We wish to present a method to quantify the value modifying effects when comparing animal farms. To achieve our objective, multi-variable statistical methods were needed. We used a principal component analysis to originate three separate principal components from nine variables that determine the value of farms. A cluster analysis was carried o...ut in order to classify farms as poor, average and excellent. The question may arise as to which principal components and which variables determine this classification.
After pointing out the significance of variables and principal components in determining the quality of farms, we analysed the relationships between principal components and market prices. Some farms did not show the expected results by the discriminant analysis, so we supposed that the third principal component plays a great role in calculating prices. To prove this supposition, we applied the logistic regression method. This method shows how great a role the principal components play in classifying farms on the basis of price categories.
Chromatometric examination of the plumage of birds is a poorly researched topic. We have approached this issue in primarily aspect of differences in plumage of species. Moulted feathers sample collection method has been increasingly used. Reliable identification of feathers becomes an increasingly important issue, hence need for an exact measur...ement-based methodology. Eurasian Collared Dove (Streptopelia decaocto) and Feral Pigeon (Columba livia domestica) primary, secondary and tail feathers were studied. Chromatometric parameters of feathers were measured in CIELAB color system and then statistical analysis (Independent samples t-test, Descriptive Statistics, Discriminant Analysis) was performed to compare the two species. Instrumental measurements has been confirmed the high similarity between colors of the two species, however species specific differences were also found. Lightless (L*) value were significantly characteristic of particular species, while the red/green (a*) and yellow/blue (b*) value had lower Predictive Power. We identified feathers and the variables which useable to separate the two species and determined the associated Confidence Intervals of these values. Our results may draw attention to a new potential direction for exact identification of the moulted feathers during sample collection.
Six macroelements and twelve microelements were identified in thirty-six Hungarian acacia honeys collected from ten counties by inductively coupled plasma optical emission spectrometry (ICP-OES) and inductively coupled plasma mass spectrometry (ICP-MS). One-Way ANOVA (LSD and Dunnett T3 test) and linear discriminant analysis (LDA) were used to...determine the statistically verified differences among the honey samples with different geographical origin.
Significant differences were established among the samples from different counties in Na, P, S, Fe, Ni, Cu and Sr concentrations. Based on the macroelement content of honeys, the separation of samples with different geographical origin was not successful because the percent of correctly categorised cases was only 64.9%. However, examining the As, B, Ba, Cu, Fe Mn, Ni and Sr concentration, the separation of different groups was convincing since the percent of correctly classified cases was 97.2%. Thus, the examination of microelement concentration may be able to determine the geographical origin of acacia honeys.