Logistic Regression and Path Analysis Method to Analyze Factors influencing Students’ Achievement

Noeryanti, N; Suryowati, Kris; Setyawan, Yudi; Aulia, Riza

doi:10.1088/1757-899x/335/1/012044

Cited by 2 publications

(1 citation statement)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several previous studies showed that the biplot analysis method was applied to several studies including Biplot Analysis on Mapping Characteristics of Poverty in Maluku Province (Leleury & Wokanubun, 2015), Biplot Analysis Approach to the Description of Relative Position Between SMAs in Denpasar City (Melissa, 2012), Biplot Analysis on Characteristics Mapping Poverty in North Sulawesi Province (Padang, et al, 2019), and Grouping of Ultra Hight Tempherature Milk Brands Based on Nutritional Content Using Biplot Analysis and apart from biplot analysis (Putri & Nanang, 2020), Pat analysis can also be applied such as in Logistic Regression and Path Analysis Method to Analyze Factors influencing Students Achievement (Noeryanti, et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Application Biplot Analysis on Mapping of Non-Convertive Diseases in Indonesia

Suryowati

Titah

Nasution

2021

View full text Add to dashboard Cite

Non-communicable diseases is diseases that are not caused by germs but rather because of physiological or metabolic problems in human body tissues. Usually, this disease occurs due to unhealthy lifestyle. One way to find out how large the spread of non-communicable diseases is by mapping the disease using biplot analysis. Biplot analysis is applied to determine the proximity information between objects, the length of the change vector, the correlation between modifiers, and the value of the change in an object. The study was conducted in 33 provinces with twelve non-communicable diseases. Descriptive analysis of twelve non-communicable diseases averaged the highest joint disease of 10.51 followed by hypertensive disease 8.85 and Stroke 6.42. While the lowest average disease is Heart Failure disease by 0.10 it is still open to research with other methods and also need to add supporting variables

show abstract

Section: Introductionmentioning

confidence: 99%