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.

scholarly journals A Meta-Analysis to Understand the Relationship between Pig Body Weight and Variation from Birth to Market

Animals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 2088
Author(s):  
Andres F. Tolosa ◽  
Joel M. DeRouchey ◽  
Mike D. Tokach ◽  
Robert D. Goodband ◽  
Jason C. Woodworth ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Analysis ◽  
Standard Deviation ◽  
Coefficient Of Variation ◽  
Polynomial Regression ◽  
Meta Analysis ◽  
Data Sets ◽  
Prediction Equations ◽  
The Mean ◽  
The Relationship

This meta-analysis aims to understand the changes in pig body weight (BW) variation from birth to market and develop prediction equations for coefficient of variation (CV) and standard deviation (SD) as a function of BW. Standard deviation is the measure of dispersion of a set of values from the mean and CV is the SD expressed as a percentage of the mean. Data collected from 16 papers and data sets yielded 117,268 individually weighed pigs with sample size ranging from 120 to 4108 pigs. Polynomial regression analysis was conducted separately for each variation measurement. The resulting prediction equations (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) suggest that there is a quadratic decreasing relationship between the CV of a population and BW, the slope gets smaller as mean BW increases from birth to market. A quadratic increasing relationship is observed for SD, with slope being smaller as mean BW of pigs increases from birth to market. These prediction equations can be used by swine producers to estimate expected CV and SD of BW among a population of pigs.

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.

An evaluation of the Hennessy Grading Probe for measuring fat depth in beef carcasses

1989 ◽  
Vol 29 (6) ◽  
pp. 781 ◽  
Author(s):  
DL Hopkins
Keyword(s):  
Linear Equations ◽  
Data Sets ◽  
Regression Equations ◽  
Data Set ◽  
General Validity ◽  
Measurement Analysis ◽  
Beef Carcasses ◽  
The Right ◽  
The Relationship ◽  
Methods Of Measurement

Fat depth at the P8 site on the rump was measured by the cut-and-measure (CM) technique and with the Hennessy Grading Probe (HGP) on 2501 beef carcasses at 1 abattoir over a 12-month period. CM measurements that differed by more than 1 mm between the right and left sides of the carcass were discarded. A subsequent data set of 1850 carcasses was randomly divided so that 2 models could be developed to assess the general validity of the relationship between the 2 methods of measurement. Analysis of measurements of the left side of the carcasses of these 2 subsamples showed the data were not normally distributed. Removal of outliers at the 95% confidence level and also measurements at both extremes of the data range improved the symmetry of the sets of data. From each adjusted data set, regression equations were developed to predict CM measurements from HGP measurements. Linear equations were adequate for predicting CM measurements from HGP measurements, and curvilinear analysis did not improve the predictions. Compared with the curvilinear equations, the linear equations resulted in smaller differences between the 2 data sets for the predicted CM measurements over a range of HGP measurements.

Relationship between body weight and testicular size in buffalo bulls

1972 ◽  
Vol 78 (3) ◽  
pp. 367-370 ◽  
Author(s):  
A. M. Yassen ◽  
M. N. Mahmoud
Keyword(s):  
Body Weight ◽  
Regression Equations ◽  
Prediction Equations ◽  
Testicular Size ◽  
Testes Size ◽  
Significant Regression ◽  
Similar Body ◽  
The Relationship

SUMMARYThe relationship between body weight, testes volume and circumference as computed for 42 slaughterhouse buffalo bulls (Bos bubalis). Animals ranged in body weight from 290 to 610 kg, testes volume (testes plus epididymides without scrota) from 102·5 to 296·7 ml and testes circumference (testes with scrota intact) from 21·31 cm. Relationships between body weight and testes volume, body weight and testes circumference, testes circumference and testes volume were all linear and correlations (r) were, 0·986, 0·932, 0·951 and were highly significant. Regression equations were calculated and it was clearly shown that buffalo bull testes volume is a function of body weight and could be easily predicted either from body weight or from testes circumference.Comparisons between the testicular size of the buffalo bull (B. bubalis) and domestic bull (B. taurus) based on calculations from prediction equations showed that the testes size of the buffalo bull was half that of a domestic bull of similar body weight.

scholarly journals Modeling of temporal development of population taking into account the situation of municipalities in the resettlement system (according to the example of the Tyumen Region)

2020 ◽  
Vol 26 (1) ◽  
pp. 215-227
Author(s):  
V. Dobryakova ◽  
A. Dobryakov
Keyword(s):  
Regression Analysis ◽  
Statistical Analysis ◽  
Spatial Statistics ◽  
Network Data ◽  
Regression Equations ◽  
Temporal Development ◽  
Data Set ◽  
Regional Center ◽  
Definition Of ◽  
The Relationship

