scholarly journals Influence of Fat and Moisture Content in the Processing of Light Requeijão

2013 ◽  
Vol 2 (6) ◽  
pp. 12
Author(s):  
Rita de Cássia Santos Navarro da Silva ◽  
Valéria Paula Rodrigues Minim ◽  
Andréa Alves Simiqueli ◽  
Luis Antônio Minim
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Multiple Linear Regression Model ◽  
Central Composite Rotatable Design ◽  
Moisture Contents ◽  
Fat Reduction ◽  
Sensorial Attributes ◽  
Preference Map ◽  
Best Fit ◽  
Central Composite

<p>The objective of this study was to evaluate the influence of fat and moisture contents in the sensory characteristics and acceptance of light <em>requeijão</em>. The experiment followed the Central Composite Rotatable design. Formulations were characterized by Conventional Profile and the acceptability was evaluated by 100 consumers. The results were analyzed by Analysis of Variance, fitting of regression models and Preference Map. The multiple linear regression model showed the best fit for all sensorial attributes. <em>Requeijão</em> samples that fat reduction was coupled with increasing moisture demonstrated intermediate intensity of the sensory properties and showed the best acceptance among consumers. Such a result indicated that the reduction in fat levels should be associated to the increase in moisture of the final product so that there is equilibrium in the sensorial properties and the optimization the acceptance of the consumers.</p>

scholarly journals Factors associated with parasite dominance in fishes from Brazil

2016 ◽  
Vol 25 (2) ◽  
pp. 225-230
Author(s):  
Cristina Fernandes do Amarante ◽  
Wagner de Souza Tassinari ◽  
Jose Luis Luque ◽  
Maria Julia Salim Pereira
Keyword(s):  
Linear Regression ◽  
Body Length ◽  
Regression Models ◽  
Mixed Model ◽  
Multiple Linear Regression Model ◽  
Mixed Effects ◽  
The Body ◽  
Linear Regression Models ◽  
Biological Aspects ◽  
The Relationship

Abstract The present study used regression models to evaluate the existence of factors that may influence the numerical parasite dominance with an epidemiological approximation. A database including 3,746 fish specimens and their respective parasites were used to evaluate the relationship between parasite dominance and biotic characteristics inherent to the studied hosts and the parasite taxa. Multivariate, classical, and mixed effects linear regression models were fitted. The calculations were performed using R software (95% CI). In the fitting of the classical multiple linear regression model, freshwater and planktivorous fish species and body length, as well as the species of the taxa Trematoda, Monogenea, and Hirudinea, were associated with parasite dominance. However, the fitting of the mixed effects model showed that the body length of the host and the species of the taxa Nematoda, Trematoda, Monogenea, Hirudinea, and Crustacea were significantly associated with parasite dominance. Studies that consider specific biological aspects of the hosts and parasites should expand the knowledge regarding factors that influence the numerical dominance of fish in Brazil. The use of a mixed model shows, once again, the importance of the appropriate use of a model correlated with the characteristics of the data to obtain consistent results.

scholarly journals Age- and sex-dependent regression parameters for estimating live weight frim body measurements of West African Dwarf sheep

2021 ◽  
Vol 39 (1) ◽  
pp. 169-179
Author(s):  
O. S. Sowande ◽  
B. A Orebela ◽  
O. S Iyasere
Keyword(s):  
Body Size ◽  
Linear Regression ◽  
Regression Models ◽  
Live Weight ◽  
West African ◽  
The Body ◽  
Body Measurements ◽  
Age Group ◽  
Best Fit ◽  
Age And Sex

