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 Analisis Pengobatan Pasien Menggunakan Model Regresi Logistik Biner

2020 ◽  
Vol 6 (2) ◽  
pp. 92
Author(s):  
Krishna Krishna Prafidya Romantica
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Explanatory Variable ◽  
Binary Logistic Regression ◽  
Residential Areas ◽  
Binary Logistic Regression Model ◽  
Dummy Variables ◽  
Explanatory Variables ◽  
Patient Class

Researcher used patient data spread across two residential areas, namely sector 1 and sector2. The research data consisted of four explanatory variables, namely: the age of the patient, the class ofpatients found in the hospital, the patient’s area of residence, and the findings of the disease suffered by the patient. Class, sector, and disease variables are variables categorized into categories 0 and 1. The researcher considers the dummy variables discussed in the explanatory variable variables. Category 0 indicates that the sample does not meet the criteria in the category. Choosing, category 1 shows that the sample meets the criteria in the category. Next, the researcher will estimate the explanatory parameter variables and dummy variables, then do the partial test to get the parameter significance and model it using the Binary Logistic Regression Model. With the Logistic Regression Model, researcher will calculate the consideration of the patient’s recovery. This probability is used as

scholarly journals Factors That Can Trigger Depression

2021 ◽  
Vol 7 (2) ◽  
pp. 164-185
Author(s):  
Haydée Maria Correia da Batista ◽  
Andrea Borges Paim ◽  
Brenda Santos Siqueira ◽  
Nelson Francisco Favilla Ebecken ◽  
Ana Claudia Dias
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
National Health Survey ◽  
Machine Learning Techniques ◽  
Binary Logistic Regression Model ◽  
Explanatory Variables ◽  
Learning Techniques

According to data from the last National Health Survey (PNS), conducted in 2013 by the Brazilian Institute of Geography and Statistics (IBGE) in partnership with the Ministry of Health, 7.6% of people aged 18 and over received diagnosis of depression. Therefore, based on this research, the purpose of this study was to identify factors that may be relevant to a possible diagnosis of depression, using machine learning techniques. The binary logistic regression model was chosen as the machine learning technique, with progressive and regressive methods for selecting variables and a model built by the researcher, generating seven different models. The model’s performance evaluation was made by comparing some metrics such as Cox-Snell R2 and Nagelkerke R2, which presented remarkably close results. Based on these models, 37 explanatory variables were selected which were applied to a new logistic regression model. The results showed that some variables significantly increased the chance of a positive diagnosis of depression as well as some variables were indicative of a reduction in the chances of this diagnosis.

scholarly journals FAKTOR-FAKTOR YANG MEMPENGARUHI PEROLEHAN KREDIT OLEH PENGUSAHA MIKRO

2018 ◽  
Vol 4 (3) ◽  
Author(s):  
Abdul Azis Safii ◽  
Tri Suwarno
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Large Scale ◽  
Logistic Regression Model ◽  
Banking Sector ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Access To Credit ◽  
The Poor ◽  
Micro Enterprises

Abstract: The number of micro-entrepreneurs and the dominant number of micro enterprises compared to medium and large-scale enterprises in Indonesia are not balanced by the provision of access to credit and venture capital for micro businesses. This resulted in a micro-sector sector identical to the poor being vulnerable to exploitation by moneylenders who exploit the difficulties of micro entrepreneurs accessing credit from the banking sector. This study examines the factors that determine the accessibility of credit by micro entrepreneur in Bojonegoro regency. A total sum of 270 micro entrepreneurs who have applied for banking loan were sampled from the study area. With an binary logistic regression model the research resulting that education, skill on entrepreneur, and monthly net profits generated by the microenterprise are significant in determining the accessibility of microcredit. Keywords: micro entrepreneur, microcredit, credit accessibility Abstrak: Perkembangan jumlah pengusaha mikro serta dominannya jumlah usaha mikro dibandingkan dengan usaha menengah dan usaha besar di Indonesia, tidak diimbingi dengan penyediaan akses kredit dan modal usaha bagi para pelaku usaha mikro. Hal tersebut mengakibatkan sektor usaha mikro yang identik dengan masyarakat miskin rentan dieksploitasi oleh rentenir yang memanfaatkan sulitnya para pengusaha mikro mengakses kredit dari sektor perbankan. Penelitian ini menggunakan data primer yang di ambil langsung dari pengusaha mikro dengan teknik kuesioner. Analisis data dengan metode binary logistic regression mendapatkan hasil variabel yang berpengaruh signifikan terhadap akses kredit para pengusaha mikro adalah variabel usia pengusaha, laba bersih usaha tiap bulan, dan jumlah karyawan yang di pekerjakan. Kata kunci : usaha mikro, microcredit, akses kredit

