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The taper-volume relationship is an essential tool for planning silvicultural management in forests with the final aim of promoting the distribution of products. The objective of this study is the generation of a compatible model that allows an accurate estimation of the taper-volume of Abies religiosa in several regions of Mexico. A data set of 2608 values was considered for the analysis of the information; the model was simultaneously adjusted by seemingly unrelated regression (SUR) for the estimation of compatible parameters. The model generated coefficients of determination with fit levels higher than 97 %, roots mean square less than 0.40, biases lower than 0.11, with certainty to model each forest region, which are complemented with their respective Akaike information criteria (AIC). In most cases, the shape factor of the species went from neiloid to paraboloid. This information will be useful for timber forest inventories. [ABSTRACT FROM AUTHOR]

It is of great interest for establishments that offer a product or service, for example museums, to know the number of possible visits as a function of other variables that customers will make in a specific period. This to evaluate the degree of demand that exists and at the same time, evaluate if the dissemination strategies are working or otherwise, make more appropriate decisions to improve the quality of attention and satisfaction with visitors. To implement the generalized linear model to estimate the number of annual visits to museums in Mexican territory as a function of predictive variables, a database of 110 museums taken from the Instituto Nacional de Estadística y Geografía (INEGI) for the years 2017 and 2018 was used. The models used were regression ordinary linear (RLO) and Poisson regression (RP) and these were applied to all the principal components of eleven predictor variables (web page, reception and service capacity, main theme, permanent collection, ownership, entry, adult fee, discounts, type of visits, open days of the year and temporary exhibitions) to counteract multicollinearity. Besides, the coefficient of determination (R²) was measured between the observed and estimated data to determine the method with the best fit. The functions were used to estimate the number of visits for the years 2019 and 2020. With the model that best fit, an analysis of estimation errors was performed. Comparing the observed data for the years 2017 and 2018 with the results estimated with the characteristic functions of each model, the determination coefficients were R² = 0.61 for the RLO and R² = 0.86 for the PR, respectively. For the years 2019 and 2020, the coefficients of determination obtained were R² = 0.68 and R² = 0.31 for the RLO, and R² = 0.87 and R² = 0.84 for the RP, respectively. The maximum error of estimation registered was between 10 001 and 20 000 annual visits. It is shown that the most suitable model to forecast future annual visits to any museum in Mexico is the Poisson regression. It is believed that the high adjustment to the data observed with the PR method is because these did not have an excess of zeros and fulfilled the assumption of equidispersion. Finally, with the proposed characteristic function, the number of annual visits can be estimated with a maximum error of around 10 %, which is low compared to the maximum number of annual visits that a museum receives. [ABSTRACT FROM AUTHOR]

Diameter and height are crucial parameters in forest inventories to determine the volume that is stored in the forest. The objective of this research was to generate an equation for eight federative entities that allows an accurate estimate of the timber volume of Abies religiosa in the center and south of the country. Around 2,747 trees were used to adjust equations of total tree roll volume, branch volume and total tree volume (vrta, vramas and vta). The following criteria were set for the selection of the best model; adjusted coefficient of determination, bias, mean error; as well as the graphical and numerical analysis of the residuals. Due to the lack of information on the branches, for Tlaxcala and Veracruz states only the total tree roll volume was obtained. The goodness of fit generated by the equations is reliable, since it explains more than 93% to the dependent variable, the errors are less than 0.47 m. These expressions are useful tools that can be applied by forestry technicians in different stands in the study area. [ABSTRACT FROM AUTHOR]

Professionals of the social and environmental sciences must solve problems (answer questions) based on data sampling and analyses. Commonly, all professionals face similar challenges: they need to take decisions on a population (e.g., all the trees of a region), but only have data from a sample (some trees of that region). A key tool in this process is to propose population models for the response variable (tree growth as a function of tree age and climatic conditions) and then use model predictions to take decisions (e.g., when to cut trees according to climatic conditions). In this paper we discuss how to propose, estimate, and select models of a population based on sampling data. We put special emphasis in proposing several alternative models (hypotheses) to solve one problem (e.g., different tree growth functions for age), which must be proposed before data sampling, including a null model (tree growth does not depend on tree age or climatic conditions). Models guide us on how data must be sampled for a valid contrast (growth measurements in trees of different age and under contrasting climates). Then, the Akaike information criterion (AIC) can be employed to sort the most parsimonious models, selecting those with the best goodness of fit (likelihood) and the lowest number of parameters (model complexity). Along the text, we introduce basic notions of multimodel inference and discuss common user mistakes. We provide real examples, and share their data and the analyses code in R, a free and open source software. In addition to be useful to professionals from different sciences, we expect our paper to promote the teaching of multimodel inference in graduate courses. [ABSTRACT FROM AUTHOR]

The use of nonparametric tests is recommended when the data do not meet the assumptions of normality and homoscedasticity. However, the assumptions of normality of the data or the use of goodness of fit tests that are not appropriate for the assessed sample are common aspects. In many cases, this implies the use of statistical tests unadjusted for the real data distribution and, consequently, the establishment of inaccurate conclusions. Therefore, in this paper the detection power of five tests of goodness of fit (Kolmogorov- Smirnov-Lilliefors, Kolmogorov-Smirnov, Shapiro-Wilk, Anderson-Darling and Jarque-Bera) in symmetric distributions is analysed in six sample sizes between 30 and 1000 participants generated by Monte Carlo simulation. Results show a marked conservative tendency as the sample size becomes larger. Regarding sample sizes to detect non-normality: analysing small samples the best results are provided by Kolmogorov-Smirnov-Lilliefors and Anderson-Darling tests, if the sample is medium-sized (200 participants) the Kolmogorov-Smirnov, and when samples are over 500 participants the Shapiro-Wilk test is recommended. In addition, the classic test of Kolmogorov- Smirnov is considered absolutely ineffective regardless the sample size. [ABSTRACT FROM AUTHOR]

In 1994, Rudas, Clogg and Lindsay proposed a new index of lack of fit for a certain model H in contingency table analysis. The framework of the index is a two latent class model where the model H applies to the first latent class and the second latent class is unrestricted. The index of lack of fit is the proportion of individuals of the population intrinsically outside the model. In this work we propose the analysis of the second latent class to study the causes of lack of fit of a model and to detect outlying cells. By means of three classical examples from the literature Rudas' second latent class analysis is compared to standardized residual analysis, the deleted residual analysis, and the variations of likelihood ratio statistics. The second latent class was not worse than any other criterion in the three examples. [ABSTRACT FROM AUTHOR]