Correlation and regression analysis of atmospheric conditions, proximate compositions, and layer production parameters / Kristine Ilagan Peralta

Date

6-2022

Degree

Bachelor of Science in Agriculture

Major Course

Major in Animal Science

College

College of Agriculture and Food Science (CAFS)

Adviser/Committee Chair

Krystalene S

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Abstract

A secondary analysis of data using atmospheric conditions, proximate compositions, and production parameters of layers was done to build a model using stepwise regression. The independent or predictor variables used were the atmospheric parameters, particularly temperature, relative humidity, vapor pressure, evaporation, and temperature-humidity index, and proximate compositions, crude protein, fat, and fiber. As for the dependent or response variables, the production parameters of layers were used, specifically feed conversion ratio (FCR), egg weight (EW), shell weight (SW), albumen weight (AW), and yolk weight (YW). Furthermore, the study was conducted to determine the relationship between the parameters by means of Pearson correlation analysis. Upon running the analysis using Statistical Analysis System (SAS), the multicollinearity between the temperature and temperature-humidity index was observed (r = 0.99022; P < 0.0001). Meanwhile, after conducting the stepwise regression analysis, out of 48 models generated, 23 had a determination coefficient (R2) of more than 0.5 and a P-value of less than 0.05. These equations can be used to predict parameters such as egg weight, shell weight, and albumen weight. In conclusion, the atmospheric conditions and proximate compositions can be used to create a model that can predict the production performance of layer chickens.

Language

English

LC Subject

Poultry industry--Environmental conditions, Poultry industry--Philippines--Research, Agriculture Institute of Animal Science

Location

UPLB Main Library Special Collections Section (USCS)

Call Number

LG 993.5 2022 A3 P47

Document Type

Thesis

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