scholarly journals Empirical α–β runout modelling of snow avalanches in the Catalan Pyrenees

2021 ◽  
pp. 1-12
Author(s):  
Pere Oller ◽  
Cristina Baeza ◽  
Glòria Furdada
Keyword(s):  
Comparative Analysis ◽  
Regression Model ◽  
Extreme Values ◽  
Multiple Linear Regression Model ◽  
Present Knowledge ◽  
Snow Avalanches ◽  
Independent Variables ◽  
Horizontal Length ◽  
Length And Area ◽  
Runout Modelling

Abstract A variation in the α−β model which is a regression model that allows a deterministic prediction of the extreme runout to be expected in a given path, was applied for calculating avalanche runout in the Catalan Pyrenees. Present knowledge of major avalanche activity in this region and current mapping tools were used. The model was derived using a dataset of 97 ‘extreme’ avalanches that occurred from the end of 19th century to the beginning of 21st century. A multiple linear regression model was obtained using three independent variables: inclination of the avalanche path, horizontal length and area of the starting zone, with a good fit of the function (R2 = 0.81). A larger starting zone increases the runout and a larger length of the path reduces the runout. The new updated equation predicts avalanche runout for a return period of ~100 years. To study which terrain variables explain the extreme values of the avalanche dataset, a comparative analysis of variables that influence a longer or shorter runout was performed. The most extreme avalanches were treated. The size of the avalanche path and the aspect of the starting zone showed certain association between avalanches with longer or shorter runouts.

Dampak Brand Image dan Harga terhadap Keputusan Pembelian Produk Miniso

2021 ◽  
Vol 2 (2) ◽  
pp. 75-87
Author(s):  
Kardinah Indrianna Meutia ◽  
Hadita Hadita ◽  
Wirawan Widjarnarko
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Brand Image ◽  
Multiple Linear Regression Model ◽  
Purchasing Decisions ◽  
Business World ◽  
Independent Variables ◽  
Spss Software ◽  
Fierce Competition

The economy in the current era of globalization has fierce competition, especially in the business world, where each company moves to continue to make products primarily to meet what is needed by consumers and companies are always innovating to make products that are different from before and from  competitors and strive to be superior to other products.  This study was conducted with the aim of analyzing the independent variables which include brand image and price variables on their influence on the dependent variable, namely purchasing decisions.  This study uses multiple linear regression model and with classical assumption test using SPSS software version 24. Data were obtained primarily by distributing questionnaires to 162 students at Bhayangkara University, Jakarta Raya.  This study states that brand image and price variables can partially and significantly influence consumer purchasing decisions positively. The F test explains that the brand image and price variables together can influence purchasing decisions with results showing f-count>f-table.

A Mathematical Comparison of the Lachance - Traill Matrix Correction Procedure with Statistical Multiple Linear Regression Analysis in XRF Applications

1992 ◽  
Vol 36 ◽  
pp. 1-10
Author(s):  
Anthony J. Klimasara
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Mathematical Meaning ◽  
Matrix Correction ◽  
Independent Variables ◽  
Xrf Analysis

AbstractIt will be shown that the Lachance-Traill XRF matrix correction equations can be derived from the statistical multiple linear regression model. By selecting and properly transforming the independent variables and then applying the statistical multiple linear regression model, the following form of the matrix correction equation is obtained:Furthermore, it will be shown that the Lachance-Traill influence coefficients have a deeper mathematical meaning. They can be related to the multiple regression coefficients of the transformed system:Finally, it will be proposed that the Lachance-Traill model is equivalent to the statistical multiple linear regression model with the transformed independent variables. Knowing these facts will simplify correction subroutines in Quantitative/Empirical XRF Analysis programs. These mathematical facts have already been implemented and presented in a paper: “Automated Quantitative XRF Analysis Software in Quality Control Applications” (Pacific-International Congress on X-ray Analytical Methods, Hawaii, 1991).This demonstrates that the Lachance-Traill model has a strong mathematical foundation and is naturally justified mathematically.

scholarly journals The Effect of Phase on Paper Thickness Optimization in the Paper Recycling Process Using Recycling Machine

2020 ◽  
Vol 2 (1) ◽  
pp. 19
Author(s):  
Fauzhia Rahmasari
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Multiple Linear Regression Model ◽  
Thickness Optimization ◽  
Recycling Process ◽  
Paper Recycling ◽  
Independent Variables ◽  
The Relationship

