scholarly journals PENERAPAN REGRESI LOGISTIK ORDINAL UNTUK MENGANALISIS TINGKAT KEPARAHAN KORBAN KECELAKAAN LALU LINTAS KABUPATEN BULELENG

2015 ◽  
Vol 4 (2) ◽  
pp. 54
Author(s):  
DEWA AYU MADE DWI YANTI PURNAMI ◽  
I KOMANG GDE SUKARSA ◽  
G. K. GANDHIADI
Keyword(s):  
Logistic Regression ◽  
Statistical Method ◽  
Traffic Accident ◽  
Ordinal Logistic Regression ◽  
Ordinal Scale ◽  
Predictor Variables ◽  
The Status ◽  
Minor Injuries ◽  
Serious Injuries

Ordinal logistic regression is a statistical method for analyzing the respone variables that have an ordinal scale consisting of three or more categories. This method is an extension of logistic regression with a binary respone variable. In this study the cases studies was the severity of traffic accident victims in Buleleng. The severity of the victims were divided into three categories: minor injuries, serious injuries and died. This research also used six predictor variables, namely age, hours of accident, education, gender, the status of location, and the venicles involved. Result of study shows that the variables age, hours of accident, education and the status of location have a significan effect on the severity of traffic accident victims.

scholarly journals Ordinal Logistic Regression with an Application to Health Service Quality in Raden Mattaher Jambi Hospital

2019 ◽  
Vol 20 (1) ◽  
pp. 35-40
Author(s):  
Feri Tiona Pasaribu ◽  
Ainun Mardia ◽  
Corry Sormin
Keyword(s):  
Logistic Regression ◽  
Health Services ◽  
Ordinal Logistic Regression ◽  
Ordinal Scale ◽  
Test Parameter ◽  
Physical Evidence ◽  
Level Of Satisfaction ◽  
Ordinal Logistic Regression Analysis ◽  
Quality Of Health Services

Health care is a treatment received by the community provided by health workers. The quality of health services will provide satisfaction to health services. Service actions that affect the value of satisfaction are availability and completeness of facilities, physical evidence of administration, reliability of officers, responsiveness of officers, guarantees received by patients and families of patients, and empathy felt by patients. This level of satisfaction assessment is in the form of an ordinal scale that is not satisfied, less satisfied, satisfied and very satisfied. The analysis used to determine the level of satisfaction is by ordinal logistic regression analysis. The sample used in this study is the patient or family of patients at Raden Mattaher Hospital. The stages of data analysis used are the validity and reliability test, parameter estimation, model feasibility test, parameter significance test and the best model selection. The results of the analysis obtained the best models  and  with factors that influence the level of quality of health services at Raden Mattaher Hospital, namely physical evidence, availability and completeness of facilities and responsiveness.

scholarly journals Bootstrap Logistic Regression on Determining Factors Affecting the Level of Entrepreneurial Capability

2019 ◽  
Vol 5 (1) ◽  
pp. 10-18
Author(s):  
Muhammad Ridho ◽  
Dodi Devianto
Keyword(s):  
Logistic Regression ◽  
Rural Area ◽  
Ordinal Scale ◽  
Predictor Variables ◽  
Response Variable ◽  
Factors Affecting ◽  
Bootstrap Estimation ◽  
Social Motive ◽  
Determining Factors ◽  
West Sumatra

The purpose of this study is to determine the factors that affect the level of entrepreneurial capability in tourism of rural area in Nagari Salayo of West Sumatra. The level of entrepreneurial capability is the response variable in this study with an ordinal scale consisting of four categories, they are lower, middle, high, or very high. Whereas the predictor variables consist of 4 socio-demographic factor variables, they are gender, education level, age group and occupation, and also 5 entrepreneurial motivation variables. To determine the predictor variables that are significantly affecting response variables, an ordinal logistic regression with a bootstrap estimation is executed. The study’s result shows two predictor variables that affect the response variable significantly, they are the entrepreneurial motive and social motive with the hit ratio of 61,667%. With that result, the model formed by bootstrapping logistic regression is able to determine the level of entrepreneurial capability in tourism of the rural area.

