On the nonparametric classification and regression methods for multivariate EAS data analysis

1997 ◽  
Vol 52 (3) ◽  
pp. 237-239 ◽  
Author(s):  
A. Chilingarian ◽  
S. Ter-Antonyan ◽  
A. Vardanyan ◽  
H.J. Gils ◽  
J. Knapp ◽  
...  
Keyword(s):  
Data Analysis ◽  
Regression Methods ◽  
Nonparametric Classification ◽  
Classification And Regression

scholarly journals Pengaruh Religiusitas, Sikap, Norma Subjektif, dan Kontrol Perilaku terhadap Niat Membeli Produk Kosmetik Halal di Fakultas Ekonomi dan Bisnis Universitas Ahmad Dahlan Yogyakarta

2020 ◽  
Vol 11 (1) ◽  
pp. 49
Author(s):  
Reza Rizki Nur Ikhsan ◽  
Sukardi Sukardi
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Behavioral Control ◽  
Subjective Norm ◽  
Subjective Norms ◽  
Cosmetic Products ◽  
Validity And Reliability ◽  
F Test ◽  
Muslim Students ◽  
Regression Methods

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>ABSTRACT</span></p><p><span>This study aims to determine the effect of religiosity, attitudes, subjective norms and behavioral control on the intention to buy halal cosmetic products on Muslim students at the Faculty of Economics and Business, Ahmad Dahlan University, Yogyakarta. This research is a survey research using a questionnaire as an instrument. For testing the instrument using a test of validity and reliability. As for the technique of data analysis using multiple linear regression methods, because the variables used are more than two variables, with the F test and t test. The results of the study showed that the religiosity variable (X1) did not significantly influence the intention to buy halal cosmetics, this was indicated by the significance value&gt;α(0.24)&gt;0.05) attitude (X2) had no significant effect on the intention to buy halal cosmetics, this is shown from the significance value&gt;α(0.509&gt;0.05), subjective norm (X3) does not affect the intention to buy halal cosmetics, this is indicated from the significance value&gt;α(0,250&gt;0.05), behavioral control (X4) influences on the interest in buying halal cosmetics, this is indicated by the significance value&lt;α(0,000&lt;0.05). Simultaneously the influence of religiosity, attitudes, subjective norms, and behavioral control on the intention to buy halal cosmetic products at the Faculty of Economics and Business Ahmad Dahlan University has a significant effect on the intention to buy halal cosmetics (Y). This is evidenced by the calculation of the F test of 28,135 with a significance of 0,000.</span></p><p><span>Keywords</span><span>: </span><span>Religiosity, Attitude, Intention, Halal Cosmetics </span><span>ABSTRAK</span></p><p><span>Penelitian ini bertujuan untuk mengetahui pengaruh religiusitas, sikap, norma subyektif dan kontrol perilaku terhadap niat membeli produk kosmetik halal pada mahasiswi muslim di Fakultas Ekonomi dan Bisnis Universitas Ahmad Dahlan Yogyakarta. Penelitian ini merupakan penelitian survei dengan menggunakan kuesioner sebagai instrumennya. Untuk pengujian instrumen menggunakan uji validitas dan reliabilitas. Sedangkan untuk Teknik analisis data menggunakan metode regresi linier berganda, karena variabel yang digunakan lebih dari dua variabel, dengan uji F dan uji t. Hasil dari penelitian menunjukkan bahwa variabel religiusitas (X</span><span>1</span><span>) tidak berpengaruh signifikan terhadap minat beli kosmetik halal, hal ini ditunjukkan dari nilai signifikansi&gt;α(0,24&gt;0,05) sikap (X</span><span>2</span><span>) tidak berpengaruh signifikan terhadap minat beli kosmetik halal, hal ini ditunjukkan dari nilai signifikansi&gt;α(0,509&gt;0,05), norma subjektif (X</span><span>3</span><span>) tidak berpengaruh terhadap minat beli kosmetik halal, hal ini ditunjukkan dari nilai signifikansi&gt;α(0,250)&gt;0,05), kontrol perilaku (X</span><span>4</span><span>) berpengaruh terhadap minat beli kosmetik halal, hal ini ditunjukkan dari nilai signifikansi&lt;α(0,000&lt;0,05). Secara simultan pengaruh religiusitas, sikap, norma subjektif, dan kontrol perilaku terhadap niat membeli produk kosmetik halal di Fakultas Ekonomi dan Bisnis Universitas Ahmad Dahlan berpengaruh signifikan terhadap niat membeli kosmetik halal (Y). Hal ini terbukti dengan perhitungan uji F sebesar 28,135 dengan signifikansi 0,000.</span></p></div></div><img src="blob:http://ejournal.uigm.ac.id/d2905649-5db7-4cde-b6fb-18abb055c78e" alt="page1image51464640" width="101.040000" height="0.480000" /><div class="layoutArea"><div class="column"><p><span>Kata Kunci: </span><span>Religiusitas, Sikap, Niat, Kosmetik Halal</span></p></div></div></div>

scholarly journals PENGARUH KOMITE AUDIT, RETURN ON ASSETS (ROA), DAN LEVERAGE TERHADAP PENGHINDARAN PAJAK

2019 ◽  
Vol 3 (01) ◽  
pp. 9-20
Author(s):  
Fabia Tiala ◽  
Ratnawati Ratnawati ◽  
M.Taufiq Noor Rokhman
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Audit Committee ◽  
Stock Exchange ◽  
Tax Avoidance ◽  
Financial Statements ◽  
Manufacturing Companies ◽  
Return On Assets ◽  
Regression Methods ◽  
Dependent Variables

