Adam Mieldzioc, Monika Mokrzycka and Aneta Sawikowska
Modern chromatography largely uses the technique of gas chromatography coupled with mass spectrometry (GC–MS). For a set of data concerning the drought resistance of barley, the problem of the characterization of a covariance structure is investigated with the use of two methods. The first is based on the Frobenius norm and the second on the entropy loss function. For the four considered covariance structures – compound symmetry, three-diagonal and penta-diagonal Toeplitz and autoregression of order one – the Frobenius norm indicates the compound symmetry matrix and autoregression of order one as the most relevant, whilst the entropy loss function gives a slight indication in favor of the compound symmetry structure.
Triticale (Triticosecale Wittmack) is obtained through the crossing of wheat (Triticum ssp.) and rye (Secale cereale L.) and is characterized by high yield potential, good health and grain value, and high tolerance to biotic and abiotic stress. Poland is a very important region for progress in triticale breeding, since it is home to most cultivars, and numerous genetic studies on triticale have been carried out. Despite the tremendous interest in triticale among both breeders and researchers, there are no studies assessing the adaptation of cultivars to environmental conditions across growing seasons. This study was conducted to investigate the influence of cultivar, management, location and growing season on grain yield. At the same time, this approach provides a new way to determine whether there is any dependency between the eight seasons, and to find the cause of the yield response to environmental conditions in a given growing season.
This paper presents some constructions of regular D-optimal weighing designs based on the incidence matrices of a balanced incomplete block design, balanced bipartite weighing design and ternary balanced block design. We determine optimality conditions and relations between the parameters of the design, and give an example.
For square contingency tables with ordered categories, Iki, Tahata and Tomizawa (2012) considered a measure to represent the degree of departure from marginal homogeneity. However, the maximum value of this measure cannot distinguish two kinds of marginal inhomogeneity. The present paper proposes a measure which can distinguish two kinds of marginal inhomogeneity. In particular, the proposed measure is useful for representing the degree of departure from marginal homogeneity when the marginal cumulative logistic model holds.
The effective dose of six herbicidal ionic liquids containing glyphosate [N-(phosphonomethyl)glycine] was investigated. Varied biological activity of the tested compounds was observed depending on the type of cation and targeted plant species. In the case of common lambsquarters, the lowest effective dose was obtained for compounds containing didecyldimethylammonium and di(hydrogenated tallow)dimethylammonium cations. In the case of white mustard, the lowest ED50 and ED90 values were obtained for the reference compound, which contained glyphosate isopropylamine salt. These parameters were determined using dose efficiency curves based on log-logistic models with three or four parameters. The study indicates that ionic liquids with glyphosate may be used as a new form of this herbicide in the future.
In the statistical literature there are proposed many test measures to determine the independence of two qualitative variables in contingency tables, in particular in two-way contingency tables larger than 2×2. For statistical analysis, three of the so-called “chi-squared tests”—the T3 test, BP test and |χ| test—were selected. These tests were compared with a logarithmic minimum test, which is the author’s proposal. Critical values for the tests were determined with the Monte Carlo method. To compare the tests, an appropriate measure of untruthfulness of H0 was used and the power of the tests was calculated.
Results of ecological studies that involve the use of multivariate analysis of variance techniques for testing various hypotheses, interesting from the point of view of comparing the linear functions of parameters, were considered. For testing the most interesting hypotheses on a variety of interaction effects and on contrasts of class means, the application of a multivariate test statistic is recommended. Canonical variate analysis is used for graphical presentation of the results of multidimensional experiments. In this paper it is shown how a generalized form of canonical variate analysis can be useful to reveal which parametric functions of a multivariate analysis of variance model are responsible for rejecting the linear hypothesis. As an example, an analysis was made of an ecological study of trace element accumulation in plants of Italian ryegrass as a method of biomonitoring of air pollution.
Iwona Mejza, Katarzyna Ambroży-Deręgowska, Jan Bocianowski, Józef Błażewicz, Marek Liszewski, Kamila Nowosad and Dariusz Zalewski
The main purpose of this study was the model fitting of data deriving from a three-year experiment with barley malt. Two linear models were considered: a fixed linear model with fixed effects of years and other factors, and a mixed linear model with random effects of years and fixed effects of other factors. Two cultivars of brewing barley, Sebastian and Mauritia, six methods of nitrogen fertilization and four germination times were analyzed. Three quantitative traits were observed: practical extractivity of the malt, malting productivity, and a quality coefficient Q. The starting point for the statistical analyses was the available experimental material, which consisted of barley grain samples destined for malting. The analyses were performed over a series of years with respect to fixed or random effects of years. Due to the strong differentiation of the years of the study and some significant interactions of factors with years, annual analyses were also carried out.
Delwyn G. Cooke, Leonard F. Blackwell and Simon Brown
It has been suggested that it is possible to monitor the menstrual cycle by measuring the concentration of urinary reproductive steroids. This neglects the variation in void volume and in urine production rate. In neither case has any systematic analysis been reported previously. Overnight urine samples were collected each day for one complete cycle by 24 women and the void volumes and intervoid times were recorded. The void volume and urine production rate were approximately lognormally distributed and the intervoid time was approximately normally distributed. Using these distributions we consider the implications of the variation in void volume and urine production rate for the comparison of the concentrations of a urinary analyte in two samples.
We develop a simple method that completely eliminates the specification error and spurious relationships in regression. Furthermore, we introduce a stronger test of causality. We apply our method to oil prices.