scholarly journals PEMODELAN INDEKS PEMBANGUNAN MANUSIA PROVINSI JAWA BARAT, JAWA TIMUR DAN JAWA TENGAH TAHUN 2019 DENGAN MENGGUNAKAN METODE REGRESI LOGISTIK ORDINAL

2021 ◽  
Vol 10 (1) ◽  
pp. 149-158
Author(s):  
Meylita Sari ◽  
Purhadi Purhadi
Keyword(s):  
Economic Growth ◽  
Growth Rate ◽  
Logistic Regression ◽  
Unemployment Rate ◽  
Classification Accuracy ◽  
Poverty Rate ◽  
Ordinal Logistic Regression ◽  
Predictor Variables ◽  
Clean Water ◽  
Economic Growth Rate

Ordinal logistic regression is one of the statistical methods to analyze response variables (dependents) that have an ordinal scale consisting of three or more categories. Predictor variables (independent) that can be included in the model are category or continuous data consisting of two or more  variables. Human Development Index (HDI) is an indicator of the success of human development in a region and can be categorized into medium, high and very high. Based on the further categorization, in this study would like to know more about the HDI model using the Ordinal Logistic Regression method, with predictor variables that are suspected to affect, so that it is obtained in West Java Province is influenced by variable poverty rates and clean water sources with a classification accuracy value of 77.78%, Central Java Province is influenced by variable economic growth rate based on constant price GDP, poverty rate and open unemployment rate with a classification accuracy value of 82.85%. East Java province is influenced by variable poverty rate and open unemployment rate with a classification accuracy value of 76.31%. As well as in the three provinces in Java Island is influenced by variable economic growth rate, variable poverty rate, variable clean water source with a classification accuracy value of 73%. Keywords : Ordinal Logistic Regression, HDI, Classification Accuracy

Modelling Information Search Behaviour of Car Purchasers

1989 ◽  
Vol 19 (3) ◽  
pp. 138-143 ◽  
Author(s):  
Phillip J. Du Plessis ◽  
Michael J. Greenacre
Keyword(s):  
Logistic Regression ◽  
Information Search ◽  
Ordinal Logistic Regression ◽  
Predictor Variables ◽  
Sources Of Information ◽  
Search Behaviour ◽  
Information Usage ◽  
Used Car ◽  
External Sources ◽  
First Time

The objective in the study was to establish whether there was any relationship between certain information usage categories and four selected predictor variables namely (1) new or used car purchase, (2) other-than-white or white buyer, (3) male or female, and (4) first-time buyer or experienced buyer. Certain external sources of information (non-market dominated and market dominated) which are available to the buyer of a car and the development of a model of the probability of buyers using the source are investigated. The technique of ordinal logistic regression is assumed to be the appropriate modelling tool in this study where the response variables of interest are ordinal.

Identifying meaningful neighbors for an improved recommender system

2017 ◽  
Vol 12 (2) ◽  
pp. 243-264 ◽  
Author(s):  
Rahul Kumar ◽  
Pradip Kumar Bala
Keyword(s):  
Logistic Regression ◽  
Linear Models ◽  
Ordinal Logistic Regression ◽  
Ordinal Scale ◽  
Data Set ◽  
New Approach ◽  
Content Type ◽  
The One ◽  
Word Of Mouth Communication ◽  
Item Item

Purpose Collaborative filtering (CF), one of the most popular recommendation techniques, is based on the principle of word-of-mouth communication between other like-minded users. The process of identifying these like-minded or similar users remains crucial for a CF framework. Conventionally, a neighbor is the one among the similar users who has rated the item under consideration. To select neighbors by the existing practices, their similarity deteriorates as many similar users might not have rated the item under consideration. This paper aims to address the drawback in the existing CF method where “not-so-similar” or “weak” neighbors are selected. Design/methodology/approach The new approach proposed here selects neighbors only on the basis of highest similarity coefficient, irrespective of rating the item under consideration. Further, to predict missing ratings by some neighbors for the item under consideration, ordinal logistic regression based on item–item similarity is used here. Findings Experiments using the MovieLens (ml-100) data set prove the efficacy of the proposed approach on different performance evaluation metrics such as accuracy and classification metrics. Apart from higher prediction quality, coverage values are also at par with the literature. Originality/value This new approach gets its motivation from the principle of the CF method to rely on the opinion of the closest neighbors, which seems more meaningful than trusting “not-so-similar” or “weak” neighbors. The static nature of the neighborhood addresses the scalability issue of CF. Use of ordinal logistic regression as a prediction technique addresses the statistical inappropriateness of other linear models to make predictions for ordinal scale ratings data.

