scholarly journals Use of Modern Regression Analysis in the Dielectric Properties of Foods

Foods ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1472
Author(s):  
Yu-Kai Weng ◽  
Jiunyuan Chen ◽  
Ching-Wei Cheng ◽  
Chiachung Chen
Keyword(s):  
Regression Analysis ◽  
Dielectric Properties ◽  
Influencing Factors ◽  
Polynomial Regression ◽  
Treatment Method ◽  
Regression Equations ◽  
Constant Variance ◽  
Dependent Variables ◽  
Qualitative Variables ◽  
The Relationship

The dielectric properties of food materials is used to describe the interaction of foods with electromagnetic energy for food technology and engineering. To quantify the relationship between dielectric properties and influencing factors, regression analysis is used in our study. Many linear or polynomial regression equations are proposed. However, the basic assumption of the regression analysis is that data with a normal distribution and constant variance are not checked. This study uses sixteen datasets from the literature to derive the equations for dielectric properties. The dependent variables are the dielectric constant and the loss factor. The independent variables are the frequency, temperature, and moisture content. The dependent variables and frequency terms are transformed for regression analysis. The effect of other qualitative factors, such as treatment method and the position of subjects on dielectric properties, are determined using categorical testing. Then, the regression equations can be used to determine which influencing factors are important and which are not. The method can be used for other datasets of dielectric properties to classify influencing factors, including quantitative and qualitative variables.

PSVI-8 Meta-regression Analysis to Determine the Relationship Between Growing Pig Body Weight and Variation

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 218-219
Author(s):  
Andres Fernando T Russi ◽  
Mike D Tokach ◽  
Jason C Woodworth ◽  
Joel M DeRouchey ◽  
Robert D Goodband ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Analysis ◽  
Sample Size ◽  
Polynomial Regression ◽  
Data Sets ◽  
Regression Equations ◽  
Prediction Equations ◽  
Data Set ◽  
Rate Of Increase ◽  
The Relationship

Abstract The swine industry has been constantly evolving to select animals with improved performance traits and to minimize variation in body weight (BW) in order to meet packer specifications. Therefore, understanding variation presents an opportunity for producers to find strategies that could help reduce, manage, or deal with variation of pigs in a barn. A systematic review and meta-analysis was conducted by collecting data from multiple studies and available data sets in order to develop prediction equations for coefficient of variation (CV) and standard deviation (SD) as a function of BW. Information regarding BW variation from 16 papers was recorded to provide approximately 204 data points. Together, these data included 117,268 individually weighed pigs with a sample size that ranged from 104 to 4,108 pigs. A random-effects model with study used as a random effect was developed. Observations were weighted using sample size as an estimate for precision on the analysis, where larger data sets accounted for increased accuracy in the model. Regression equations were developed using the nlme package of R to determine the relationship between BW and its variation. Polynomial regression analysis was conducted separately for each variation measurement. When CV was reported in the data set, SD was calculated and vice versa. The resulting prediction equations were: CV (%) = 20.04 – 0.135 × (BW) + 0.00043 × (BW)2, R2=0.79; SD = 0.41 + 0.150 × (BW) - 0.00041 × (BW)2, R2 = 0.95. These equations suggest that there is evidence for a decreasing quadratic relationship between mean CV of a population and BW of pigs whereby the rate of decrease is smaller as mean pig BW increases from birth to market. Conversely, the rate of increase of SD of a population of pigs is smaller as mean pig BW increases from birth to market.

THE MODEL FOR MANAGING INVESTMENTS IN THE MOSCOW ENVIRONMENT BASED ON A POWER REGRESSION EQUATION

2020 ◽  
Vol 2 (7) ◽  
pp. 91-99
Author(s):  
E. V. KOSTYRIN ◽  
◽  
M. S. SINODSKAYA ◽  
Keyword(s):  
Solid Waste ◽  
Influencing Factors ◽  
Pair Correlation ◽  
Approximation Error ◽  
Regression Equation ◽  
Regression Equations ◽  
The Impact ◽  
The Relationship ◽  
Average Approximation ◽  
Average Approximation Error

The article analyzes the impact of certain factors on the volume of investments in the environment. Regression equations describing the relationship between the volume of investment in the environment and each of the influencing factors are constructed, the coefficients of the Pearson pair correlation between the dependent variable and the influencing factors, as well as pairwise between the influencing factors, are calculated. The average approximation error for each regression equation is determined. A correlation matrix is constructed and a conclusion is made. The developed econometric model is implemented in the program of separate collection of municipal solid waste (MSW) in Moscow. The efficiency of the model of investment management in the environment is evaluated on the example of the growth of planned investments in the activities of companies specializing in the export and processing of solid waste.