AbstractEfforts to manage the recycling of paper waste into new paper have been carried out in recent times. It takes a tool or machine that is able to effectively and efficiently recycle used paper into new paper. There are several factors that affect the effectiveness of paper recycling machines, one of which is the paper thickness. One method that can be used to analyze the factors that influence paper thickness in the paper production process using a paper recycling machine is regression analysis. Regression analysis is data analysis techniques in statistics that is used to examine the relationship between several independent variables and dependent variable. However, if we want to examine the relationship or effect of two or more independent variables on a dependent variable, the regression model used is a multiple linear regression model. This study purposes are to analyze the factors that influence paper thickness using a paper recycling machine using multiple linear regression and to inform the modeling about that. The results showed that the factors that affect the paper thickness optimization are destruction and press phase. AbstractUpaya pengelolaan daur ulang sampah kertas menjadi kertas baru telah banyak dilakukan pada jaman sekarang. Dibutuhkan suatu alat atau mesin yang mampu secara efektif dan efisien dalam mendaur ulang kertas bekas menjadi kertas baru. Terdapat beberapa faktor yang mempengaruhi tingkat efektifitas mesin daur ulang kertas diantaranya adalah ketebalan kertas. Salah satu metode yang dapat digunakan untuk menganalisis faktor-faktor yang mempengaruhi ketebalan kertas pada proses produksi kertas menggunakan mesin daur ulang kertas adalah analisis regresi. Analisis regresi merupakan teknik analisis data dalam statistika yang digunakan untuk mengkaji hubungan antara beberapa variabel bebas dengan variabel tidak bebas. Namun, jika ingin mengkaji hubungan atau pengaruh dua atau lebih variabel bebas terhadap satu variabel tidak bebas, maka model regresi yang digunakan adalah model regresi linier berganda. Tujuan dalam penelitian ini yaitu menganalisis faktor-faktor yang mempengaruhi ketebalan kertas menggunakan mesin daur ulang kertas menggunakan regresi linier berganda serta memberikan informasi pemodelan mengenai hal tersebut. Hasil penelitian menunjukkan bahwa faktor yang mempengaruhi keoptimalan ketebalan kertas adalah fase penghancuran dan pemadatan kertas

scholarly journals Revenue Autonomy and Entrepreneurship in The Municipalities of the Warmian-Masurian Voivodeship

2019 ◽  
Vol 14 (1) ◽  
pp. 75-86
Author(s):  
Anna Wichowska ◽  
Tomasz Wierzejski
Keyword(s):  
Linear Regression ◽  
Private Sector ◽  
Regression Model ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Agricultural Producers ◽  
Factors Affecting ◽  
Independent Variables ◽  
Construction Companies ◽  
Working Age

One of the most important problems in the proper functioning and fulfillment of entrusted tasks by municipalities in Poland is their high income independence. It depends, inter alia, on the broadly understood entrepreneurship undertaken in their area. Therefore the aim of this study was to identify the factors affecting entrepreneurship in the Warmian-Masurian Voivodeship in 2014–2016, which determined the revenue autonomy of municipalities in the region. The analysis was conducted with the use of a multiple linear regression model. Revenue autonomy, which is measured by the proportion of a municipality’s own-source revenues in total revenues, was the explained (dependent) variable. The initial group of explanatory (independent) variables consisted of 22 indicators linked with the operations of local businesses in the evaluated region. The key determinants of the revenue autonomy of municipalities were: the percentage of commercial partnerships in the total number of companies in the private sector, the percentage of private-sector companies in the total number of companies, the percentage of industrial and construction companies in the total number of companies, the percentage of self-employed in the total number of companies in the private sector, the number of agricultural producers, livestock breeders and hunting companies per 1000 residents, and the number of companies employing up to 9 people per 10,000 working-age residents.

The Impact of Climate Change on Agriculture in Morocco: An Econometric Study

2020 ◽  
Vol 1 (2) ◽  
pp. 19-28
Author(s):  
Faycel Tazigh
Keyword(s):  
Climate Change ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Independent Variables ◽  
Temperature And Precipitation ◽  
The Impact ◽  
The Relationship

This paper aims to analyze the relationship that may exist between climate change and cereal yield in Morocco. In order to study this correlation between variables, we used the most common form of regression model which is the multiple linear regression model. There are two main uses of multiple linear regression model. The first one is to quantify the weight of impact that the independent variables had on the dependent variable. The second use is to predict not only the relationship that may found between variables but also their impacts. In our case, we have chosen temperature and precipitation as an independent variables and cereal yield as dependent variable.

