WORKING WITH DATA IN STATISTICAL RESEARCH

Statistical data in most cases are described as an arithmetic mean value and a median depending on themselves, in the absence or presence of extreme values, respectively. Also, when using the classical hypothesis testing, errors of the type I (erroneous rejection of the null hypothesis of the absence of differences) and type II (erroneous acceptance of the null hypothesis) inevitably occur. Sometimes in the data, you can make the wrong choice when choosing the dependent and independent variable. The classical check does not give a complete picture of the data of the general population. Confidence intervals for a range of values for selection within a certain probability (95%). One of the modern methods for assessing the mean, mean and other measures is resampling, in particular, bootstrap (multiple generation of samples).

Pengaruh Perputaran TATO, Rasio Lancar dan DAR Atas ROE pada Perusahaan Industri Barang Konsumsi di BEI 2015 – 2019

The purpose of this analysis is to explain how the impact of TATO, Current Ratio, DAR on ROE on the consumer goods industry on the IDX during 2015-2019. The total population is 58 with 26 companies as the sample. Techniques and data analysis in this study using purposive sampling, multiple linear regression analysis and classical hypothesis testing. This can be seen from the results of the study that simultaneously TATO, Current Ratio, DAR have an effect but not significant on ROE in consumer goods industrial companies listed on the IDX 2015-2019 with the results Fcount> Ftable (7,760> 2,68). Partially, TATO has a significant effect on ROE, with the result tcount <ttable (3,753> 1,97897). Current ratio has no and insignificant effect on ROE while DAR has no significant effect on ROE. The result of adjusted R-squared 0.136 or the coefficient of determination of 13,6% percent of the dependent variable ROE which can be explained by the independent variables TATO, Current Ratio, and DAR.

Non-random errors and their importance in testing of hypotheses

Increasing numbers of non-random errors are observed in contemporary sample surveying – in particular, those resulting from no response or faulty measutrements (imprecise statistical observation). Until recently, the consequences of these kinds of errors have not been widely discussed in the context of the testing of hypoteses. Researchers focused almost entirely on sampling errors (random errors), whose magnitude decreases as the size of the random sample grows. In consequence, researchers who often use samples of very large sizes tend to overlook the influence random and non-random errors have on the results of their study. The aim of this paper is to present how non-random errors can affect the decision-making process based on the classical hypothesis testing procedure. Particular attention is devoted to cases in which researchers manage samples of large sizes. The study proved the thesis that samples of large sizes cause statistical tests to be more sensitive to non-random errors. Systematic errors, as a special case of non-random errors, increase the probability of making the wrong decision to reject a true hypothesis as the sample size grows. Supplementing the testing of hypotheses with the analysis of confidence intervals may in this context provide substantive support for the researcher in drawing accurate inferences.

Decision Theory and large deviations for dynamical hypotheses tests: The Neyman-Pearson Lemma, Min-Max and Bayesian tests

<p style='text-indent:20px;'>We analyze hypotheses tests using classical results on large deviations to compare two models, each one described by a different Hölder Gibbs probability measure. One main difference to the classical hypothesis tests in Decision Theory is that here the two measures are singular with respect to each other. Among other objectives, we are interested in the decay rate of the wrong decisions probability, when the sample size <inline-formula><tex-math id="M1">\begin{document}$ n $\end{document}</tex-math></inline-formula> goes to infinity. We show a dynamical version of the Neyman-Pearson Lemma displaying the ideal test within a certain class of similar tests. This test becomes exponentially better, compared to other alternative tests, when the sample size goes to infinity. We are able to present the explicit exponential decay rate. We also consider both, the Min-Max and a certain type of Bayesian hypotheses tests. We shall consider these tests in the log likelihood framework by using several tools of Thermodynamic Formalism. Versions of the Stein's Lemma and Chernoff's information are also presented.</p>