scholarly journals Establishing the interrelation of the main influencing factors on the safety of methane-air mixture transportation

2019 ◽  
Vol 109 ◽  
pp. 00002
Author(s):  
Ruslan Ahaiev ◽  
Kateryna Dudlia ◽  
Dmytro Prytula
Keyword(s):  
Regression Analysis ◽  
Influencing Factors ◽  
Methane Production ◽  
Vacuum Pump ◽  
Coal Deposits ◽  
Geological Factors ◽  
The Relationship

The computing of the pipeline diameter and the decision of a vacuum pump for capturing methane-air mixture from underground wells in a real mine are presented in the article. The safety conditions for transportation of methane-air mixture are presented. The relationship between mining and mining and geological factors using regression analysis are established. These factors affect the transportation of methane-air mixture safety. The results of the work can be used in the projecting of underground degassing systems in the development of coal deposits and methane production.

scholarly journals Determinants of Capital Structure in Bahrain Stock Market

2016 ◽  
Vol 3 (2) ◽  
pp. 177
Author(s):  
Abdelrhman Ahmad Meero
Keyword(s):  
Regression Analysis ◽  
Correlation Analysis ◽  
Stock Market ◽  
Capital Structure ◽  
Linear Model ◽  
Significant Relationship ◽  
Correlation And Regression Analysis ◽  
Dependent Variables ◽  
The Relationship ◽  
Industrial Investment

The aim of this paper is to examine the determinants of capital structure (profitability, size, risk and growth). The sample is composed of 39 Bahraini firms listed in Bahrain Stock Market. The study covered the period 2011-2015. Correlation and regression analysis have been used to identify the relationship between the capital structure determinants and debt leverages (book leverage and market leverage). Correlation analysis aims to identify this relationship at market level and at sectorial level. Regression analysis objective is to anticipate the models characterizing the relationships between determinants and capital leverages. Results of the analysis shows negative significant relationship between profitability and dependent variables, with more significance relationship with market leverage. This relationship is demonstrated in market level and in insurance and services sectors between profitability and book leverage. When the market leverage is the dependent variable this relationship is valid in market level and in banking, hotels, insurance and services sectors. Positive significant relationship has been found between size and both leverages in market level. Similar result is detected on sectorial level in banking, industrial, investment and services when the dependent variable is book leverage. Size-market leverage relationship is positive and significant also in insurance, investment and services sectors. The relationship risk—book leverage is significant only on sectorial level in Industrial, insurance and investment sectors. In term of market leverage—risk relationship, significant relationship is detected in market level and in investment and services sectors. Regression analysis results present a significant linear model reflecting the relationship between determinants of capital structure and leverages.

ANALISIS PENGARUH FIRM SIZE DAN PROFITABILITAS TERHADAP ISLAMIC SOCIAL REPORTING (ISR) DENGAN EARNING GROWTH SEBAGAI VARIABEL MODERATING PADA JAKARTA ISLAMIC INDEX (JII)

1970 ◽  
Vol 18 (1) ◽  
pp. 137-150
Author(s):  
Titin Hartini
Keyword(s):  
Regression Analysis ◽  
Data Analysis ◽  
Firm Size ◽  
Sampling Technique ◽  
Social Reporting ◽  
Analysis Method ◽  
Earning Growth ◽  
Independent Variables ◽  
Dependent Variables ◽  
The Relationship

This study aims to determine and analyze the effect of Firm Size and Profitability on Islamic Social Reporting, and see whether Earning Growth can strengthen or weaken the relationship between independent variables to the dependent variable. This research takes object in companies registered in Jakarta Islamic Index (JII) for period 2011-2015, amounted to 30 companies and obtained by 12 companies by using purposive sampling technique. Data analysis method used is multiple regression analysis. Based on the results of simultaneous research all the variables berpegaruh against Islamic Social Reporting, but partially, only Firm Size that affect the Islamic Social Reporting. In addition, Earning Growth can not strengthen or weaken the relationship between independent variables and dependent variables.