The Prediction Model and Application for Net Calorific Value of Biomass Power Plant Fuel

2013 ◽  
Vol 781-784 ◽  
pp. 2420-2424
Author(s):  
Hong Liang Zhang ◽  
Dian Zhen Fu ◽  
Pan Zhang ◽  
Wei Li
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression Model ◽  
Calorific Value ◽  
Woody Biomass ◽  
Proximate Analysis ◽  
Analysis Method ◽  
Value Prediction ◽  
Independent Variables ◽  
Biomass Power Plant

A quantitative multivariate linear regression equation is established with the net calorific value of woody biomass fuel as the dependent variable and proximate analysis indexes as the independent variables. The prediction effect of the regression model is evaluated by the error analysis method. Results show that within the variable application ranges, the prediction error of the multiple linear regression model developed is small, and it could provide basis and reference for the calorific value prediction of woody biomass.

FACTORS ASSOCIATED WITH CHILDREN’S HEIGHT AND BODY MASS INDEX IN LITHUANIA, 1990–2008

2015 ◽  
Vol 48 (4) ◽  
pp. 502-529 ◽  
Author(s):  
Andrej Suchomlinov ◽  
Janina Tutkuviene
Keyword(s):  
Body Mass Index ◽  
Linear Regression ◽  
Regression Model ◽  
Socioeconomic Factors ◽  
Multiple Linear Regression ◽  
Birth Order ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Factors Associated ◽  
Independent Variables

SummaryThe aim of the study was to reveal the ethnic and socioeconomic factors associated with height and body mass index (BMI) of children during the period of political and social transition in Lithuania in 1990–2008. Data were derived from the personal health records of 1491 children (762 boys and 729 girls) born in 1990 in Vilnius city and region. Height and BMI from birth up to the age of 18 years were investigated. Children were divided into groups according to their ethnicity, place of residence, father’s and mother’s occupation and birth order. Height and BMI were compared between the groups; a Bonferroni correction was applied. A multiple linear regression model was used to measure the effects of the independent variables on height and BMI. Girls living in Vilnius city were significantly taller in later life at the ages of 8 and 11 years. Sons of mothers employed as office workers appeared to be significantly taller at the ages of 7, 12, 14 and 15 years compared with the sons of labourers. First-born girls were taller at the age of 7 years than later-born girls of the same age (124.48±5.11 cm and 122.92±5.14 cm, respectively,p<0.001). Later-born children of both sexes had higher BMIs at birth compared with first-borns; however, first-born girls had higher BMIs at the age of 11 years compared with their later-born peers (17.78±2.87 kg/m² and 16.79±2.14 kg/m² respectively,p<0.001). In the multiple linear regression model, the five tested independent variables explained only up to 18% of total variability. Boys were more sensitive to ethnic and socioeconomic factors: ethnicity appeared to be a significant predictor of boys’ height at the age of 5 years (p<0.001), while birth order (p<0.001) predicted boys’ BMI at birth. In general, ethnicity, place of residence, father’s and mother’s occupation and birth order were not associated with children’s height and BMI in most age groups.

scholarly journals Bridge Construction Cost Prediction using Multiple Linear Regression

2019 ◽  
Vol 8 (9) ◽  
pp. 3115-3121
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Correlation Method ◽  
Multiple Linear Regression Model ◽  
Percentage Error ◽  
Cost Prediction ◽  
Independent Variables ◽  
Qualitative Variables ◽  
Leave One Out

Cost of construction of bridges is predicted using multiple linear regression model, based on data of bridges from Maharashtra state in India. Cost per unit area is taken as an appropriate dependent variable. Using both conventional and double log regression techniques, models for cost/m2 and log of cost/m2 are developed. Total 6 independent variables, which include both qualitative and quantitative variables, are used to develop the model. Height of bridge, average span length and depth of foundation are used as quantitative variables. Zone of construction, deck type and foundation type are used as qualitative variables in developing model. Strength of these independent variables with dependent variable is found out using pearson’s correlation method. Model is then verified using Leave One Out Cross Validation (LOOCV) technique. The most suited regression model obtained from the data experiment is double log regression with R2 of 0.850 and a Mean Absolute Percentage Error (MAPE) of 17.74%, as compared to 25% MAPE observed in past for studies related to traditional cost prediction.