The relationships between live weight and eight body measurements of West African Dwarf (WAD) sheep were studied using 300 animals under farm condition. The animals were categorized based on age and sex. Data obtained on height at withers (HW), heart girth (HG), body length (BL), head length (HL), length of hindquarter (LHQ), width of hindquarter (WHQ), head width(HDW), and loin girth (LG) were fitted into simple linear (change in body measurement is directly proportional to weight or body size), allometric (body measurements do not necessarily change in direct proportion to weight or body size), and multiple linear regression models to predict live weight from the body measurements according to age group and sex. Results showed that live weight and body measurements of ewe were higher than that of the ram. Live weight, HG, HW, WHQ, LG, BL, LHQ, HL, and HW increased with the age of the animals. In multiple linear regression model, WHQ, LHQ, HW, HL and HDW best fit the model for sheep aged ≤1; HG, LG, BL and HDW for 2 year-old sheep; HG, BL, and HL best fit the model for sheep 3 years age group; LHQ best fit the model for sheep of 4 years of age; while HL best fits sheep that were in 5 year age category. Coefficients of determination (R2) values for linear and allometric models for predicting the live weight of WAD sheep increased with age in all the body measurements (HW, HG, BL, HL, LHQ, WHQ, HDW and LG). Sex had significant influence on the model with R2 values consistently higher in females except the models for LHQ, WHQ, LG and BL were they the same with the males. Based on R2 values, it was concluded that both linear and allometric regression models could be used to predict live weight from body measurements of WAD sheep.   

Optimisation of modification parameters for amaranth starch for the development of pudding and study of the quality traits of developed pudding

2021 ◽  
Vol 50 (1) ◽  
pp. 22-32
Author(s):  
A.K. Gupta ◽  
A.K. Jha ◽  
S. Singhal
Keyword(s):  
Functional Properties ◽  
Food Industry ◽  
Value Added ◽  
Quality Traits ◽  
Central Composite Rotatable Design ◽  
Freeze Thaw ◽  
A Value ◽  
Starch Gel ◽  
Sensorial Attributes ◽  
Central Composite

AbstractAmaranth is considered to be a part of “superfood”, however, due to multiple restricting properties, its functionality in the food industry is still not explored to its fullest. The present study investigated the effect of almond gum concentration (3–10 g), temperature (50–90 °C), and quantity of water (30–70 mL) on the functional properties of amaranth starch. A central composite rotatable design (CCRD) showed that the 6.9 g of almond gum, 64.43 mL of water, and temperature maintained at 90 °C, were the optimised conditions to attain 16.77 g g−1 of swelling power, 12.97% of solubility index, and 20.13% freeze-thaw stability. Moreover, the modified amaranth starch was further employed to develop pudding as a value-added product. The findings concluded that the developed pudding using modified amaranth starch exhibited enhanced sensorial attributes due to an increase in cohesiveness, chewiness, and resilience of starch gel.

scholarly journals EVALUATION OF MULTIPLE LINEAR REGRESSION MODEL TO OBTAIN DBH OF TREES USING DATA FROM A LIGHTWEIGHT LASER SCANNING SYSTEM ON-BOARD A UAV

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 449-454 ◽  
Author(s):  
M. V. Machado ◽  
A. M. G. Tommaselli ◽  
V. M. Tachibana ◽  
R. P. Martins-Neto ◽  
M. B. Campos
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Regression Models ◽  
Statistical Tests ◽  
Low Cost ◽  
Multiple Linear Regression Model ◽  
Urban Trees ◽  
Scanning System ◽  
Linear Regression Method

<p><strong>Abstract.</strong> Vegetation mapping requires information about trees and underlying vegetation to ensure proper management of the urban and forest environments. This information can be obtained using remote sensors. For instance, lightweight systems composed of Unmanned Aerial Vehicles (UAVs) as a platform, low-cost laser units and the recent miniaturized navigation sensors (positioning and orientation) have become a very feasible and flexible alternative. Low-cost UAV-ALS systems usually provide centimetric accuracy in altimetry, according to flight data configuration and quality of observations. This paper presents a feasibility study of a lightweight ALS system on-board a UAV to estimate the diameters at breast height (DBH) of urban trees using LiDAR data and linear regression model. A mathematical model correlating the crown diameter and height of the tree to estimate the DBH was developed based on a linear regression with stepwise method. The stepwise linear regression method enables the addition and the removal of predictor variables through statistical tests. The tree samples were separated in two classes (A and B), according to the diametric distribution. These sample classes were used to define two linear regression models. The regression models that best fit the samples achieved an R<sup>2</sup> adj value above 94% for class A and B, which demonstrates the closeness between the samples and the developed mathematical models. The quality control of the proposed regression models was performed comparing the DBH values estimated and directly measured (reference). DBH of the trees were estimated with an average discrepancy of 8.7&amp;thinsp;cm.</p>

scholarly journals 10 km running performance predicted by a multiple linear regression model with allometrically adjusted variables

