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Statystyczna korelacja wydatków obronnych i PKB w Polsce w latach 2010- 2019

Gręzicki Marek, Płaczek Janusz

Gospodarka Materiałowa i Logistyka

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2019

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nr 5

610--626, CD

PL

Wydatki obronne w Polsce w okresie 2010-2019 utrzymywały się na poziomie dobrym i stanowiły w PKB od 1,9% do 2,1%. Pozwalał na to rosnący z roku na rok PKB, którego przybyło w tym czasie aż o ok. 46,5% w ujęciu nominalnym i ok. 30% realnie. Można zaobserwować dla tego okresu prawidłowość silnej i dodatniej korelacji statystycznej tych dwóch zmiennych dla naszego kraju. Wynosiła ona aż R = 0,947993. Oznacza to właściwą proporcję między tymi zmiennymi. Tym samym potrzeby obronne państwa nie są nadmiernie eksponowane względem innych zadań i wpasowały się w stałą zależność. Można mówić o ich optymalnym poziomie nie obciążającym zbytnio rozwoju naszego społeczeństwa.

EN

In today’s, defence expenditures in Poland in the period 2010-2019 remained at a good level and constituted in GDP from 1.9% to 2.1%. This was allowed by the year-on-year increase in GDP, which arrived at that time by as much as 46.5% in nominal terms and ca. 30% in real terms. One can observe for this period the correct-ness of the strong and positive statistical correlation of these two variables for our country. It amounted to as much as R = 0.947993. This means the right balance between these variables. Thus, the defense needs of the state are not excessively exposed to other tasks and fit into a permanent dependence. One can talk about their optimal level, which does not overly burden our society's development.

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Can Confirmation Measures Reflect Statistically Sound Dependencies in Data? The Concordance-based Assessment

Susmaga R., Szczęch I.

Foundations of Computing and Decision Sciences

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2018

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Vol. 43, No. 1

41--66

EN

The paper considers particular interestingness measures, called confirmation measures (also known as Bayesian confirmation measures), used for the evaluation of “if evidence, then hypothesis” rules. The agreement of such measures with a statistically sound (significant) dependency between the evidence and the hypothesis in data is thoroughly investigated. The popular confirmation measures were not defined to possess such form of agreement. However, in error-prone environments, potential lack of agreement may lead to undesired effects, e.g. when a measure indicates either strong confirmation or strong disconfirmation, while in fact there is only weak dependency between the evidence and the hypothesis. In order to detect and prevent such situations, the paper employs a coefficient allowing to assess the level of dependency between the evidence and the hypothesis in data, and introduces a method of quantifying the level of agreement (referred to as a concordance) between this coefficient and the measure being analysed. The concordance is characterized and visualised using specialized histograms, scatter-plots, etc. Moreover, risk-related interpretations of the concordance are introduced. Using a set of 12 confirmation measures, the paper presents experiments designed to establish the actual concordance as well as other useful characteristics of the measures.

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Efficient Search Methods for Statistical Dependency Rules

Hämäläinen W.

Fundamenta Informaticae

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2011

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Vol. 113, nr 2

117-150

EN

Dependency analysis is one of the central problems in bioinformatics and all empirical science. In genetics, for example, an important problem is to find which gene alleles are mutually dependent or which alleles and diseases are dependent. In ecology, a similar problem is to find dependencies between different species or groups of species. In both cases a classical solution is to consider all pairwise dependencies between single attributes and evaluate the relationships with some statistical measure like the χ2-measure. It is known that the actual dependency structures can involve more attributes, but the existing computational methods are too inefficient for such an exhaustive search. In this paper, we introduce efficient search methods for positive dependencies of the form X → A with typical statistical measures. The efficiency is achieved by a special kind of a branch-andbound search which also prunes out redundant rules. Redundant attributes are especially harmful in dependency analysis, because they can blur the actual dependencies and even lead to erroneous conclusions. We consider two alternative definitions of redundancy: the classical one and a stricter one. We improve our previous algorithm for searching for the best strictly non-redundant dependency rules and introduce a totally new algorithm for searching for the best classically non-redundant rules. According to our experiments, both algorithms can prune the search space very efficiently, and in practice no minimum frequency thresholds are needed. This is an important benefit, because biological data sets are typically dense, and the alternative search methods would require too large minimum frequency thresholds for any practical purpose.