Infrared Spectroscopic Quantification of Methacrylation of Hyaluronic Acid: A Scaffold for Tissue Engineering Applications

2018 ◽  
Vol 72 (10) ◽  
pp. 1455-1466 ◽  
Author(s):  
Farzad Yousefi ◽  
Shital Kandel ◽  
Nancy Pleshko
Keyword(s):  
Tissue Engineering ◽  
Hyaluronic Acid ◽  
Ir Spectroscopy ◽  
Regression Model ◽  
Multiple Linear Regression Model ◽  
Total Reflection ◽  
Attenuated Total Reflection ◽  
Reaction Process ◽  
Independent Variables ◽  
H Nmr

Methacrylated hyaluronic acid (MeHA) has been used extensively in tissue engineering and drug delivery applications. The degree of methacrylation (DM) of HA impacts hydrogel crosslinking, which is of pivotal importance for cell interactions. The methacrylation reaction occurs over several hours, and DM is currently assessed post reaction and after dialysis of the solution, using nuclear magnetic resonance (1H NMR) data. Thus, there is little control over exact DM in a specific reaction. Here, infrared (IR) spectroscopy in attenuated total reflection (ATR) mode was investigated as an alternate modality for assessment of the DM of HA hydrogels, including during the reaction progression. Attenuated total reflection is a low-cost technique that is widely available in research and industry labs that can be used online during the reaction process. Strong correlations were achieved with IR-derived peak heights from dialyzed and lyophilized samples at 1708 cm−1 (from the methacrylic ester carbonyl vibration), and 1H NMR values ( R = 0.92, P = 6.56E-11). Additional IR peaks of importance were identified using principal component analysis and resulted in significant correlations with the 1H NMR DM parameter: 1454 cm−1 ( R = 0.85, P = 2.81E-8), 1300 cm−1 ( R = 0.95, P = 4.50E-14), 950 ( R = 0.85, P = 3.55E-8), 856 cm−1 ( R = 0.94, P = 1.20E-12), and 809 cm−1 ( R = 0.93, P = 3.54E-12). A multiple linear regression model to predict 1H NMR-derived DM using the 1708, 1300, and 1200 cm−1 peak heights as independent variables resulted in prediction with an error of 3.2% using dialyzed and lyophilized samples ( P < 0.001). Additionally, a multilinear regression model to predict the DM in undialyzed liquid MeHA samples obtained during the reaction process using similar peak height positions as independent variables resulted in a prediction error of 0.81% ( P < 0.05). Thus, IR spectroscopy can be utilized as an alternate modality to 1H NMR for quantification of the DM of MeHA while sampling either on-line during the methacrylation reaction as well as in post-lyophilized products. This could greatly simplify workflow for tissue engineering and other applications.

scholarly journals Predicting Heart Rate Variability Parameters in Healthy Korean Adults: A Preliminary Study

2021 ◽  
Vol 58 ◽  
pp. 004695802110562
Author(s):  
Sung-Woo Kim ◽  
Hun-Young Park ◽  
Won-Sang Jung ◽  
Kiwon Lim
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Linear Regression ◽  
Regression Model ◽  
Frequency Domain ◽  
Time Domain ◽  
Multiple Linear Regression Model ◽  
Coefficient Of Determination ◽  
Independent Variables

The purpose of the study was to examine the development of a multiple linear regression model to estimate heart rate variability (HRV) parameters using easy-to-measure independent variables in preliminary experiments. HRV parameters (time domain: SDNN, RMSSD, NN50, pNN50; frequency domain: TP, VLF, LF, HF) and the independent variables (e.g., sex, age, body height, body weight, BMI, HR, HRmax, HRR) were measured in 75 healthy adults (male n = 27, female n = 48) for estimating HRV. The HRV estimation multiple linear regression model was developed using the backward elimination technique. The regression model’s coefficient of determination for the time domain variables was significantly high (SDNN = R2: 72.2%, adjusted R2: 69.8%, P < .001; RMSSD = R2: 93.1%, adjusted R2: 92.1%, P < .001; NN50 = R2: 78.0%, adjusted R2: 74.9%, P < .001; pNN50 = R2: 89.1%, adjusted R2: 87.4%, P < .001). The coefficient of determination of the regression model for the frequency domain variable was moderate (TP = R2: 75.6%, adjusted R2: 72.6%, P < .001; VLF = R2: 41.6%, adjusted R2: 40.3%, P < .001; LF = R2: 54.6%, adjusted R2: 49.2%, P < .001; HF = R2: 67.5%, adjusted R2: 63.4%, P < .001). The coefficient of determination of time domain variables in the developed multiple regression models was shown to be very high (adjusted R2: 69.8%–92.1%, P < .001), but the coefficient of determination of frequency domain variables was moderate (adjusted R2: 40.3%–72.6%, P < .001). In addition to the equipment used for measuring HRV in clinical trials, this study confirmed that simple physiological variables could predict HRV.

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