Analysis of sweet corn nutritional values using multivariate statistical methods103-108Views:197
Processing large amounts of data provided by automated analytical equipment requires carefulness. Most mathematical and statistical methods have strict application conditions. Most of these methods are based on eigenvalue calculations and require variables to be correlated in groups. If this condition is not met, the most popular multivariate methods cannot be used. The best procedure for such testing is the Kaiser-Meyer-Olkin test for Sampling Adequacy. Two databases were examined using the KMO test. One of them resulted from the sweet corn measured in the scone of the study, while the other from the 1979 book of János Sváb. For both databases, MSA (measures sampling adequacy) was well below the critical value, thus they are not suitable e.g. for principal component analysis. In both databases, the values of the partial correlation coefficients were much higher than Pearson’s correlation coefficients. Often the signs of partial coefficients did not match the signs of linear correlation coefficients. One of the main reasons for this is that the correlation between the variables is non-linear. Another reason is that control variables have a non-linear effect on a given variable. In such cases, classical methods should be disregarded and expert models better suited to the problem should be chosen in order to analyse the correlation system.
Study of drought stress correlation on yield and yield components of maize cultivars (Zea mays L.)67-73Views:150
This article was investigated to study the correlation and analysis of drought stress regression on maize cultivars' yield and components. The variance analysis results showed a significant difference between drought stress levels in terms of plant height, total dry weight and number of seeds per row, the total weight of cob, grain yield, harvest index, stem diameter, and cob weight with protective leave. Also, there was a significant difference in ear weight without protective leaves, ear diameter, ear length, plant weight, 100-seed weight, and seed per ear on hybrid treatments. There were statistically significant differences between cultivars in plant height, leaf area, ear diameter, ear length, number of seeds per row, number of seeds per ear, the total weight of cob wood, 100-seed weight, harvest index, plant dry weight. The results of the correlation of traits for the mean levels of drought stress showed a positive and significant correlation between plant yield and plant height, seed per row, ear length and weight of 5 pieces of wood and also with a total weight of cob wood, ear weight with bark showed the highest correlation. There is a significant correlation between leaf area and stem diameter, plant weight, total dry weight at the probability level of 0.05. Correlation coefficients between traits in non-stress conditions showed a positive and significant correlation between grain yield and height, ear length and grain in the row, which was a significant increase compared to stress conditions. The correlation of traits under full stress conditions also showed that the correlation coefficient between cob length trait and positive height was positive and significant. From the study of correlation coefficients between maize traits in non-stress conditions, it can be concluded that the most important components of grain yield are cob length and grain per row. While the correlation coefficients under moisture stress conditions show that the grain trait in the row has a positive and significant correlation with yield, under stress conditions in the cob stage did not show any traits with correlation yield.