Predict the Medicare Functional Classification Level (K-level) using the Amputee Mobility Predictor in people with unilateral transfemoral and transtibial amputation: A pilot study

2017 ◽  
Vol 42 (2) ◽  
pp. 191-197 ◽  
Author(s):  
Michael P Dillon ◽  
Matthew J Major ◽  
Brian Kaluf ◽  
Yuri Balasanov ◽  
Stefania Fatone
Keyword(s):  
Logistic Regression ◽  
Pilot Study ◽  
Patient Characteristics ◽  
Ordinal Logistic Regression ◽  
Predictor Variables ◽  
Functional Classification ◽  
Transfemoral Amputation ◽  
Potential Predictor ◽  
Younger Age ◽  
Representative Samples

Background: While Amputee Mobility Predictor scores differ between Medicare Functional Classification Levels (K-level), this does not demonstrate that the Amputee Mobility Predictor can accurately predict K-level. Objectives: To determine how accurately K-level could be predicted using the Amputee Mobility Predictor in combination with patient characteristics for persons with transtibial and transfemoral amputation. Study design: Prediction. Method: A cumulative odds ordinal logistic regression was built to determine the effect that the Amputee Mobility Predictor, in combination with patient characteristics, had on the odds of being assigned to a particular K-level in 198 people with transtibial or transfemoral amputation. Results: For people assigned to the K2 or K3 level by their clinician, the Amputee Mobility Predictor predicted the clinician-assigned K-level more than 80% of the time. For people assigned to the K1 or K4 level by their clinician, the prediction of clinician-assigned K-level was less accurate. The odds of being in a higher K-level improved with younger age and transfemoral amputation. Conclusion: Ordinal logistic regression can be used to predict the odds of being assigned to a particular K-level using the Amputee Mobility Predictor and patient characteristics. This pilot study highlighted critical method design issues, such as potential predictor variables and sample size requirements for future prospective research. Clinical relevance This pilot study demonstrated that the odds of being assigned a particular K-level could be predicted using the Amputee Mobility Predictor score and patient characteristics. While the model seemed sufficiently accurate to predict clinician assignment to the K2 or K3 level, further work is needed in larger and more representative samples, particularly for people with low (K1) and high (K4) levels of mobility, to be confident in the model’s predictive value prior to use in clinical practice.

scholarly journals Penerapan Regresi Logistik Ordinal Proportional Odds Model pada Analisis Faktor-Faktor yang Mempengaruhi Kelengkapan Imunisasi Dasar Anak Balita di Provinsi Aceh Tahun 2015

2017 ◽  
Vol 10 (1) ◽  
pp. 37
Author(s):  
Budyanra Budyanra ◽  
Ghaida Nasria Azzahra
Keyword(s):  
Logistic Regression ◽  
Odds Ratio ◽  
Maternal Education ◽  
Immunization Coverage ◽  
Ordinal Logistic Regression ◽  
Proportional Odds Model ◽  
Proportional Odds ◽  
Number Of Children ◽  
Ordinal Logistic Regression Analysis ◽  
The Status

