation of unknown parameters are also considered. Numerical experiments suggest that the proposed methods may behave satisfactory in practice.

Molecular biomarker analysis and survival in patients (pts) with advanced urothelial cancer (UC) previously treated with chemotherapy.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 4529-4529
Author(s):  
Bernadett Szabados ◽  
Marlon Rebelatto ◽  
Craig Barker ◽  
Alvin Milner ◽  
Arthur Lewis ◽  
...  
Keyword(s):  
Proportional Hazards ◽  
Bootstrap Method ◽  
Univariate Analysis ◽  
Prognostic Significance ◽  
Disease Process ◽  
Cox Proportional Hazards ◽  
Multivariate Model ◽  
Significant Variable ◽  
Platinum Based Chemotherapy ◽  
Biomarker Analysis

4529 Background: The biomarkers PD-L1, FOXP3, and CD8 have been explored in pts with advanced UC who progressed after platinum-based chemotherapy (CTx). However, their relevance earlier in the disease process is less well understood. Methods: The Phase 2/3 LaMB study (NCT00949455) compared maintenance lapatinib vs placebo after first-line (1L) platinum-based CTx in pts with HER1/HER2-overexpressing stage IV advanced UC. Pre-CTx archival samples from this study were retrospectively analyzed and included both randomized and screen failure pts. PD-L1 expression was assessed (VENTANA SP263 Assay) and categorized as high (≥25% of tumor cells [TC] and/or immune cells [IC]) or low/negative ( < 25% TC and IC). Overall survival (OS) and progression-free survival (PFS) were estimated via Kaplan-Meier method; results were stratified by PD-L1 expression. The exploratory biomarkers CD8 and FOXP3 were also analyzed. The prognostic significance of the biomarkers was explored by multivariable Cox proportional hazards models and a bootstrap method for model selection. Results: Of 446 pts (232 randomized; 214 screened), 243 (54.5%) were assessed for PD-L1 expression, with 61 (25.1%) PD-L1 high and 158 (65.0%) PD-L1 low/negative. In PD-L1 high and low/negative pts, respectively, median OS (95% CI) was 12.0 (9.4–19.7) vs 12.5 months (10.4–15.5); median PFS (95% CI) was 6.5 (3.5–8.8) vs 5.0 months (4.3–6.3). PD-L1 expression was not associated with OS or PFS in univariate analysis or in a multivariate model for OS (hazard ratio [HR] for PD-L1 high vs low/negative 1.4 [95% CI, 0.8–2.3]). In a multivariate model for PFS, PD-L1 expression improved accuracy of the model by 23% and was a significant variable (HR, 2.1 [95% CI, 1.2–3.5]). Results of analyses of CD8 and FOXP3 will also be reported. Conclusions: Overall, these data suggest a lack of association between PD-L1 expression and survival in pts receiving 1L platinum-based CTx. Mechanisms underlying the potential association of PD-L1 expression with PFS remain unclear. CD8 and FoxP3 exploratory analyses may help to elucidate these results. Clinical trial information: NCT00949455.

scholarly journals Factors associated with the success of exclusive breastfeeding in the rural area

2021 ◽  
Vol 129 (s2) ◽  
Author(s):  
Reza Putri Maghriza ◽  
Ninuk Dwi Ariningtyas ◽  
Yelvi Levani ◽  
Musa Ghufron
Keyword(s):  
Family Support ◽  
Rural Area ◽  
Exclusive Breastfeeding ◽  
Maternal Education ◽  
Rural Region ◽  
Factors Associated ◽  
The Relationship

Introduction: In Indonesia, coverage of exclusive breastfeeding has not yet met the government’s goal of 80%. This study aimed to ascertain the relationship between maternal education and occupation, family support, and belief in myths and exclusive breastfeeding success in a rural region.

Spurious effects in variational corpus linguistics

2014 ◽  
Vol 19 (4) ◽  
pp. 478-504 ◽  
Author(s):  
José Tummers ◽  
Dirk Speelman ◽  
Dirk Geeraerts
Keyword(s):  
Corpus Linguistics ◽  
Multiple Correspondence Analysis ◽  
Multivariate Model ◽  
Data Matrix ◽  
Response Variable ◽  
Explanatory Variables ◽  
Confounding Variables ◽  
The Impact ◽  
Attributive Adjectives

As repositories of spontaneously realized language, corpora generally have an uncontrolled and unbalanced structure where all variables operate simultaneously. Consequently, a variable’s real effect can be concealed when studied in isolation because of the exclusion of the impact of other potentially confounding variables. Analyzing a variational case study, the alternation between inflected and uninflected attributive adjectives in Dutch, it will be demonstrated how confounding variables alter the impact of explanatory variables on the response variable, resulting in spurious effects in the bivariate analyses. Multiple Correspondence Analysis will be used as a heuristic tool to unveil the association patterns between explanatory variables in the data matrix which induce the spurious effects. Based on these findings, we will argue for a thorough analysis of the database patterns to gain insight in the underlying associations between explanatory variables before modeling their real impact on the response variable in a multivariate model.

scholarly journals PENGARUH KUALITAS PRODUK, PROMOSI PENJUALAN, DAN LOKASI GERAI TERHADAP KEPUTUSAN PEMBELIAN KONSUMEN TAS EIGER DI KOTA BOGOR