Does social media matter for post typology? Impact of post content on Facebook and Instagram metrics

2016 ◽  
Vol 40 (4) ◽  
pp. 458-471 ◽  
Author(s):  
Ricardo Limongi França Coelho ◽  
Denise Santos de Oliveira ◽  
Marcos Inácio Severo de Almeida
Keyword(s):  
Social Media ◽  
Regression Analysis ◽  
Design Methodology ◽  
Ordinary Least Squares ◽  
User Preference ◽  
Content Type ◽  
Dependent Variables ◽  
Body Design ◽  
The Impact ◽  
The Relationship

Purpose – The purpose of this paper is to measure the impact of post type (advertising, fan, events, information, and promotion) on two interaction metrics: likes and comments. The measuring involved two popular social media, Facebook and Instagram, and in business profiles of five different segments (food, hairdressing, ladies’ footwear, body design, fashion gym wear). Design/methodology/approach – The method used was multiple regression analysis with an estimator of the ordinary least squares for 1,849 posts from five different companies posted on Facebook (680 posts) and Instagram (1,169 Instagram) over an eight-month posting period. Regression analysis was used to identify the relationship between the dependent variables (likes and comments), and the independent variables (post typology, segments, week period, month, characters and hashtag). Findings – It was seen that the post types events and promotion led to a greater involvement of followers in Instagram, in particular. In Facebook, the events post type was only significant in the like’s interaction. Another finding of the research is the relevance of the food and body design segment which was significant in both virtual social media. This indicates a user preference involving their day-to-day lives, in this case, having a tattoo done or seeing a photo of a dessert. Originality/value – With the findings of this study, academics and social media managers can improve the return indicators of interactions in posts and broaden the discussion on the types of post and interaction in different virtual social media.

Proton NMR Analysis of Octane Number for Motor Gasoline: Part IV

1992 ◽  
Vol 46 (8) ◽  
pp. 1294-1300 ◽  
Author(s):  
M. Ichikawa ◽  
N. Nonaka ◽  
H. Amano ◽  
I. Takada ◽  
S. Ishimori ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Aromatic Compounds ◽  
Octane Number ◽  
Proton Magnetic Resonance ◽  
Linear Regression Analysis ◽  
Regression Equations ◽  
Nmr Analysis ◽  
Motor Gasoline ◽  
The Relationship

Software (a program) for predicting the octane number of motor gasoline by proton magnetic resonance (PMR) spectrometry has been formulated. At the same time, a method has been studied to predict the composition of gasoline (in terms of the contents of paraffin, olefin, and aromatic compounds). The formulated program was evaluated by using it to predict the octane numbers of 31 samples of marketed summer gasoline (including 16 regular and 15 premium products), whose octane numbers and compositions were identified according to the ASTM standards. Also, the relationship between the PMR spectrum and gasoline composition was subjected to linear regression analysis by using the 31 samples whose octane numbers were calculated, and the appropriateness of the resultant regression equations was assessed. This report concerns the results of the study in which the octane numbers of the 31 samples were satisfactorily predicted by the formulated program and useful linear regression equations were obtained for the prediction of the composition of gasoline.

scholarly journals Regresi Median Pada Copula Bivariat

2019 ◽  
Vol 3 (1) ◽  
pp. 07
Author(s):  
Geraldus Anggoro Rinadi ◽  
Leopoldus Ricky Sasongko ◽  
Bambang Susanto
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Median Regression ◽  
Independent Variables ◽  
Bivariate Data ◽  
Dependent Variables ◽  
Frank Copula ◽  
The Relationship

Abstrak: Analisis regresi adalah analisis yang sering digunakan dalam segala bidang yang bertujuan untuk memodelkan hubungan antara dua jenis variabel tak bebas dengan satu atau variabel bebas. Regresi linier masih memiliki beberapa kekurangan, maka dari untuk mengatasinya dengan regresi median. Copula dapat digunakan untuk mendeteksi hubungan data bivariat dengan peubah-peubah yang berbeda. Hasil penelitian menunjukkan kurva kuantil bersyarat terbaik berdasarkan MSE terkecil Data I yaitu copula Plackett sebesar 0.8650. Sedangkan nilai MSE terkecil Data II yaitu copula Gaussian sebesar 0.3954. Nilai MSE terkecil Data III yaitu copula Frank sebesar 0.5575. Terakhir, nilai MSE terkecil Data IV yaitu copula Clayton sebesar 0.3190.Abstract:  Regression analysis is an analysis that is often used in all fields which aims to model the relationship between two types of non-dependent variables with one or independent variables. Linear regression still has several drawbacks, so to overcome this by median regression. Copula can be used to detect bivariate data relations with different variables. The results showed that the best conditional curves based on the smallest MSE of Data I were Plackett copula of 0.8650. While the smallest MSE value is Data II, which is a Gaussian population of 0.3954. The smallest MSE value of Data III is Frank copula of 0.5575. Finally, the smallest MSE value is Data IV which is copula Clayton of 0.3190.