The work is devoted to application of spatial statistics and regression analysis tools in the ArcGIS Pro program. In this report we try to confirm two theories in the relationship between positional characteristics of municipalities and the temporal development of population: The farther the locality is from the main settlement of the territory, the faster it loses its own population. The farther the locality is from the main highways of the territory, the faster it loses its own population. The main aim of this article is to find the strictest definition of the type of correlation between such specific distances as the distance to the regional center, the distance to the nearest highway and the relative changes in the municipalities’ population, according to the example of the Tyumen region. A network data set was created to calculate the distances, it contains several elements: main roads, calculated centers of municipalities (CM), lines — distances from centers to the nearest road (“stops”). For the study we used information on changes of population for 4 periods: 1981–1990, 1990–2002, 2002–2010 and 2010–2018. The dependence was done by enumerating the degrees of distances. We considered that the dependence was selected in case the relevant correlation coefficient was the largest. For each chosen relationship, ArcGIS Pro performed a complete statistical analysis, based on the results, the significance of the model was identified, residual maps constructed, and regression equations calculated. All the models except the first period turned out to be significant, but they were displaced, which indicates the existence of some unexplored factors. In the context of the constructed models, it was assumed that the distance to the regional center is closely connected with an expansion of the population in the surrounding municipalities, but the expansion gets more the closer the municipal district is to Tyumen. The distance to the nearest highway is associated with a decrease of population, and the farther the municipality is from the highway, the more it loses population.

scholarly journals Examination of Different Item Response Theory Models on Tests Composed of Testlets

2017 ◽  
Vol 6 (4) ◽  
pp. 113
Author(s):  
Esin Yilmaz Kogar ◽  
Hülya Kelecioglu
Keyword(s):  
Item Response Theory ◽  
Sample Size ◽  
Item Response ◽  
Data Sets ◽  
Meaningful Difference ◽  
Response Theory ◽  
Size Change ◽  
Data Set ◽  
Testlet Response Theory ◽  
Item Response Theory Models

The purpose of this research is to first estimate the item and ability parameters and the standard error values related to those parameters obtained from Unidimensional Item Response Theory (UIRT), bifactor (BIF) and Testlet Response Theory models (TRT) in the tests including testlets, when the number of testlets, number of independent items, and sample size change, and then to compare the obtained results. Mathematic test in PISA 2012 was employed as the data collection tool, and 36 items were used to constitute six different data sets containing different numbers of testlets and independent items. Subsequently, from these constituted data sets, three different sample sizes of 250, 500 and 1000 persons were selected randomly. When the findings of the research were examined, it was determined that, generally the lowest mean error values were those obtained from UIRT, and TRT yielded a mean of error estimation lower than that of BIF. It was found that, under all conditions, models which take into consideration the local dependency have provided a better model-data compatibility than UIRT, generally there is no meaningful difference between BIF and TRT, and both models can be used for those data sets. It can be said that when there is a meaningful difference between those two models, generally BIF yields a better result. In addition, it has been determined that, in each sample size and data set, item and ability parameters and correlations of errors of the parameters are generally high.

scholarly journals Children with 5′-end NF1 gene mutations are more likely to have glioma

2017 ◽  
Vol 3 (5) ◽  
pp. e192 ◽  
Author(s):  
Corina Anastasaki ◽  
Stephanie M. Morris ◽  
Feng Gao ◽  
David H. Gutmann
Keyword(s):  
Gene Mutation ◽  
Statistical Significance ◽  
Gene Mutations ◽  
Neurofibromatosis Type ◽  
Published Data ◽  
Data Sets ◽  
Nonsense Mutations ◽  
Data Set ◽  
Nf1 Gene ◽  
The Relationship

Objective:To ascertain the relationship between the germline NF1 gene mutation and glioma development in patients with neurofibromatosis type 1 (NF1).Methods:The relationship between the type and location of the germline NF1 mutation and the presence of a glioma was analyzed in 37 participants with NF1 from one institution (Washington University School of Medicine [WUSM]) with a clinical diagnosis of NF1. Odds ratios (ORs) were calculated using both unadjusted and weighted analyses of this data set in combination with 4 previously published data sets.Results:While no statistical significance was observed between the location and type of the NF1 mutation and glioma in the WUSM cohort, power calculations revealed that a sample size of 307 participants would be required to determine the predictive value of the position or type of the NF1 gene mutation. Combining our data set with 4 previously published data sets (n = 310), children with glioma were found to be more likely to harbor 5′-end gene mutations (OR = 2; p = 0.006). Moreover, while not clinically predictive due to insufficient sensitivity and specificity, this association with glioma was stronger for participants with 5′-end truncating (OR = 2.32; p = 0.005) or 5′-end nonsense (OR = 3.93; p = 0.005) mutations relative to those without glioma.Conclusions:Individuals with NF1 and glioma are more likely to harbor nonsense mutations in the 5′ end of the NF1 gene, suggesting that the NF1 mutation may be one predictive factor for glioma in this at-risk population.

