scholarly journals Study on the Influence of Air Pressure and Temperature on PM2.5 by Multivariate Functional Linear Regression Model

2020 ◽  
Vol 194 ◽  
pp. 05009
Author(s):  
Jinjing Yang
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Regression Model ◽  
Functional Data Analysis ◽  
Linear Regression Model ◽  
Functional Data ◽  
Basis Functions ◽  
Air Pressure ◽  
The Internet ◽  
Functional Linear Regression

In recent years, the Internet has developed rapidly, and we have more and more ways to collect data. We find that many data have the characteristics of functions. We can use the important method of functional data analysis to analyze these data. The basic idea of functional data analysis is to treat data with functional properties as a whole for analysis and corresponding processing. In this paper, the daily air pressure, temperature and PM2.5 data of 49 cities with serious PM2.5 pollution in 2017 are sorted out. We use a multivariate functional linear regression model to discuss the influence of pressure and temperature on PM2.5 when the number of basis functions is different.

scholarly journals A Literature Review of Semi-functional Partial Linear Regression Models

2021 ◽  
Author(s):  
Mohammad Fayaz
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Missing Values ◽  
Electricity Consumption ◽  
Confidence Regions ◽  
Mixed Data ◽  
Linear Regression Models ◽  
Partial Linear ◽  
Functional Linear Regression

Background: In the functional data analysis (FDA), the hybrid or mixed data are scalar and functional datasets. The semi-functional partial linear regression model (SFPLR) is one of the first semiparametric models for the scalar response with hybrid covariates. Various extensions of this model are explored and summarized. Methods: Two first research articles, including “semi-functional partial linear regression model”, and “Partial functional linear regression” have more than 300 citations in Google Scholar. Finally, only 106 articles remained according to the inclusion and exclusion criteria such as 1) including the published articles in the ISI journals and excluding 2) non-English and 3) preprints, slides, and conference papers. We use the PRISMA standard for systematic review. Results: The articles are categorized into the following main topics: estimation procedures, confidence regions, time series, and panel data, Bayesian, spatial, robust, testing, quantile regression, varying Coefficient Models, Variable Selection, Single-index model, Measurement error, Multiple Functions, Missing values, Rank Method and Others. There are different applications and datasets such as the Tecator dataset, air quality, electricity consumption, and Neuroimaging, among others. Conclusions: SFPLR is one of the most famous regression modeling methods for hybrid data that has a lot of extensions among other models.

Faktor-Faktor Yang Mempengaruhi Permintaan Tahu Jawa

2020 ◽  
Vol 2 (2) ◽  
pp. 76-85
Author(s):  
Hotman tuah ◽  
Marlan ◽  
Fitria Nazar
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Confidence Level ◽  
Household Income ◽  
Family Income ◽  
Family Members ◽  
Analysis Method ◽  
Income Family

Tujuan penelitian ini adalah untuk menganalisis bagaimana pengaruh harga tahu jawa, pendapatan rumah tangga, jumlah tanggungan dan harga tempeterhadap permintaan tahu jawa di Kota Pematangsiantar.Metode analisis data yang digunakan adalah model regresi linier berganda yang diolah dengan program SPSS 23 dengan pengujian hipotesis yang terdiri dari koefisien (R2), uji F, dan uji t. Harga tahu jawa, pendapatan keluarga, jumlah anggota keluarga, dan harga tempe  mampu  menjelaskan variasi permintaan sebesar 46,6%, sedangkan  sisanya sebesar 53,4% & dijelaskan oleh faktor-faktor lain yang tidak disertakan dalam persamaan. Secara bersama-sama,variabel harga tahu jawa,pendapatan rumah tangga, dan jumlah anggota keluarga, dan harga tempe  berpengaruh secara tidak nyata terhadap permintaan tahu jawa. Secara parsial, pendapatan konsumen berpengaruh nyata terhadap permintaan tahu jawa pada tingkat kepercayaan 95% Nilai thitung (2,216) dan hipotesis dapat diterima. sedangkan  harga tahu jawa, harga tempe, jumlah anggota keluarga tidak berpengaruh nyata terhadap permintaan tahu jawa.    ABSTRACT  The purpose of this research is to analyze how the influence of Javanese tofu price, household income, number of dependents and price of tempeh to the request of tofu Jawa in Pematangsiantar city. The data analysis method used is a double linear regression model that is processed with the SPSS 23 program with hypothesis testing consisting of coefficient (R2), test F, and T test. Javanese tofu prices, family income, family members, and Tempe prices were able to explain the variation in demand by 46.6%, while the remaining of 53.4% & explained by other factors not included in the equation. Together, variable prices of Javanese tofu, household income, and the number of family members, and the price of Tempe effect is not noticeable to the demand for Javanese tofu. Partially, the consumer income has a real impact on the demand for Javanese tofu at a confidence level of 95% of the Thitung value (2.216) and the hypothesis acceptable. While the price of Tofu Jawa, Tempe Price, the number of family members does not affect the demand for Javanese tofu.

scholarly journals On estimation in a spatial functional linear regression model with derivatives

2018 ◽  
Vol 356 (5) ◽  
pp. 558-562
Author(s):  
Stéphane Bouka ◽  
Sophie Dabo-Niang ◽  
Guy Martial Nkiet
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Functional Linear Regression
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