Province of Aceh has basic immunization coverage toddler lowest in Indonesia in 2015. even though, this province has Posyandu and Puskesmas ratio per population of the highest in the western region of Indonesia. This data their concerns regarding immunization coverage has not been handled well in Aceh Province. This papers aims to identify variables that affect the status of complete basic immunization of children aged 12-59 months in Aceh by using ordinal logistic regression analysis. Ordinal logistic regression model used is proportional odds models. Data are obtained from Susenas 2015 that was held in March 2015 by BPS-Statistic of Indonesia. Based on the results of processing data, known only 37.7% of children aged 12-59 months in the province of Aceh in 2015 which gets fully immunized, the remaining 50.6% receive primary immunization but is not complete, even about 11.7% have not received basic immunization at all. From the proportional odds model results showed that the number of children born to mothers (odds ratio = 0.88), maternal age at delivery (odds ratio = 1.03), the level of maternal education (odds ratio = 1.22), and the educational level of the household (odds ratio = 1,2) have a significant impact on the status of complete basic immunization of children. Future studies are expected to include the element of timeliness and add other variables and also with other models in ordinal logistic regression.Keywords:Immunization, Ordinal Logistic Regression, Proportional Odds, Susenas

scholarly journals Potential Traffic Accident Analysis of Bangkinang – Rantau Berangin Road Riau Province, Indonesia

2021 ◽  
Vol 2 (1) ◽  
pp. 27-33
Author(s):  
Abdul Kudus Zaini ◽  
Abdussalam
Keyword(s):  
Traffic Accident ◽  
Traffic Accidents ◽  
Black Spot ◽  
Accident Rate ◽  
Road Users ◽  
Property Loss ◽  
Minor Injuries ◽  
Serious Injuries ◽  
The Village ◽  
Calculation Analysis

A traffic accident is an unexpected and unintentional event involving a vehicle with or without other road users, resulting in human casualties (minor injuries, serious injuries, and death) and property loss. In penel itian have taken vertebra way the segments road Bangkinang - Rantau Berangin KM 60 to KM 100 . This study aims for know se how much the level of traffic accidents in the area and for get The value of Accident Rate is Black Spot and Black Site on road Bangkinang-Rantau Berangin.The method used in this research is the Calculation Analysis method by identifying the Black Spot and Black Site based on the Accident Rate . The results of the analysis of data years 2014 - 2018 indicates that the segments road Bangkinang - Rantau Berangin can be identified value Accident rate which is Black Spot is the village of Pulau Gadang - Tanjung Alai with accident rate amounted to 2,069 and also the village of Tanjung Alai – Batu Bersurat with grades Accident rate amounted to 2,069. While for Accident rate against Black Site among others , namely the segment road Tanjung Alai – Batu Bersurat with Accident rate of 0.295 followed by the village of Pulau Gadang - Tanjung Alai is 0,258 and followed the village of Kuok - Village Lereng with Accident rate 0,240. From the analysis the researchers concluded that there are three locations which are the areas rawaan traffic accidents on roads Bangkinang - namely Rantau Berangin Village Tanjung Alai - Village Batu Bersurat , the village of Pulau Gadang - Tanjung Alai and villages Kuok - Village Lereng

scholarly journals PEMODELAN FAKTOR - FAKTOR PENYEBAB KEPARAHAN KORBAN KECELAKAAN LALU LINTAS DI KOTA AMBON DENGAN MENGGUNAKAN MODEL REGRESI LOGISTIK MULTINOMIAL

2019 ◽  
Vol 1 (1) ◽  
pp. 17-26
Author(s):  
Miranti Miranti ◽  
F. Y. Rumlawang ◽  
F. Kondolembang
Keyword(s):  
Logistic Regression ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Multinomial Logistic Regression ◽  
Regression Method ◽  
Predictor Variables ◽  
Death Toll ◽  
Logistic Regression Method ◽  
Significant Factors ◽  
Response Variables

Traffic accidents are one of the main causes of the highest increase in mortality in Indonesia. This problem needs attention to anticipate the fall of the death toll in a traffic accident. So in this study, there are response variables and several predictor variables. The purpose of this study was to find out what factors influence the severity of traffic accident victims in Ambon city based on categories and model the severity of traffic accident victims in Ambon city based on significant factors using the Multinomial Logistic Regression method. In this study, the results obtained are factors that significantly affect the severity of the traffic accident victims are sex variables (X1), age (X1), education (X3) and type of vehicle (X5).

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