scholarly journals A Statistical Approach for Predicting Airtightness in Residential Units of Reinforced Concrete Apartment Buildings in Korea

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3598
Author(s):  
Kyung-Hwan Ji ◽  
Hyun-Kook Shin ◽  
Seungwoo Han ◽  
Jae-Hun Jo
Keyword(s):  
Regression Analysis ◽  
Reinforced Concrete ◽  
Correlation Analysis ◽  
Prediction Model ◽  
Statistical Approach ◽  
Prediction Models ◽  
Linear Regression Analysis ◽  
Apartment Buildings ◽  
Residential Units ◽  
Connection Length

In this study, a model equation is derived that uses a statistical analysis based on empirical models to predict the airtightness of reinforced concrete apartment buildings popular in Asian regions. Airtightness data from 486 units personally measured by the authors in the past eight years are used. As major variables used in the prediction model, two groups of variables are configured for the geometric components of the envelope, which is a major path of airflow in a building and is where air infiltration and leakage occur. The two groups of variables represent (1) the areas of the individual components forming the envelope and (2) the connection lengths between different components of the envelope. For the effective prediction of airtightness, correlation analysis and multiple regression analysis were applied step by step in this study. The results of the correlation analysis indicated that the areas of the slab and the window are the area variables that present the greatest impact, whereas the perimeter length of the window is the connection length variable that presents the greatest impact. Using a multiple linear regression analysis method, airtightness prediction model equations can be derived, and it is found that the model with variables for area is able to predict airtightness more accurately compared to the two models derived from variables for connection length and all variables for area and connection length. Although the statistical approach in this study shows a limitation in that the prediction results may vary depending on the attributes and type of data collected by countries, the methodology and procedure in this study contribute to similar studies for making prediction models and finding the influence of variables in the future with high applicability and feasibility.

Analysis of variations in and correlations between selected physical parameters of common beech (Fagus silvatica L.) nuts

2017 ◽  
Vol 1 (21) ◽  
pp. 49-63
Author(s):  
Zdzisław Kaliniewicz ◽  
Piotr Markowski ◽  
Andrzej Anders ◽  
Paweł Tylek ◽  
Zbigniew Krzysiak ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Correlation Analysis ◽  
Cross Sections ◽  
Linear Regression Analysis ◽  
Physical Parameters ◽  
Common Beech ◽  
Mesh Screen ◽  
Before And After ◽  
Fagus Silvatica ◽  
Highly Correlated

The basic dimensions and the mass of common beech nuts and seeds from five nut batches, harvested from tree stands in northern Poland, were determined. Environmental conditions had a greater influence on seed plumpness than the age of tree stands. The results of measurements were analyzed statistically by analysis of variance, correlation analysis and linear regression analysis. Despite differences in their plumpness, nuts were characterized by nearly identical cross-sections which resembled an equilateral triangle. The thickness of nuts and seeds was highly correlated with their mass, and this information can facilitate seed husking and separation into mass categories. Before and after husking, seeds should be separated with the use of a mesh screen with longitudinal openings. Medium-sized (most numerous) seeds were separated into the following plumpness categories using a screen separator with ≠6 mm and ≠7 mm openings: 84% of moderately plump seeds, 3% of seeds with reduced plumpness, and 13% of plump seeds.

scholarly journals HUBUNGAN PERSEPSI TENTANG PROFESI FISIOTERAPI DENGAN MOTIVASI BELAJAR MAHASISWA AKADEMI FISIOTERAPI UKI

2018 ◽  
Vol 10 (3) ◽  
pp. 226
Author(s):  
Maksimus Bisa
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Correlation Analysis ◽  
Correlation Coefficient ◽  
Linear Regression Analysis ◽  
Motivation To Learn ◽  
Simple Linear Regression ◽  
Level 1 ◽  
Relationship Of ◽  
The Relationship