scholarly journals Individual Tree Volume Equations for Red Pine in Michigan

1997 ◽  
Vol 14 (2) ◽  
pp. 53-58 ◽  
Author(s):  
Gary W. Fowler
Keyword(s):  
Red Pine ◽  
Data Sets ◽  
Regression Equations ◽  
Independent Data ◽  
Data Set ◽  
Individual Tree ◽  
Standing Trees ◽  
Pine Stands ◽  
Volume Estimates ◽  
Volume Equations

Abstract New total, pulpwood, sawtimber, and residual pulpwood cubic foot individual tree volume equations were developed for red pine in Michigan using nonlinear and multiple linear regression. Equations were also developed for Doyle, International 1/4 in., and Scribner bd ft volume, and a procedure for estimating pulpwood and residual pulpwood rough cord volumes from the appropriate cubic foot equations was described. Average ratios of residual pulpwood (i.e., topwood, cubic foot or cords) to mbf were developed for 7.6 and 9.6 in. sawtimber. Data used to develop these equations were collected during May-August 1983-1985 from 3,507 felled and/or standing trees from 27 stands in Michigan. Sixteen and 11 stands were located in the Upper and Lower Peninsulas, respectively. All equations were validated on an independent data set. Rough cord volume estimates based on the new pulpwood equation were compared with contemporary tables for 2 small cruise data sets. The new equations can be used to more accurately estimate total volume and volume per acre when cruising red pine stands. North. J. Appl. For. 14(2):53-58.

A Ccomparison of sedigraph and pipette methods for soil particle-size analysis

Soil Research ◽  
1993 ◽  
Vol 31 (4) ◽  
pp. 407 ◽  
Author(s):  
GD Buchan ◽  
KS Grewal ◽  
JJ Claydon ◽  
RJ Mcpherson
Keyword(s):  
Particle Size ◽  
New Zealand ◽  
Particle Size Analysis ◽  
Size Analysis ◽  
Data Sets ◽  
Regression Equations ◽  
Total Iron Content ◽  
Data Set ◽  
X Ray ◽  
The Difference

The X-ray attenuation (Sedigraph) method for particle-size analysis is known to consistently estimate a finer size distribution than the pipette method. The objectives of this study were to compare the two methods, and to explore the reasons for their divergence. The methods are compared using two data sets from measurements made independently in two New Zealand laboratories, on two different sets of New Zealand soils, covering a range of textures and parent materials. The Sedigraph method gave systematically greater mass percentages at the four measurement diameters (20, 10, 5 and 2 �m). For one data set, the difference between clay (<2 �m) percentages from the two methods is shown to be positively correlated (R2 = 0.625) with total iron content of the sample, for all but one of the soils. This supports a novel hypothesis that the typically greater concentration of Fe (a strong X-ray absorber) in smaller size fractions is the major factor causing the difference. Regression equations are presented for converting the Sedigraph data to their pipette equivalents.

scholarly journals Technical Note: Regression Analysis of Proximal Hyperspectral Data to Predict Soil pH and Olsen P

Agriculture ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 55 ◽  
Author(s):  
Miles Grafton ◽  
Therese Kaul ◽  
Alan Palmer ◽  
Peter Bishop ◽  
Michael White
Keyword(s):  
Soil Nutrient ◽  
Large Data ◽  
Technical Note ◽  
Soil Samples ◽  
Hyperspectral Data ◽  
Data Sets ◽  
Regression Equations ◽  
Data Set ◽  
Hill Country ◽  
Olsen P

This work examines two large data sets to demonstrate that hyperspectral proximal devices may be able to measure soil nutrient. One data set has 3189 soil samples from four hill country pastoral farms and the second data set has 883 soil samples taken from a stratified nested grid survey. These were regressed with spectra from a proximal hyperspectral device measured on the same samples. This aim was to obtain wavelengths, which may be proxy indicators for measurements of soil nutrients. Olsen P and pH were regressed with 2150 wave bands between 350 nm and 2500 nm to find wavebands, which were significant indicators. The 100 most significant wavebands for each proxy were used to regress both data sets. The regression equations from the smaller data set were used to predict the values of pH and Olsen P to validate the larger data set. The predictions from the equations from the smaller data set were as good as the regression analyses from the large data set when applied to it. This may mean that, in the future, hyperspectral analysis may be a proxy to soil chemical analysis; or increase the intensity of soil testing by finding markers of fertility cheaply in the field.

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