scholarly journals Thai Silk Product Industry Development In The Northeastern Region Of Thailand

2011 ◽  
Vol 8 (10) ◽  
Author(s):  
Nak Gulid ◽  
Sirivan Serirat ◽  
Suphada Sirikudta ◽  
Udom Sayaphan ◽  
Aurathai Lertwannawit ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Quantitative Analysis ◽  
Brand Personality ◽  
Purchasing Behavior ◽  
Silk Industry ◽  
Independent Variables ◽  
Northeastern Region ◽  
Dependent Variables ◽  
The Relationship ◽  
Product Industry

This research aims to study the relationship between independent variables (value, motivation, brand personality, attitude toward product and demographic factor) and dependent variables (purchasing behavior and trend to buy in the future) of Thai silk industry in Northeastern region, Thailand. Quantitative analysis is allowed in this study. This research includes 400 customers who buy Thai silk in two provinces (Roi-Et and Khon-Khen) in the Northeastern region in Thailand. Multiple regression analysis was employed in this study. The result shows that motivation and value are strong influenced on purchasing behavior and trend to buy product in both provinces. However, brand personality and attitude toward product are partially supported our expectations.

scholarly journals 4557 Defining the relationship between kidney structure and function in patients with and without diabetes and hypertension

2020 ◽  
Vol 4 (s1) ◽  
pp. 47-47
Author(s):  
Ghazal Zekavat Quinn ◽  
Matthew Palmer ◽  
Jordana Cohen ◽  
Xin Sheng ◽  
Katalin Susztak
Keyword(s):  
Regression Analysis ◽  
Kidney Disease ◽  
Structural Changes ◽  
Polynomial Regression ◽  
Interstitial Fibrosis ◽  
Kidney Tissue ◽  
The United States ◽  
Hypertensive Patients ◽  
Patients With Diabetes ◽  
The Relationship

OBJECTIVES/GOALS: Histopathological descriptions of kidney tissue provide more information about kidney disease severity and prognosis than serum measures, yet most patients with chronic kidney disease do not undergo kidney biopsy. We aim to develop a method to determine the degree of renal injury in patients with diabetes and hypertension without the need for biopsy. METHODS/STUDY POPULATION: Clinical data and renal tissue samples were collected from 864 patients undergoing tumor-associated nephrectomy in seven medical centers in the United States. Exclusion criteria included age < 18, presence of pyelonephritis or non-diabetic or hypertensive renal disease or incomplete clinical or histopathologic data. 19 histologic parameters were scored in a blinded manner by one renal pathologist. We examined the relationship between and functional variables (such as estimated glomerular filtration rate (eGFR)). Polynomial regression analysis was performed to model histopathologic variables and important clinical parameters such as eGFR RESULTS/ANTICIPATED RESULTS: 607 samples met inclusion criteria and were stratified as: control (no history of diabetes or hypertension, n = 160), hypertension alone (n = 224) and both diabetes and hypertension (n = 223). Interstitial fibrosis (IF) and glomerulosclerosis (GS) had the strongest correlations with eGFR. Regression analysis was used to model histopathologic score for a given eGFR. We found that diabetes and hypertension modified the relationship between tubulointerstitial fibrosis and eGFR. For example, while hypertensive patients without diabetes had 33% IF at an eGFR of 30 ml/min/1.73m2 (r2 = 0.64, p<0.01), hypertensive patients with diabetes had 32% IF at an eGFR of 30 ml/min/1.73m2 (r2 = .43, p<0.01) and control patients had approximately 23% IF at an eGFR of 30 ml/min/1.73m2 (r2 = 0.22, p<0.01). DISCUSSION/SIGNIFICANCE OF IMPACT: Here, we describe the relationship between renal structural changes and renal function and show that hypertension significantly modifies the relationship at a given eGFR. These data can be used to reasonably predict renal structural changes given clinical information without the need for renal biopsy and may provide prognostic value.

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