ABSTRACTThis study is descriptive analitik, aims to describe the relationship of perceptions about the physiotherapy profession with the motivation to learn students of the Academy of Physiotherapy UKI. Data collection through questionnaires to students of Physiotherapy Academy UKI level 1, 2, and 3 with a sample of 53 students, then give a score of each statement of questionnaire.The result of correlation analysis shows that p = 0,584> α (0,05) ho is accepted, so there is no significant relationship between the two variables. To measure the closeness and intensity of the relationship between the two variables, test of correlation coefficient and simple linear regression. The result of correlation coefficient test (r) obtained by -0,077, lies below the value of -0.30 (very weak) thus can be said there is no relation between perception about physiotherapy profession with motivation learn student Akfis UKI. Result of linear regression analysis obtained equation: Y = 73,52 + (-0,088) X. This means that every 1 point decrease of perception value will influence motivation value equal to 0,088 times.Keywords: Perception, motivation, physiotherapy profession, and learning achievement. ABSTRAKPenelitian ini bersifat deskriptif analitik, bertujuan untuk mendeskripsikan hubungan persepsi tentang profesi fisioterapi dengan motivasi belajar siswa Akademi Fisioterapi UKI. Pengumpulan data melalui kuesioner kepada siswa Fisioterapi Academy UKI tingkat 1, 2, dan 3 dengan sampel sebanyak 53 siswa, kemudian memberikan skor masing-masing kuesioner pernyataan. Hasil analisis korelasi menunjukkan bahwa p = 0,584> α (0,05) ho diterima, sehingga tidak ada hubungan yang signifikan antara kedua variabel tersebut. Untuk mengukur kedekatan dan intensitas hubungan antara kedua variabel tersebut, uji koefisien korelasi dan regresi linier sederhana. Hasil uji koefisien korelasi (r) diperoleh sebesar -0,077, berada di bawah nilai -0,30 (sangat lemah) sehingga dapat dikatakan tidak ada hubungan antara persepsi tentang profesi fisioterapi dengan motivasi belajar siswa Akfis UKI. Hasil analisis regresi linier diperoleh persamaan: Y = 73,52 + (-0,088) X. Artinya setiap 1 titik penurunan nilai persepsi akan mempengaruhi nilai motivasi sebesar 0,088 kali.Kata kunci: Persepsi, motivasi, profesi fisioterapi, dan prestasi belajar.

scholarly journals The Relationship of Functional Status of Cortisol, Testosterone, and Parameters of Metabolic Syndrome in Male Schizophrenics

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yi Han ◽  
Haifeng Ji ◽  
Li Liu ◽  
Yuncheng Zhu ◽  
Xixi Jiang
Keyword(s):  
Quality Of Life ◽  
Metabolic Syndrome ◽  
Regression Analysis ◽  
Correlation Analysis ◽  
Testosterone Level ◽  
Linear Regression Analysis ◽  
Fasting Blood Glucose ◽  
Metabolic Indices ◽  
The Relationship

Background. The cross-sectional study is aimed at investigating the relationship between cortisol, testosterone, and metabolic characteristics among male schizophrenics. Methods. 174 patients were grouped based on their risk of metabolic syndrome (MetS) into the non-MetS, high-risk-MetS (HR-MetS), or MetS groups. Metabolic indices (body mass index (BMI), mean arterial pressure (MAP), cholesterol, triglyceride, and fasting blood glucose (FBG)) were associated with cortisol and testosterone levels using correlation analysis. Multiple linear regression analysis was used to associate the correlations between the WHO Quality of Life–BREF (WHOQOL–BREF) score and the five metabolic indices. Results. The WHOQOL–BREF score for the non-MetS group significantly differed from the scores of the HR-MetS and MetS groups. The triglyceride level was positively correlated with the cortisol level, while all five metabolic indices were negatively correlated with testosterone level. Stepwise regression analysis produced a model predicting WHOQOL–BREF scores with four variables including MAP, intelligence quotient (IQ), FBG, and age. The correlation analysis then showed that there was a weak linear correlation between the testosterone level and all five metabolic indices. Conclusions. Among the five metabolic indices, the risks of hypertension and hyperglycemia are correlated with the quality of life in male schizophrenics rather than those of obesity or hyperlipidemia.

scholarly journals Analisis Pengaruh Harga, Produk, Perilaku Konsumen Terhadap Keputusan Pembelian Telon Lang

2017 ◽  
Vol 26 (02) ◽  
pp. 296-316
Author(s):  
Bida Sari ◽  
Estu Mahanani
Keyword(s):  
Regression Analysis ◽  
Quantitative Analysis ◽  
Consumer Behavior ◽  
Correlation Analysis ◽  
Linear Regression Analysis ◽  
Sampling Technique ◽  
Multiple Linear Regression Analysis ◽  
The People ◽  
Research Population ◽  
Analysis Determination

This study aims to determine the effect of variable price (X1), product (X2) and consumer behavior (X3) on buying decisions (Y). The method used is the interactive-associative research. This research population is all of the people who live at RT.13 and RT.14 RW.07 North Utan Kayu, East Jakarta.The sampling technique is done by Accidented Sampling of 67 respondents who attendCSR event “Kunjungan Posyandu Telon Cap Lang” PT. Eagle Indo Pharma at Posyandu RW.07.Data collection is using observation, interviews and questionnaires. Analysis of the data used is quantitative analysis, including correlation analysis, determination analysis and multiple linear regression analysis (simultaneously), and hypothesis testing using t-test and F-test.The results of data processing performed with SPSS 19.0 forwindows. The value of correlation coefficient (R) is 0.950.The value of determination coefficient is(R2) = 0.903. It means that 90.3%variation of dependent variable (decision of buying) could be predicted from the combination of variables (pricing, product and consumer behavior)and the remaining 9.7% is influenced by other factors. The regression equation was obtained Y = 1,291+ 0.289 X1 + 0.476 X2 + 0.205 X3.  For the F test obtained F value is calculated at 142.689, greater than F-table (2.807) with α = 5%, so the conclusion:  reject Ho and Ha is accepted. It means there is significant influence of variable price (X1), product (X2) and consumer behavior (X3) together on decision of buying(Y).

