Hypothesis Testing in Large-scale Functional Linear Regression

2021 ◽  
Author(s):  
Kaijie Xue ◽  
Fang Yao
Keyword(s):  
Linear Regression ◽  
Hypothesis Testing ◽  
Large Scale ◽  
Functional Linear Regression

Maintaining Customer Loyalty by Intensifying Service Quality and Customer Satisfaction in Commuter Line Jabodetabek

2019 ◽  
Vol 10 (9) ◽  
pp. 902-909
Author(s):  
Umbas Krisnanto ◽  
◽  
Conny Marpaung ◽  
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Customer Satisfaction ◽  
Service Quality ◽  
Hypothesis Testing ◽  
Customer Loyalty ◽  
T Test ◽  
Coefficient Of Determination ◽  
Simple Linear Regression ◽  
Significance Level

This study aims to determine and analyze the influence of Service Quality and Customer Satisfaction on Customer Loyalty in Jabodetabek Commuter Line. The sample of this study was 50 people. Methods of collecting data by distributing questionnaires. Data analysis using the analysis used is simple linear regression, t test and coefficient of determination. The results showed 1) Service Quality has a positive and significant effect on Customer Loyalty in Jabodetabek Commuter Line, with a significance level of 0.048; and supported by the results of hypothesis testing with a t-count value of 4.433 > t-table value of 1.95, with a significance of 0.048 or < 0.05; 2) Customer Satisfaction positive and significant effect on Customer Loyalty in Jabodetabek Commuter Line, with a level significance of 0,000; and supported by the results of hypothesis testing with a t-count value of 4,969 > t-table value of 1.95, with a significance of 0,000 or < 0.05, 3) Service quality and Customer Satisfaction have a positive and significant effect on Customer Loyalty in Jabodetabek Commuter Line, with a significance level of 0,000. This means that the hypothesis H0 is rejected and Ha is accepted so that it can be concluded that service quality and customer satisfaction together have a positive and significant effect on customer loyalty in Jabodetabek Commuter Line.

PENGARUH HARGA DAN PEMAKAIAN PERSEDIAAN BAHAN BAKU TERHADAP VOLUME PRODUKSI PADA PT. KELOLA MINA LAUT DI GRESIK

MANAJERIAL ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 66
Author(s):  
BAYU YRI WIDHARTO
Keyword(s):  
Linear Regression ◽  
Hypothesis Testing ◽  
Multiple Linear Regression ◽  
Raw Materials ◽  
T Test ◽  
Raw Material ◽  
Analysis Tool ◽  
Production Volume ◽  
F Test ◽  
Material Price

The purpose of the research was to know the affect of many factors which affected to the production volume in PT. Kelola Mina Laut Gresik. What the price of raw materials was and the used of raw materials partially and simultan eously affected on the production volume. The analysis tool which used was a model of multiple linear regression. Hypothesis testing used t test and F test, both at the significant level 5%. Based of the analysis of research on PT Kelola Mina Laut Gresik. Partially, inventory raw material price had not significant effect on the production volume, consumption of raw material inventory affected significantly of the production volume. Inventory of raw material price and the use of raw material simultan eously affect significantly to the production volume.

scholarly journals QUBO formulations for training machine learning models

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Prasanna Date ◽  
Davis Arthur ◽  
Lauren Pusey-Nazzaro
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Large Scale ◽  
Support Vector ◽  
Quantum Computers ◽  
Np Hard ◽  
Learning Models ◽  
Moore’S Law ◽  
Moore's Law ◽  
Machine Learning Models

AbstractTraining machine learning models on classical computers is usually a time and compute intensive process. With Moore’s law nearing its inevitable end and an ever-increasing demand for large-scale data analysis using machine learning, we must leverage non-conventional computing paradigms like quantum computing to train machine learning models efficiently. Adiabatic quantum computers can approximately solve NP-hard problems, such as the quadratic unconstrained binary optimization (QUBO), faster than classical computers. Since many machine learning problems are also NP-hard, we believe adiabatic quantum computers might be instrumental in training machine learning models efficiently in the post Moore’s law era. In order to solve problems on adiabatic quantum computers, they must be formulated as QUBO problems, which is very challenging. In this paper, we formulate the training problems of three machine learning models—linear regression, support vector machine (SVM) and balanced k-means clustering—as QUBO problems, making them conducive to be trained on adiabatic quantum computers. We also analyze the computational complexities of our formulations and compare them to corresponding state-of-the-art classical approaches. We show that the time and space complexities of our formulations are better (in case of SVM and balanced k-means clustering) or equivalent (in case of linear regression) to their classical counterparts.

