scholarly journals Correlation between live weight and body measurements in certain dog breeds

2021 ◽  
Vol 51 (2) ◽  
pp. 151-159
Author(s):  
B. Yüceer Özkul ◽  
P.C.K. Doka ◽  
D. Özen ◽  
F.T. Özbaşer ◽  
B. Özarslan ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Live Weight ◽  
The Body ◽  
Acceptable Level ◽  
Body Measurements

The purpose of this study was to determine the correlation between live weight and body measurements in Zağar, Zerdava, and Çatalburun dogs. Animal materials were obtained from various regions of Turkey. A total of 304 dogs from three breeds were used: Zağar (45 females, 59 males), Zerdava (50 females, 50 males), and Çatalburun (62 females, 38 males). Live weights and certain body measurements were determined. A linear regression model was created using the parameters obtained in this study. The bodyweights calculated with the body measurements were found to be at a high or acceptable level in the Zağar, Zerdava, and Çatalburun genotypes (R2 = 0.902, 0.467, and 0.697, respectively).

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.   

scholarly journals Multiple Linear Regression Model for Estimating the Price of a Housing Unit

2017 ◽  
Vol 17 (2) ◽  
pp. 66-77
Author(s):  
P. Boye ◽  
D. Mireku-Gyimah ◽  
C. A. Okpoti
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
The Body ◽  
Housing Unit ◽  
Unit Price ◽  
Data Matrix ◽  
Housing Units

This paper uses the respective unit costs, over fifteen (15) years, of selected Housing Unit Major Components (HUMC): cement, iron rods, aluzinc roofing sheets, coral paint, wood and sand, to develop Multiple Linear Regression Model (MLRM) for determining Housing Unit Price (HUP) for one-bedroom and two-bedroom housing units. In the modeling, the Ordinary Least Squares (OLS) normality assumption which could introduce errors in the statistical analyses was dealt with by log transformation of the data, ensuring the data is normally distributed and there is no correlation between them. Minimisation of Sum of Squares Error method was used to derive the model coefficients. The resultant MLRM is:  Ŷi MLRM = (X'X)-1 X'Y(xi') where X is the sample data matrix. The specific model for one-bedroom housing unit is loge (HUPMLRM)1-Bed = 1.017 – 2.225 x 10-5 x CC + 2.512 x 10-6 x CS + 6.016 x 10-4 x CIR  +  1.985 x  10-4 x CR + 5.694 x 10-4 x CP -7.437 x 10-4 x CW and that for two-bedroom housing unit is loge (HUPMLRM)2-Bed = 5.760 – 7.501 x 10-7 x CC + 2.935 x 10-6 x CS + 1.898 x 10-3 x CIR  +  6.695 x 10-4 x CR - 9.157 x 10-3 x CP +6.136 x 10-3 x CW, where CC, CS, CIR, CR, CP and CW are costs of the total quantity of cement, sand, iron rods, roofing, paint and wood respectively. The MLRM was validated by using it to estimate the known HUP in the 15.5th year. From the results, the percentage absolute deviations of the estimated HUP from the known HUP are 1.27% and 2.02% for one-bedroom and two-bedroom housing units respectively, which are satisfactory. The novel approach presented in this paper is a valuable contribution to the body of knowledge in modeling. Keywords: Multiple Regression Analysis, Housing Unit Major Components, Housing Unit Price

Physico-Chemical Analysis of Groundwater and Effect of Climatic Change in Angul-Talcher Area

2018 ◽  
Vol 6 (12) ◽  
pp. 26
Author(s):  
Aliva Bera ◽  
D.P. Satapathy
Keyword(s):  
Water Resources ◽  
Linear Regression ◽  
Regression Model ◽  
Climatic Change ◽  
Linear Regression Model ◽  
Ann Model ◽  
Quality Of Water ◽  
Physico Chemical ◽  
Simulation Based

In this paper, the linear regression model using ANN and the linear regression model using MS Excel were developed to estimate the physico-chemical concentrations in groundwater using pH, EC, TDS, TH, HCO3 as input parameters and Ca, Mg and K as output parameters. A comparison was made which indicated that ANN model had the better ability to estimate the physic-chemical concentrations in groundwater. An analytical survey along with simulation based tests for finding the climatic change and its effect on agriculture and water bodies in Angul-Talcher area is done. The various seasonal parameters such as pH, BOD, COD, TDS,TSS along with heavy elements like Pb, Cd, Zn, Cu, Fe, Mn concentration in water resources has been analyzed. For past 30 years rainfall data has been analyzed and water quality index values has been studied to find normal and abnormal quality of water resources and matlab based simulation has been done for performance analysis. All results has been analyzed and it is found that the condition is stable. 

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

scholarly journals Vitamin A and E Homologues Impacting the Fate of Acrylamide in Equimolar Asparagine-Glucose Model System

Antioxidants ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 993
Author(s):  
Su Lee Kuek ◽  
Azmil Haizam Ahmad Tarmizi ◽  
Raznim Arni Abd Razak ◽  
Selamat Jinap ◽  
Maimunah Sanny
Keyword(s):  
Vitamin E ◽  
Linear Regression ◽  
Vitamin A ◽  
Regression Model ◽  
Linear Regression Model ◽  
Model System ◽  
Concentration Increase ◽  
Acrylamide Formation ◽  
Β Carotene

This study aims to evaluate the influence of Vitamin A and E homologues toward acrylamide in equimolar asparagine-glucose model system. Vitamin A homologue as β-carotene (BC) and five Vitamin E homologues, i.e., α-tocopherol (AT), δ-tocopherol (DT), α-tocotrienol (ATT), γ-tocotrienol (GTT), and δ-tocotrienol (DTT), were tested at different concentrations (1 and 10 µmol) and subjected to heating at 160 °C for 20 min before acrylamide quantification. At lower concentrations (1 µmol; 431, 403, 411 ppm, respectively), AT, DT, and GTT significantly increase acrylamide. Except for DT, enhancing concentration to 10 µmol (5370, 4310, 4250, 3970, and 4110 ppm, respectively) caused significant acrylamide formation. From linear regression model, acrylamide concentration demonstrated significant depreciation over concentration increase in AT (Beta = −83.0, R2 = 0.652, p ≤ 0.05) and DT (Beta = −71.6, R2 = 0.930, p ≤ 0.05). This study indicates that different Vitamin A and E homologue concentrations could determine their functionality either as antioxidants or pro-oxidants.

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