scholarly journals A Machine Learning Model to Predict Intravenous Immunoglobulin-Resistant Kawasaki Disease Patients: A Retrospective Study Based on the Chongqing Population

2021 ◽  
Vol 9 ◽  
Author(s):  
Jie Liu ◽  
Jian Zhang ◽  
Haodong Huang ◽  
Yunting Wang ◽  
Zuyue Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Retrospective Study ◽  
Kawasaki Disease ◽  
Prediction Model ◽  
Prediction Models ◽  
Ivig Resistance ◽  
Independent Risk Factors

Objective: We explored the risk factors for intravenous immunoglobulin (IVIG) resistance in children with Kawasaki disease (KD) and constructed a prediction model based on machine learning algorithms.Methods: A retrospective study including 1,398 KD patients hospitalized in 7 affiliated hospitals of Chongqing Medical University from January 2015 to August 2020 was conducted. All patients were divided into IVIG-responsive and IVIG-resistant groups, which were randomly divided into training and validation sets. The independent risk factors were determined using logistic regression analysis. Logistic regression nomograms, support vector machine (SVM), XGBoost and LightGBM prediction models were constructed and compared with the previous models.Results: In total, 1,240 out of 1,398 patients were IVIG responders, while 158 were resistant to IVIG. According to the results of logistic regression analysis of the training set, four independent risk factors were identified, including total bilirubin (TBIL) (OR = 1.115, 95% CI 1.067–1.165), procalcitonin (PCT) (OR = 1.511, 95% CI 1.270–1.798), alanine aminotransferase (ALT) (OR = 1.013, 95% CI 1.008–1.018) and platelet count (PLT) (OR = 0.998, 95% CI 0.996–1). Logistic regression nomogram, SVM, XGBoost, and LightGBM prediction models were constructed based on the above independent risk factors. The sensitivity was 0.617, 0.681, 0.638, and 0.702, the specificity was 0.712, 0.841, 0.967, and 0.903, and the area under curve (AUC) was 0.731, 0.814, 0.804, and 0.874, respectively. Among the prediction models, the LightGBM model displayed the best ability for comprehensive prediction, with an AUC of 0.874, which surpassed the previous classic models of Egami (AUC = 0.581), Kobayashi (AUC = 0.524), Sano (AUC = 0.519), Fu (AUC = 0.578), and Formosa (AUC = 0.575).Conclusion: The machine learning LightGBM prediction model for IVIG-resistant KD patients was superior to previous models. Our findings may help to accomplish early identification of the risk of IVIG resistance and improve their outcomes.

scholarly journals Development of a Probabilistic Seismic Performance Assessment Model of Slope Using Machine Learning Methods

2020 ◽  
Vol 12 (8) ◽  
pp. 3269
Author(s):  
Shinyoung Kwag ◽  
Daegi Hahm ◽  
Minkyu Kim ◽  
Seunghyun Eem
Keyword(s):  
Machine Learning ◽  
Regression Analysis ◽  
Linear Regression ◽  
Seismic Performance ◽  
Prediction Models ◽  
Linear Regression Analysis ◽  
Assessment Model ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods

The objective of this study is to propose a model that can predict the seismic performance of slope relatively accurately and efficiently by using machine learning methods. Probabilistic seismic fragility analyses of the slope had been carried out in other studies, and a closed-form equation for slope seismic performance was proposed through a multiple linear regression analysis. However, the traditional statistical linear regression analysis showed a limit that could not accurately represent such nonlinear slope seismic performances. To overcome this limit, in this study, we used three machine learning methods (i.e., support vector machine (SVM), artificial neural network (ANN), Gaussian process regression (GPR)) to generate prediction models of the slope seismic performance. The models obtained through the machine learning methods basically showed better performance compared to the models of the traditional statistical methods. The results of the SVM showed no significant performance difference compared with the results of the nonlinear regression analysis method, but the results based on the ANN and GPR showed a remarkable improvement in the prediction performance over the other models. Furthermore, this study confirmed that the GPR-based model predicted relatively accurate seismic performance values compared with the model through the ANN.