This study aims to test and describe Tax Avoidance which is influenced by the Audit Committee, Return On Assets (ROA), and Leverage. This study uses two variables, namely the independent and dependent variables, and the source of data in this study in the form of financial statements of mining sector manufacturing companies listed on the Indonesia Stock Exchange (BEI) in 2015-2017. Data analysis was performed by classical assumption test and hypothesis testing in this study using multiple linear regression methods. The results of this study indicate that partially the Audit Committee and Leverage variables have a significant effect on tax avoidance, while the Return on Assets (ROA) does not affect tax avoidance.

scholarly journals Fair Trade Certification and Livelihoods: A Panel Data Analysis of Coffee-growing Households in India

2016 ◽  
Vol 45 (3) ◽  
pp. 436-458 ◽  
Author(s):  
Sabina Khatri Karki ◽  
Pradyot Ranjan Jena ◽  
Ulrike Grote
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Fair Trade ◽  
Panel Data Analysis ◽  
Small Scale ◽  
Bottom Of The Pyramid ◽  
Regression Methods ◽  
Fair Trade Coffee ◽  
Participation Decision ◽  
Positive Effect

This study analyzes the participation decision and income impacts of fair-trade coffee certification on small-scale coffee producers in the Araku valley in India using panel data for 183 households and endogenous-switching and quantile regression methods. The results show that fair trade certification has a positive effect on income; the income of certified farmers is 17 percent higher on average than the income of uncertified coffee producers. Furthermore, fair trade certification has a “bottom of the pyramid” effect in that the largest income gains accrue to farmers in the poorer quantiles.

scholarly journals Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges

Materials ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 5764
Author(s):  
Karol Frydrych ◽  
Kamran Karimi ◽  
Michal Pecelerowicz ◽  
Rene Alvarez ◽  
Francesco Javier Dominguez-Gutiérrez ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Multiscale Modeling ◽  
Data Science ◽  
Large Data ◽  
Mechanical Deformation ◽  
Materials Informatics ◽  
Fast Growing ◽  
Regression Methods ◽  
Classification And Regression ◽  
Materials Behavior

In the design and development of novel materials that have excellent mechanical properties, classification and regression methods have been diversely used across mechanical deformation simulations or experiments. The use of materials informatics methods on large data that originate in experiments or/and multiscale modeling simulations may accelerate materials’ discovery or develop new understanding of materials’ behavior. In this fast-growing field, we focus on reviewing advances at the intersection of data science with mechanical deformation simulations and experiments, with a particular focus on studies of metals and alloys. We discuss examples of applications, as well as identify challenges and prospects.

scholarly journals METODE CART UNTUK MENGIDENTIFIKASI FAKTOR-FAKTOR YANG MEMENGARUHI WAKTU PEMBELIAN KENDARAAN KEDUA

2021 ◽  
Vol 10 (2) ◽  
pp. 140-151
Author(s):  
Eka Setiawaty ◽  
Farit Mochamad Afendi ◽  
Cici Suhaeni
Keyword(s):  
Data Analysis ◽  
Regression Tree ◽  
Transaction Data ◽  
Independent Variables ◽  
Group A ◽  
Classification And Regression ◽  
Group B ◽  
The Right ◽  
The Relationship ◽  
Splitting Process

Increased competition between personal vehicle dealers make them need strategies to hold their customers and increase their sales. One of the strategies they could apply is prospecting their customers at the right time. We could predict the right time by identifying the relationship between the length of their purchase time and its factors based on the transaction data of Z Company from year 2002 to 2015 using Classification and Regression Trees (CART). Data analysis is separated between groups of customers who made the second purchase maximum of 10 years after the first purchase (group A) and more than 10 years after the first purchase (group B). Group A’s regression tree produces 8 terminal nodes with MAD value 1.84 years. The independent variables that plays a role are tenor, job, age, and brand. Group B’s regression tree produces 4 terminal nodes. Authorized service and job come out as independent variables which affect the splitting process. MAD value for Group B’s regression tree is 0.56 years.

Supervised Learning in Game Data Science

2021 ◽  
pp. 179-218
Author(s):  
Magy Seif El-Nasr ◽  
Truong Huy Nguyen Dinh ◽  
Alessandro Canossa ◽  
Anders Drachen
Keyword(s):  
Data Science ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Classification Methods ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Regression Methods ◽  
Vector Machines ◽  
Pros And Cons ◽  
Classification And Regression

This chapter discusses several classification and regression methods that can be used with game data. Specifically, we will discuss regression methods, including Linear Regression, and classification methods, including K-Nearest Neighbor, Naïve Bayes, Logistic Regression, Linear Discriminant Analysis, Support Vector Machines, Decisions Trees, and Random Forests. We will discuss how you can setup the data to apply these algorithms, as well as how you can interpret the results and the pros and cons for each of the methods discussed. We will conclude the chapter with some remarks on the process of application of these methods to games and the expected outcomes. The chapter also includes practical labs to walk you through the process of applying these methods to real game data.

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