regression line from data in geogebra classic

REGRESSION LINE FROM DATA IN GEOGEBRA CLASSIC SOFTWARE
REGRESSION LINE FROM DATA IN GEOGEBRA CLASSIC SERIES

Previous research demonstrated a relationship between biometric parameters (e.g., body mass, cranial and skeletal measures) and environmental features (e.g., climate, habitat quality, autumn-winter food availability). The morphometric data analysis provides information on skeletal development and allows the identification of body measures useful as ecological indicators, permitting the formulation of better management strategies for wildlife. The expansion of wildlife populations has, therefore, caused a general development in hunting activities, together with the necessity of using biometry (size analysis) and geometric morphometry (shape analysis) as methods to monitor the status of wildlife populations and suitable environments for different wildlife species. The necessity of involving this kind of dataset in wildlife management has been particularly relevant in Italy, where the number of wild animals has considerably increased during the last decades, and where the procedures of measurement are nowadays standardized. Biometric surveys and analysis of the resulting cranial and body data are now crucial in wildlife management decisions. Over the last few decades, the dimensions and shapes of structures in wildlife populations have been extensively investigated. The conversion factor is helpful to improve classical biometric databases to better clarify the relationship between environment and wildlife status. The analysis of variance showed that one parameter was significantly different between sexes ( p = 0.04), demonstrating the usefulness of the measurement conversion. We used the conversion factor to obtain, from the GeoGebra database, four additional parameters of 50 mandibles.

REGRESSION LINE FROM DATA IN GEOGEBRA CLASSIC SERIES

The series of measurements, taken with two different methods (direct measurement using the caliper and conversion from GeoGebra output), showed a good degree of agreement. After image processing using GeoGebra, we calculated the conversion factor as the mean ratio between measures taken using GeoGebra and the caliper. We took photos of 25 mandibles of adult roe deer and at the same time measured mandible length and teeth row length using a caliper.

REGRESSION LINE FROM DATA IN GEOGEBRA CLASSIC SOFTWARE

A method to convert values obtained from the GeoGebra software into biometric measures is described. Unlike classical biometry, which involves the use of a caliper for measurements, geometric morphometry acquires, through software, a series of reference points (landmarks) from digital photos, providing a series of linear measures. Study of dimensions (biometry) and shapes (geometric morphometry) of bone structures in ungulates is of extreme importance in wildlife population management. The obtained data can be very useful to assess both wildlife population status and its management. Therefore, the relationship among biometric parameters, animal and environmental features can be better studied. The availability of this conversion factor enhances the numerousness of parameters into the classical biometric database, by means of the conversion of shape measures into size measures. Thus, we attempted to identify a conversion factor between shape and size analysis methods, using the mandible of adult roe deer as a bone model. Biometric surveys and analysis of the resulting cranial and body data are now crucial in management decisions and new possibilities of improving datasets should be considered. Thus, in the last decades, study of the sizes and shapes of structures in wildlife populations has been extensively investigated. The wildlife expansion in the Italian Apennines caused a general development in hunting activities, together with the necessity of using biometry (size analysis) and geometric morphometry (shape analysis) as methods for monitoring the status of wildlife populations.

Regression line from data in geogebra classic