scholarly journals Research on New Energy and Traditional Energy Based on SPSS

2017 ◽  
Vol 9 (2) ◽  
pp. 189
Author(s):  
Yue Liu ◽  
Jingqiu Wu
Keyword(s):  
Regression Analysis ◽  
Correlation Analysis ◽  
Stock Price ◽  
Linear Regression Analysis ◽  
National Policy ◽  
Investment Strategy ◽  
Green Economy ◽  
Energy Industry ◽  
New Energy ◽  
Fierce Competition

In China, the main profit of the energy industry is traditional energy sources, the proportion of traditional energy companies take on a high number. However, China has been putting forward green economy, with strongly support of national policy, the new energy enterprises emerge in an endlessly stream, the businesses involved in new energy economy profit a lot and that everyone is better off, which leads to a relatively strong upward tendency for new energy stocks. Therefore, based on such a fierce competition in the energy industry, it is necessary to know if the relevance of the new energy stock and traditional energy stock is positive or negative. This thesis is based on a combination of correlation analysis and regression analysis, analyze the correlation of new energy stock and traditional energy stock, and the sub-sectors of new energy, do research on stock investment strategy through the analysis of convergence. We firstly use SPSS to carry out correlation analysis on stock price, quantitatively illustrate the relationship between the two kinds of stocks, and then calculate the correlation coefficient, determine its correlation strength, at last linear regression analysis by SPSS, and summarize a functional relationship for the stock.

Analysis of the Production Components in the Soybean Crop in the Last Two Decades in the State of Parana and Brazil

2019 ◽  
pp. 1-7
Author(s):  
Norma Schlickmann Lazaretti ◽  
Patrícia Clemente Abraão ◽  
Tatiane Calandrino Da Mata ◽  
Kerolém Prícila Sousa Cardoso ◽  
Noélle Khristinne Cordeiro ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Correlation Analysis ◽  
Pearson Correlation ◽  
Linear Regression Analysis ◽  
The State ◽  
Agricultural Crop ◽  
Simple Linear Regression ◽  
Soybean Crop ◽  
Simple Linear Regression Analysis

Introduction: Soy is an agricultural crop that has great economic importance. The analysis considering the cultivated area, the production and the yield of grains contributes to research and transfer of technology to the producers. Aims: To adjust the mathematical model using simple linear regression analysis and correlation between the variables of planted area, production and productivity, in the state of Paraná and Brazil, in the last two decades. Study Design: Data collection and research of information on the cultivation of soybeans in the state of Paraná and in Brazil on official websites Place and Duration of Study: State University of Western Paraná, Post-Graduation in Agronomy, between July 2018 and December 2018. Methodology: Data were obtained from the Portal of the Brazilian Institute of Geography and Statistics between 1997 and 2017. Simple linear regression analysis and Pearson correlation analysis were used. Results: By analyzing the results, it is possible to observe significant increases in soybean production in both Paraná and Brazil over this 20-year period. Paraná achieved a 205% increase in planted area, 290% in production and 141% in average yield. Conclusion: The simple linear regression and correlation analysis showed an adjustment between the cultivated area, the production and the average productivity in the soybean crop in the period from 1997 to 2017.

scholarly journals Video Quality Prediction Models Based on Video Content Dynamics for H.264 Video over UMTS Networks

2010 ◽  
Vol 2010 ◽  
pp. 1-17 ◽  
Author(s):  
Asiya Khan ◽  
Lingfen Sun ◽  
Emmanuel Ifeachor ◽  
Jose-Oscar Fajardo ◽  
Fidel Liberal ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Prediction Models ◽  
Linear Regression Analysis ◽  
Video Quality ◽  
Quality Prediction ◽  
Application Layer ◽  
Loss Models ◽  
The Impact ◽  
Umts Networks

The aim of this paper is to present video quality prediction models for objective non-intrusive, prediction of H.264 encoded video for all content types combining parameters both in the physical and application layer over Universal Mobile Telecommunication Systems (UMTS) networks. In order to characterize the Quality of Service (QoS) level, a learning model based on Adaptive Neural Fuzzy Inference System (ANFIS) and a second model based on non-linear regression analysis is proposed to predict the video quality in terms of the Mean Opinion Score (MOS). The objective of the paper is two-fold. First, to find the impact of QoS parameters on end-to-end video quality for H.264 encoded video. Second, to develop learning models based on ANFIS and non-linear regression analysis to predict video quality over UMTS networks by considering the impact of radio link loss models. The loss models considered are 2-state Markov models. Both the models are trained with a combination of physical and application layer parameters and validated with unseen dataset. Preliminary results show that good prediction accuracy was obtained from both the models. The work should help in the development of a reference-free video prediction model and QoS control methods for video over UMTS networks.

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