INTERFERENCE OF CLOSABLE CRACKS AND NARROW SLITS IN AN ELASTIC PLATE UNDER BENDING

The problem of bending of an infinite plate containing an array of trough closable cracks and narrow slits is considered in a two-dimensional statement. A crack is treated as a mathematical cut, the edges of which are able to contact along the line on the plate outside. A slit is referred to as a cut with contact stress-free surfaces and the negative jump of normal displacement can occur on this cut. The crack closure caused by bending deformation was studied based on the classical hypothesis of direct normal and previously developed model of the contact of edges along the line. A new boundary problem for a couple of biharmonic equations of plane stress and plate bending with interconnected boundary conditions in the form of inequalities on the cuts is formulated. The method of singular integral equations was applied in order to develop approximate analytical and numerical solutions to the problem. The forces and moments intensity factors near the peaks of defects and contact reaction on the closed edges of the cracks are calculated. A detailed analysis was carried out for parallel rectilinear crack and slit, depending on their relative location. Presented results demonstrate qualitative differences in the stress concentration near the defects of different nature.

Making Sense of Computational Psychiatry

In psychiatry we often speak of constructing “models.” Here we try to make sense of what such a claim might mean, starting with the most fundamental question: “What is (and isn’t) a model?” We then discuss, in a concrete measurable sense, what it means for a model to be useful. In so doing, we first identify the added value that a computational model can provide in the context of accuracy and power. We then present limitations of standard statistical methods and provide suggestions for how we can expand the explanatory power of our analyses by reconceptualizing statistical models as dynamical systems. Finally, we address the problem of model building—suggesting ways in which computational psychiatry can escape the potential for cognitive biases imposed by classical hypothesis-driven research, exploiting deep systems-level information contained within neuroimaging data to advance our understanding of psychiatric neuroscience.

Why Overfitting is Not (Usually) a Problem in Partial Correlation Networks

Network psychometrics is undergoing a time of methodological reflection. In part, this was spurred by the revelation that l1-regularization does not reduce spurious associations in partial correlation networks. In this work, we address another motivation for the widespread use of regularized estimation: the thought that it is needed to mitigate overfitting. We first clarify important aspects of overfitting and the bias-variance tradeoff that are especially relevant for the network literature, where the number of nodes or items in a psychometric scale are not largecompared to the number of observations (i.e., a low p/n ratio). This revealed that bias and especially variance are most problematic in p=n ratios rarely encountered. We then introduce a nonregularized method, based on classical hypothesis testing, that fulfills two desiderata: (1) reducing or controlling the false positives rate and (2) quelling concerns of overfitting by providing accurate predictions. These were the primary motivations for initially adopting the graphical lasso (glasso). In several simulation studies, our nonregularized method provided more than competitive predictive performance, and, in many cases, outperformed glasso. Itappears to be nonregularized, as opposed to regularized estimation, that best satisfies these desiderata. We then provide insights into using our methodology. Here we discuss the multiple comparisons problem in relation to prediction: stringent alpha levels, resulting in a sparse network, can deteriorate predictive accuracy. We end by emphasizing key advantages of our approach that make it ideal for both inference and prediction in network analysis.

Analisis pengaruh growth terhadap profit pada perusahaan manufaktur terbuka di Indonesia: Analisis model panel data dinamis

Growth will affect profitability of a firm. There is ongoing debate about how growth will affect profit both theoritically and empirical results. Classical hypothesis predict growth will affect profit positively. Growth can improve firm profitability because the effect from economies of scale and the learning curve effect that makes the production process and the cost of production become more efficient. Behavioral hypothesis predict growth will affect profit negatively because of principal agent problem, managerial constraints, penrose effect or diseconomies of scale. The objective of this study is to examine the effect of growth to profit based on the argument between Classical hypothesis and behavioral hypothesis. This study used dynamics panel data with generalized method of moments (GMM) as estimator. This study observed 82 publicly listed manufacturing firm in Indonesia consist of nine periods from 2009 to 2018 resulting in 656 observations. Empirical result shows that growth will affect profit negatively. Thus, prove the behavioral hypothesis that predict negative influence of growth to profit.