2016 ◽  
Vol 51 (1) ◽  
pp. 193-200 ◽  
Author(s):  
Cesar C. C. Abad ◽  
Ronaldo V. Barros ◽  
Romulo Bertuzzi ◽  
João F. L. Gagliardi ◽  
Adriano E. Lima-Silva ◽  
...  
Keyword(s):  
Linear Regression ◽  
Linear Regression Model ◽  
Regression Models ◽  
Multiple Linear Regression Model ◽  
Running Economy ◽  
Treadmill Running ◽  
Incremental Test ◽  
Running Performance ◽  
Running Time ◽  
Multiple Regression Models

Abstract The aim of this study was to verify the power of VO2max, peak treadmill running velocity (PTV), and running economy (RE), unadjusted or allometrically adjusted, in predicting 10 km running performance. Eighteen male endurance runners performed: 1) an incremental test to exhaustion to determine VO2max and PTV; 2) a constant submaximal run at 12 km·h−1 on an outdoor track for RE determination; and 3) a 10 km running race. Unadjusted (VO2max, PTV and RE) and adjusted variables (VO2max0.72, PTV0.72 and RE0.60) were investigated through independent multiple regression models to predict 10 km running race time. There were no significant correlations between 10 km running time and either the adjusted or unadjusted VO2max. Significant correlations (p < 0.01) were found between 10 km running time and adjusted and unadjusted RE and PTV, providing models with effect size > 0.84 and power > 0.88. The allometrically adjusted predictive model was composed of PTV0.72 and RE0.60 and explained 83% of the variance in 10 km running time with a standard error of the estimate (SEE) of 1.5 min. The unadjusted model composed of a single PVT accounted for 72% of the variance in 10 km running time (SEE of 1.9 min). Both regression models provided powerful estimates of 10 km running time; however, the unadjusted PTV may provide an uncomplicated estimation.

A fuzzy multiple linear regression model based on meteorological factors for air quality index forecast

2021 ◽  
pp. 1-25
Author(s):  
Yujie Gu ◽  
Yuxiu Zhao ◽  
Jian Zhou ◽  
Hui Li ◽  
Yujie Wang
Keyword(s):  
Air Quality ◽  
Linear Regression ◽  
Linear Regression Model ◽  
Regression Models ◽  
Quality Index ◽  
Fuzzy Numbers ◽  
Meteorological Factors ◽  
Multiple Linear Regression Model ◽  
Linear Regression Models ◽  
Fuzzy Linear Regression

Air quality index (AQI) is an indicator usually issued on a daily basis to inform the public how good or bad air quality recently is or how it will become over the next few days, which is of utmost importance in our life. To provide a more practicable way for AQI prediction, so that residents can clear about air conditions and make further plans, five imperative meteorological indicators are elaborately selected. Accordingly, taking these indicators as independent variables, a fuzzy multiple linear regression model with Gaussian fuzzy coefficients is proposed and reformulated, based on the linearity of Gaussian fuzzy numbers and Tanaka’s minimum fuzziness criterion. Subsequently, historical data in Shanghai from March 2016 to February 2018 are extracted from the government database and divided into two parts, where the first half is statistically analyzed and used for formulating four seasonal fuzzy linear regression models in views of the special climate environment of Shanghai, and the second half is used for prediction to validate the performance of the proposed model. Furthermore, considering that there is beyond dispute that triangular fuzzy number is more prevalent and crucial in the field of fuzzy studies for years, plenty of comparisons between the models based on the two types of fuzzy numbers are carried out by means of the three measures including the membership degree, the fuzziness and the credibility. The results demonstrate the powerful effectiveness and efficiency of the fuzzy linear regression models for AQI prediction, and the superiority of Gaussian fuzzy numbers over triangular fuzzy numbers in presenting the relationships between the meteorological factors and AQI.