2019 ◽  
Author(s):  
Heruwansyah
Keyword(s):  
Product Quality ◽  
Sampling Method ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Statistical Test ◽  
Sales Promotion ◽  
Purchase Decision ◽  
Significant Variable ◽  
Purchasing Decisions ◽  
Independent Variables

This study aims to examine the effect of Product Quality, Sales Promotion, and Outlet Location on Consumer Purchasing Decisions on Eiger Brand Bags in Bogor City. This study uses independent variables namely Product Quality, Sales Promotion, Outlet Location. The dependent variable is the Consumer Purchase Decision. The data in this study are secondary data.The sample of this study is consumers who have already bought the Eiger brand bag products. The sample is done by non-probability sampling method. Data collection was conducted with a questionnaire distributed directly to consumers who had bought Eiger brand bags as many as 120 respondents. The statistical method uses multiple linear regression analysis, by testing the statistical test hypothesis t.The results of this study indicate a positive and significant variable in Product Quality with t count greater than t table (3.674&gt; 1.66), sales promotion t count greater than t table (2.526&gt; 1.66), and there is one variable that is not positive and significant effect on Consumer Purchasing Decisions namely Outlet Location variables with the acquisition of t count smaller than t table (0.65 &lt;1.66).

scholarly journals Diagnosing Multicollinearity of Logistic Regression Model

2019 ◽  
pp. 1-9 ◽  
Author(s):  
N. A. M. R. Senaviratna ◽  
T. M. J. A. Cooray
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Sample Size ◽  
Logistic Regression Model ◽  
Secondary Data ◽  
Binary Logistic Regression ◽  
Condition Index ◽  
Binary Logistic Regression Model ◽  
Correlated Variables ◽  
Explanatory Variables

One of the key problems arises in binary logistic regression model is that explanatory variables being considered for the logistic regression model are highly correlated among themselves. Multicollinearity will cause unstable estimates and inaccurate variances that affects confidence intervals and hypothesis tests. Aim of this was to discuss some diagnostic measurements to detect multicollinearity namely tolerance, Variance Inflation Factor (VIF), condition index and variance proportions. The adapted diagnostics are illustrated with data based on a study of road accidents. Secondary data used from 2014 to 2016 in this study were acquired from the Traffic Police headquarters, Colombo in Sri Lanka. The response variable is accident severity that consists of two levels particularly grievous and non-grievous. Multicolinearity is identified by correlation matrix, tolerance and VIF values and confirmed by condition index and variance proportions. The range of solutions available for logistic regression such as increasing sample size, dropping one of the correlated variables and combining variables into an index. It is safely concluded that without increasing sample size, to omit one of the correlated variables can reduce multicollinearity considerably.

scholarly journals Determinants of University Dropout: A Case of Thailand

2019 ◽  
Vol 15 (7) ◽  
pp. 49
Author(s):  
Kelzang Tentsho ◽  
Rhysa McNeil ◽  
Phattrawan Tongkumchum
Keyword(s):  
Logistic Regression Model ◽  
Dropout Rate ◽  
Educational Institutions ◽  
First Semester ◽  
Explanatory Variables ◽  
Student Dropout ◽  
Prince Of Songkla University ◽  
The World ◽  
Admission Type ◽  
University Dropout

Student dropout is a growing concern for educational institutions across the world and extensive research on this issue has been done in past few decades. In this study, we analyzed the determinants of student propensity to dropout at Prince of Songkla University, Pattani campus. The data comprised 10,377 students enrolled between the 2007 and 2011 academic years. Variables included in the analysis were admission year, faculty, gender-religion, first semester GPA and admission type. The overall dropout rate over the five-year period was 23.9%, and a decreasing trend in dropout rate was found from second semester and onwards. A logistic regression model was used to determine the effect of explanatory variables on dropout. The findings indicate that admission year, gender-religion, faculty and first semester GPA are strongly associated with student dropout.

scholarly journals Pengaruh Total Aktiva, Jumlah Sekuritas, Perputaran Portofolio, Laba/Rugi Operasi Dan Opini Akuntan Terhadap Audit Delay Pada Produk Reksa Dana Di Indonesia

2010 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Joicenda Nahumury
Keyword(s):  
Mutual Funds ◽  
Mutual Fund ◽  
Sampling Method ◽  
Financial Statements ◽  
Multiple Linear Regressions ◽  
Audit Report ◽  
Purposive Sampling ◽  
Explanatory Variables ◽  
Future Researcher ◽  
Linear Regressions

AbstrackThis study empirically examined the effect of several company & audit characteristics on audit report lag or audit delay. Determinants of audit delay in mutual funds were chosen as an object of investigation. The purpose of this study to reveal that those variables have significant effect on audit delay simultaneously or partially. Samples are selected by purposive sampling method. The results of multiple linear regressions show that all of the explanatory variables influences audit delay simultaneously. The rest of variables do not appear to have any bearing on mutual fund audit delay. This Result is suggested for auditor to perform the audit more efficient and effective to get audit report timely, for BAPEPAM-LK as regulator to review again the deadline of audited financial statements delivery of mutual funds, for the future researcher to be reference in developing investigation.                                                                                                                                                                           

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