Value Of Test Statistic Research Articles

Determining the attitudes of university and high school students towards sports by structural equation modelling

Sport is considered as an important service sector with its protective features for personal and public health, and developed countries regard sport as an indispensable part of their social life. The main purpose of this study is to assess factors affecting views of high school and university students towards sports. Within the scope of the study, the sample of the study is composed of 8546 students chosen with random method throughout Turkey. 5-point Likert scale consisting of 19 questions was used in order to determine views of the participants towards sports within the framework of the research. For analysing data, frequency analysis, reliability analysis and explanatory factor analysis were used in order to determine attitudes of students towards sports while structural equation model analyses were used in order to determine the relationship among the said factors regarding views towards sports. According to significance values of test statistics, all measured questions were statistically found significant for social- psychological attitude factor and physical mental attitude factor. According to the result of the model, there is a same-oriented and statistically significant relationship between social- psychological attitudes of students and their physical-mental attitudes. According to this result, when the views of students tend to be positive socially-psychologically towards sports, their physical-mental attitudes also become positive in parallel with this change. In line with the acquired result, it is necessary to demonstrate positive effects of sports on individuals socially, psychologically, physically and mentally in order to enable students to become more inclined to sports.

CD4 Cells, Haemopoietic Cells and Factors as Veritable Factors on Reproductive Hormones and Fertility

Immune system contributes to infertility, where T Reg play major role. Paucity of research in this crucial area, deems pertinent refocusing attentions on. CD4, haemopoietic cells (HC) and haemopoietic factors (HF) as veritable factors on fertility & reproductive hormones [Follicles stimulating hormone (FSH); Luteinizing hormone (LH); Prolactin (PRL); Estradiol (EZ); Progesterone (PROG); Testosterone (TESTO)] were investigated at a Teaching Hospital in Enugu, Nigeria between Feb – Oct 2022 on a pilot scale. Patients on doctor’s provisional diagnosis of infertility were examined for their hormones’ levels, CD4 & primary immune cells (PIC) counts. Analysis, using Paired Students t-test at 5% alpha level; & p value of test statistics was used for confirmation. All the patients have low CD4 counts (Range: 157-490). Our hypotheses were largely rejected: (i.e., Yes), CD4 & PIC have impact on the hormones; p values confirmed negative correlative association between CD4 & the six hormones; all the PIC have impact on PRL; except on EZ, neutrophil (N) has very significant impact on all the hormones; except on FSH, lymphocytes (L) has very significant impact on all the hormones; only on PRL that all the PIC have impact; except on PRL & TESTO, WBC (T) has no significant impact on the hormones except on PRL and TESTO; only N & L have significant impact on the PROG; only WBC (T), N & L have impact on TESTO; except L none of the PIC has impact on the EZ. Monocyte, Eosinophil & Basophils has impact only on FSH, LH & PLT. So, under no hormonal abnormality, the work strongly suggests that the inability to conceive resides on the low CD4 cells, plus impacts of it & PIC on hormones; hence, they are veritable factors. CD4 & PIC cells count should be a paradigm in infertility investigations.

Predicting Gait Speed Improvement in Idiopathic Normal Pressure Hydrocephalus Patients: The Role of Evans Index and Ventricular Volume.

Objective This study aims to investigate the association between specific imaging parameters, namely, the Evans index (EI) and ventricular volume (VV), and the variation in gait speed observed in patients with idiopathic normal pressure hydrocephalus (iNPH) before and after cerebrospinal fluid (CSF)removal/lumbar drain (LD). Furthermore, it seeks to identify which imaging parameters are the most reliable predictors for significant improvements in gait speed post procedure. Methods In this retrospective analysis, the study measured the gait speed of 35 patients diagnosed with idiopathic normal pressure hydrocephalus (iNPH) before and after they underwent CSF removal. Before lumbar drain (LD), brain images were segmented to calculate the Evans index and ventricular volume. The study explored the relationship between these imaging parameters (the Evans index and ventricular volume) and the improvement in gait speed following CSF removal. Patients were divided into two categories based on the degree of improvement in gait speed, and we compared the imaging parameters between these groups. Receiver operating characteristic (ROC) curve analysis was employed to determine the optimal imaging parameter thresholds predictive of gait speed enhancement. Finally, the study assessed the predictive accuracy of these thresholds for identifying patients likely to experience improved gait speed post-LD. Results Following CSF removal/lumbar drain, the participants significantly improved in gait speed, as indicated by a paired sample t-test (p-value = 0.0017). A moderate positive correlation was observed between the imaging parameters (EI and VV) and the improvement in gait speed post-LD. Significant differences were detected between the two patient groups regarding EI, VV, and a composite score (statistical test value = 3.1, 2.8, and 2.9, respectively; p-value < 0.01). Receiver operating characteristic (ROC) curve analysis identified the optimal thresholds for the EI and VV to be 0.39 and 110.78 cm³, respectively. The classification based on these thresholds yielded significant associations between patients displaying favorable imaging parameters and those demonstrating improved gait speed post-LD, with chi-square (χ²) values of 8.5 and 7.1, respectively, and p-values < 0.01. Furthermore, these imaging parameter thresholds had a 74% accuracy rate in predicting patients who would improve post-LD. Conclusion The study demonstrates that ventricle volume and the Evans index can significantly predict gait speed improvement after lumbar drain (LD) in patients with iNPH.