Functional Linear Regression

2018 ◽  
Author(s):  
Hervé Cardot ◽  
Pascal Sarda
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Linear Models ◽  
Estimation Error ◽  
Asymptotic Properties ◽  
Principal Component Regression ◽  
Principal Component ◽  
Penalized Least Squares ◽  
Open Problems ◽  
Functional Linear Regression

This article presents a selected bibliography on functional linear regression (FLR) and highlights the key contributions from both applied and theoretical points of view. It first defines FLR in the case of a scalar response and shows how its modelization can also be extended to the case of a functional response. It then considers two kinds of estimation procedures for this slope parameter: projection-based estimators in which regularization is performed through dimension reduction, such as functional principal component regression, and penalized least squares estimators that take into account a penalized least squares minimization problem. The article proceeds by discussing the main asymptotic properties separating results on mean square prediction error and results on L2 estimation error. It also describes some related models, including generalized functional linear models and FLR on quantiles, and concludes with a complementary bibliography and some open problems.

Principal component analysis for functional data

2018 ◽  
Author(s):  
Peter Hall
Keyword(s):  
Principal Component Analysis ◽  
Linear Regression ◽  
Functional Data ◽  
Weighted Least Squares ◽  
Principal Component ◽  
Adaptive Methods ◽  
Component Analysis ◽  
Infinite Dimensional ◽  
Role Of Principal ◽  
Functional Linear Regression

This article discusses the methodology and theory of principal component analysis (PCA) for functional data. It first provides an overview of PCA in the context of finite-dimensional data and infinite-dimensional data, focusing on functional linear regression, before considering the applications of PCA for functional data analysis, principally in cases of dimension reduction. It then describes adaptive methods for prediction and weighted least squares in functional linear regression. It also examines the role of principal components in the assessment of density for functional data, showing how principal component functions are linked to the amount of probability mass contained in a small ball around a given, fixed function, and how this property can be used to define a simple, easily estimable density surrogate. The article concludes by explaining the use of PCA for estimating log-density.

scholarly journals The Influence Of Motivation And Discipline On The Work Productivity Of Employees In Camat Office Of Sukaraja District, Seluma Province, Bengkulu Province

2021 ◽  
Vol 2 (1) ◽  
pp. 1-12
Author(s):  
Ida Ayu Made Er Meytha Gayatri ◽  
Mimi Kurnia Nengsih
Keyword(s):  
Linear Regression ◽  
Hypothesis Testing ◽  
Multiple Linear Regression ◽  
Work Productivity ◽  
Positive Influence ◽  
Regression Equation ◽  
Head Office ◽  
Regression Test

The purpose of this study was to determine factors such as motivation and work discipline tahat affect the work prductivity of employees at the Head Office of Sukaraja District, Seluma Regency. The sample in this study was 29 employees at the Head Office of Sukaraja District, Seluma Regency. Data collectiion using a questionnaire and the method of analysis used is multiple linear regression, test of determination and hypothesis testing. The result of the regression equation is Y = n10,107 + 0,417 X1 + 0,361X2 + e, which means that motivation and work discipline have a positive influence of the work productivity of employees at the Head Office of Sukaraja District, Seluma Regency. The results of the determination test was 0,567 0r (56,7%). This shows that motivation and work discipline have an effect on the work  productivity of employees at the Subdistrict Head Office of Sukaraja District, Seluma Regency by 56,7 % while the rest are explained or influenced by other variables not examined.

scholarly journals Analisis Kinerja Anggaran Pemerintah Provinsi Sumatera Selatan Dengan Pendekatan Konsep Value For Money

2021 ◽  
Vol 7 (1) ◽  
pp. 1-10
Author(s):  
Periansya Periansya ◽  
Dendy Pratama ◽  
Rosi Armaini
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Hypothesis Testing ◽  
Multiple Linear Regression ◽  
Sample Selection ◽  
Value For Money ◽  
Selection Technique ◽  
A Value ◽  
Analysis Technique ◽  
Independent Variable

This research is intended as an effort to analyze the budget performance of Regional Apparatus Organizations in South Sumatra Province with a value for money concept approach. This study consisted of the dependent variable, namely the performance of the regional apparatus organization budget, while the independent variable consisted of transparency, accountability, and supervision. The data used in this research is the result of distributing questionnaires to selected respondents in 21 Regional Apparatus Organizations. The sample selection technique used purposive sampling, while the data analysis technique used multiple linear regression. The results of the analysis and discussion of hypothesis testing show that partially transparency, accountability, and supervision have a positive and significant effect on-budget performance, including simultaneously also showing a positive and significant effect on-budget performance, but it is necessary to increase budget performance, this shows that the R2 value is 0.446. or 44.6%.

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