Related Papers
Social Science Research Network
Agrarian Economy and Rural Development - Realities and Perspectives for Romania', 2017
2017 •
Lucian Tanasa
Agriculture & Forestry, Vol. 61 Issue 1: 7-14, Podgorica
APPLICATION OF STATISTICAL METHODS IN ANALYSIS OF AGRICULTURE CORRELATION AND REGRESSION ANALYSIS20190521 39002 uthan5
2015 •
Jelena Zvizdojevic, Milica Vukotic
The understanding and proper use of statistics is an integral part of everyday business environment. Statistics is a scientific discipline that deals with the collection, analysis and interpretation of the data of the observed phenomena, and it is usually associated with a numerical indicator. As a scientific discipline, it can be divided into descriptive and inferential statistics. Descriptive statistics describes the statistical data, and within it, the most commonly applied time series and graphs. Inferential statistics includes statistical methods to test hypotheses, determines the relationship between variables and predicts trends of the observed phenomena. This paper analyses statistical methods, and special emphasis in this paper is given to the correlation and regression analysis (multiple regressions). The paper has two objectives. The first is to use statistical methods and statistical analysis, to determine the relationship between selected independent variables and the dependent variable (the value of agricultural production). Another aim of the paper is that using Chow tests the significance of individual factors on agricultural production is tested, by looking at two groups of countries.
Anais da Academia Brasileira de Ciências
Use of the correlation coefficient in agricultural sciences: problems, pitfalls and how to deal with them
2012 •
Wojtek Krzanowski
This paper discusses a number of aspects concerning the analysis, interpretation and reporting of correlations in agricultural sciences. Various problems that one might encounter with these aspects are identified, and suggestions of how to overcome these problems are proposed. Some of the examples presented show how mistaken and even misleading the interpretation of correlation can be when one ignores simple rules of analysis.
International Journal for Innovation Education and Research
Canonical correlations in agricultural research: Method of interpretation used leads to greater reliability of results
Tiago Olivoto
Canonical correlations analyzes are being used in the agrarian sciences and constitute an important tool in the interpretation of results. This analysis is performed by complicated mathematical equations and it is only possible to use it thanks to the development of computational software, which allow different interpretations of results, and it is up to the researcher to choose according to his knowledge. Canonical correlations can be interpreted using canonical weights, canonical loadings, or canonical cross-loadings. In Brazil, most of the works that use these analyzes interpret the canonical weights. Therefore, this study aims to show, through an analysis of canonical correlations, the best way to interpret the results, so that they are presented in the most reliable way possible. Data from an experiment with two cultivars of biquinho pepper seeded in 5 light spectrums were performed. The variables were root length and volume, plant height, number of leaves, fresh shoot and root...
An analysis of the Romanian agriculture using quantitative methods
2011 •
Tudorel Andrei
Canonical Correlation Analysis and DEA for Azorean Agriculture Efficiency
2010 •
Armando Mendes
Canadian Journal of Plant Science
Analysis of covariance in agronomy and crop research
2011 •
Patricia Juskiw
Conference on Applied Statistics in Agriculture
Variance as a Factor Effect in Interdisciplinary Studies of Agricultural Systems
Jill Schroeder
Cuban Journal of Agricultural Science
Statistical procedures most used in the analysis of measures repeated in time in the agricultural sector
2012 •
Verena Torres
In the agricultural research, situations are presented where it is difficult to use the classical linear models of analysis of variance, because the assumptions of independence, equality of variances and linearity are not fulfilled by making measures repeated in time. This paper had as object to review the statistical procedures used to analyze the designs of measures repeated in time, and determine which analytical strategies are more appropriate for each purpose. In this study, three types of traditionally used analyses are described: univariate variance (ANOVA), multivariate variance (MANOVA), and the recent one, the approach of mixed models. At present, it has been agreed that the latter is the most adequate and versatile, because it provides the possibility of examining data with structures of dependence, unbalance, and lack of normality. Besides, it provides a solution to the limitation of the multivariate analysis of variance in respect to the number of individuals and variab...
Vol. 2 No. 2 (2021): Journal of Applied Research in Plant Sciences
Checking the Significance of Correlation Coefficient from the Regression Analysis using Wheat Yield
2021 •
MOULA BUX PEERZADO
Major objective of the present study is estimate regression model and Correlation coefficient. The data were collected from the wheat section, Agriculture Research Institute (ARI) Tandojam, regarding various factors influencing on the wheat production. The survey result show that yield has significant positive correlation with the parameters such as the number of tillers per plant, number of seed per spike, length of spike in cm, the number of spikes let per, tiller per hectare and plant height cm. The parameter of wheat number of tillers per plant meter square standard error is (0.569), coefficient is (0.816). T-value is (1.43) and with positively significant is (0.018). The parameter of number of seed per spike standard error is (0.107) coefficient is (0.0811) To determine the effect of dependent and independent T-value of (0.75) with significant (0.4724).The parameter of wheat is length of spike in cm is standard error is (1.704), Coefficient is (1.092), T-value is (0.64) with th...