scholarly journals ESTIMATION OF THE BINARY LOGISTIC REGRESSION MODEL PARAMETER USING BOOTSTRAP RE-SAMPLING

2018 ◽  
Vol 48 (3) ◽  
pp. 199-204 ◽  
Author(s):  
R. LI ◽  
J. ZHOU ◽  
L. WANG
Keyword(s):  
Logistic Regression ◽  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
Parametric Bootstrap ◽  
Binary Logistic Regression Model ◽  
Bayesian Bootstrap ◽  
Non Parametric

In this paper, the non-parametric bootstrap and non-parametric Bayesian bootstrap methods are applied for parameter estimation in the binary logistic regression model. A real data study and a simulation study are conducted to compare the Nonparametric bootstrap, Non-parametric Bayesian bootstrap and the maximum likelihood methods. Study results shows that three methods are all effective ways for parameter estimation in the binary logistic regression model. In small sample case, the non-parametric Bayesian bootstrap method performs relatively better than the non-parametric bootstrap and the maximum likelihood method for parameter estimation in the binary logistic regression model.

Die Flexion der Indefinita jemand und niemand

2021 ◽  
Vol 49 (2) ◽  
pp. 209-243
Author(s):  
Linnéa Weitkamp
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Influencing Factors ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Corpus Study ◽  
Frequent Use ◽  
Current Variation ◽  
Reference Corpus

Abstract This article investigates the inflection of the German indefinite pronouns jemand and niemand in the accusative and dative. The pronouns are used both with inflectional suffix (jemanden/jemandem, niemanden/niemandem) and without (jemand, niemand) and are thus an example of current variation in contemporary German. The grammars take an unusually liberal stance and describe both forms as correct, partially even with preference to the uninflected form. A corpus study which examines conceptually written data of the DeReKo (German reference corpus) and conceptually oral data of the DECOW16B (German web corpus), shows that over 90 % of occurrences are inflected. But almost 10 % of uninflected forms show that these formations are no arbitrary errors either. To find out what influences the presence or absence of the inflectional ending, a binary logistic regression model was calculated. The following factors proved to be significant influencing factors for inflection: the degree of formality (DeReKo vs. DECOW16B), the lexeme (jemand vs. niemand), the case (acc vs. dat), government by preposition vs. government by verb and the following nominalized adjective (jemand anderen). With regard to the different inflectional suffixes, the frequent use of -en in the dative stood out in particular. Although this form is classified as erroneous in all grammars, almost 30 % of the dative occurrences in informal DECOW16B data are formed in this way.

scholarly journals Determinants of adaptation strategies to climate change among the smallholder farmers in Adama District, Ethiopia

2020 ◽  
Vol 12 (4) ◽  
pp. 463-476
Author(s):  
Hurgesa Hundera Hirpha ◽  
Sylvester Mpandeli ◽  
Amare Bantider
Keyword(s):  
Climate Change ◽  
Logistic Regression ◽  
Regression Model ◽  
Climate Change Adaptation ◽  
Logistic Regression Model ◽  
Smallholder Farmers ◽  
Binary Logistic Regression ◽  
Adaptation Strategies ◽  
Binary Logistic Regression Model ◽  
Content Type

Purpose The Ethiopian economy is mainly based on the rain-fed agriculture practiced by smallholder farmers. The sector is highly vulnerable to climate change impacts. This study aims to examine the determinants of adaptation strategies to climate change among the smallholder farmers in Adama District, Ethiopia. Design/methodology/approach A cross-sectional survey design was used to collect quantitative data using questionnaire with 351 randomly selected smallholder farmers. To collect qualitative data focus group discussions, key informant interviews and field observations were also used. Triangulated with thematic analysis, descriptive statistics and binary logistic regression model were used for the analysis. Findings The result indicated that the majority of the smallholder farmers use at least one climate change adaptation strategy in their local areas though the strategy is generally weak. In this regard, some of the dominant climate change adaptation activities identified in the study area are using improved crop varieties, planting trees, watershed management, adjusting planting date and terracing. The result from binary logistic regression model showed that age and sex of household head, as well as their education, family size, access to agricultural extension services and training on climate change significantly influence the practices of adaptation measures. Originality/value This study would help the practitioners to modify the existing weak adaptation activities by introducing advanced and technological-based adaptation strategies to the rural farming communities.

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