scholarly journals Regression Models for Predictions of COVID-19 New Cases and New Deaths Based on May/June Data in Ethiopia

2020 ◽  
Author(s):  
Alemayehu Siffir Argawu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Regression Models ◽  
Laboratory Tests ◽  
Addis Ababa ◽  
Pearson Correlation ◽  
Multiple Linear Regression Model ◽  
African Countries ◽  
Predicted Values

As the 15 of June 2020, we have 7,984,067 total COVID-19 cases, globally and 435,181 total deaths. Ethiopia was ranked 2nd and 15th in the table by 176 new cases and by 3,521 total new cases from African countries. Then, this study aimed to predict COVID-19 new cases and new deaths based on May/June data in Ethiopia using regression model. In this study, I used Pearson correlation analysis and the linear regression model to predict COVID-19 new cases and new deaths based on the available data from 12th May to 10th June 2020 in Ethiopia. There was a significant positive correlation between COVID-19 new cases and new deaths with different related variables. In the regression models, the simple linear regression model was a better fit the data of COVID-19 new cases and new deaths than as compared with quadratic and cubic regression models. In the multiple linear regression model, variables such as the number of days, the number of new laboratory tests, and the number of new cases from AA city significantly predicted the COVID-19 new cases. In this model, the number of days and new recoveries significantly predicted new deaths of COVID-19. The number of days, daily laboratory tests, and new cases from Addis Ababa city significantly predicted new COVID-19 cases, and the number of days and new recoveries significantly predicted new deaths from COVID-19. According to this analysis, if strong preventions and action are not taken in the country, the predicted values of COVID-19 new cases and new deaths will be 590 and 12 after two months (after 9th of August) from now, respectively. The researcher recommended that the Ethiopia government, Ministry of Health and Addis Ababa city administrative should give more awareness and protections for societies, and they should also open more COVID-19 laboratory testing centers. Generally, the obtained results of this study may help Ethiopian decision-makers put short-term future plans to face this epidemic.

scholarly journals Model-based Informal Inference

2017 ◽  
Vol 6 (5) ◽  
pp. 140
Author(s):  
Theodosia Prodromou
Keyword(s):  
Linear Regression ◽  
Instructional Practices ◽  
Linear Model ◽  
Regression Models ◽  
Linear Models ◽  
Linear Regression Models ◽  
Journal Paper ◽  
Support Students ◽  
Best Fit ◽  
Informal Inference

Following recent scholarly interest in teaching informal linear regression models, this study looks at teachers’ reasoning about informal lines of best fit and their role in pedagogy. The case results presented in this journal paper provide insights into the reasoning used when developing a simple informal linear model to best fit the available data. This study also suggests potential in specific aspects of bidirectional modelling to help foster the development of robust knowledge of the logic of inference for those investigating and coordinating relations between models developed during modelling exercises and informal inferences based on these models. These insights can inform refinement of instructional practices using simple linear models to support students’ learning of statistical inference, both formal and informal.

scholarly journals Multi-criteria approach to pair-multiple linear regression models constructing

2021 ◽  
Vol 21 (1) ◽  
pp. 88-99
Author(s):  
Mikhail P. Bazilevskiy ◽  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Multiple Regression ◽  
Linear Regression Model ◽  
Regression Models ◽  
Multiple Linear Regression Model ◽  
Linear Regression Models ◽  
Deming Regression ◽  
Multiple Linear Regression Models

A pair-multiple linear regression model which is a synthesis of Deming regression and multiple linear regression model is considered. It is shown that with a change in the type of minimized distance, the pair-multiple regression model transforms smoothly from the pair model into the multiple linear regression model. In this case, pair-multiple regression models retain the ability to interpret the coefficients and predict the values of the explained variable. An aggregated quality criterion of regression models based on four well-known indicators: the coefficient of determination, Darbin – Watson, the consistency of behaviour and the average relative error of approximation is proposed. Using this criterion, the problem of multi-criteria construction of a pair-multiple linear regression model is formalized as a nonlinear programming problem. An algorithm for its approximate solution is developed. The results of this work can be used to improve the overall qualitative characteristics of multiple linear regression models.

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