A Test Statistic Estimation-Based Approach for Establishing Self-Interpretable CNN-Based Binary Classifiers.

Interpretability is highly desired for deep neural network-based classifiers, especially when addressing high-stake decisions in medical imaging. Commonly used post-hoc interpretability methods have the limitation that they can produce plausible but different interpretations of a given model, leading to ambiguity about which one to choose. To address this problem, a novel decision-theory-inspired approach is investigated to establish a self-interpretable model, given a pre-trained deep binary black-box medical image classifier. This approach involves utilizing a self-interpretable encoder-decoder model in conjunction with a single-layer fully connected network with unity weights. The model is trained to estimate the test statistic of the given trained black-box deep binary classifier to maintain a similar accuracy. The decoder output image, referred to as an equivalency map, is an image that represents a transformed version of the to-be-classified image that, when processed by the fixed fully connected layer, produces the same test statistic value as the original classifier. The equivalency map provides a visualization of the transformed image features that directly contribute to the test statistic value and, moreover, permits quantification of their relative contributions. Unlike the traditional post-hoc interpretability methods, the proposed method is self-interpretable, quantitative. Detailed quantitative and qualitative analyses have been performed with three different medical image binary classification tasks.

A new approach of multi-dimensional correlation as a separability measure of multiple outliers in GNSS applications

Abstract Detecting and identifying outliers/failures in GNSS measurements has garnered significant attention among researchers aiming to enhance the quality of GNSS positioning and navigation. This study delves into the analysis of the separability of multiple outliers when single, double, and triple outliers occur in single-point positioning (SPP) measurements. To achieve this, a novel method includes introducing a multi-dimensional correlation coefficient among test statistics. This coefficient functions as a measure of outliers separability and, in turn, assesses the possible impact of outliers on other measurements. This multi-dimensional correlation approach is based on a nested correlation ( ρ nested θ , φ ${\rho }_{\text{nested}}^{\theta ,\varphi }$ ) that explains the variations in test statistic values with and without common measurements in two pairs/combinations. The performance of the ρ nested θ , φ ${\rho }_{\text{nested}}^{\theta ,\varphi }$ is then compared with other two existing methods of multi-dimensional correlation namely the maximum ( ρ max θ , φ ${\rho }_{\mathrm{max}}^{\theta ,\varphi }$ ) and global ( ρ Global θ , φ ${\rho }_{\text{Global}}^{\theta ,\varphi }$ ) correlation. The results show that under the presence of two outliers and with and without common measurements in two pairs, the ρ nested θ ${\rho }_{\text{nested}}^{\theta }$ outperforms the, ρ max θ ${\rho }_{\mathrm{max}}^{\theta }$ and ρ Global θ ${\rho }_{\text{Global}}^{\theta }$ exhibiting a determination coefficient (R 2) value of approximately 0.95 and 0.62 respectively. The results furthermore reveal that for three outliers and with one, two, and noncommon measurements intersecting between two combinations, the values of R 2 are 0.62, 0.96, and 0.34. respectively. This means that the ρ nested θ , φ ${\rho }_{\text{nested}}^{\theta ,\varphi }$ can explain the variations in outlier test statistic values particularly in the case that common measurements appear in two pairs/combinations.

Analysis of Rainfall Trend in North Interior Karnataka (NIK) Meteorological Subdivision of Karnataka by Non-Parametric Methodologies

The present study analyzed the trend in the month-wise, seasonal and annual rainfall employing three non-parametric tests viz. Mann-Kendall (M-K), Modified Mann-Kendall (MM-K) tests and Sen’s slope estimator for North Interior Karnataka (NIK) meteorological subdivision of Karnataka using 60 years (1960-2019) rainfall data. The M-K test results revealed that there is no monotonic trend in all data sets which indicates that there may exist the influence of serial correlation in the rainfall data. The MM-K test was applied to rainfall data of the NIK subdivision, the result of the test revealed that January, February, March and August months have a monotonic increasing trend whereas April, May, July and September months have a monotonic decreasing trend whereas June, October, November and December have no monotonic trend in monthly rainfall data. The winter season has a monotonic increasing trend, monsoon and post-monsoon seasons have a monotonic decreasing trend, and the pre-monsoon season has no monotonic trend in seasonal rainfall data. The MM-K test statistic (Tau) value for the NIK (-0.12) subdivision was found to be significant and negative indicating that there is a monotonic decreasing trend. Larger negative Sen’s slope values for NIK (-1.40) subdivision indicate the high decreasing rate of change of rainfall for annual rainfall data. Therefore, to know the trend in rainfall data most accurately in the presence of outliers and serial correlation, the Modified Mann-Kendall (MM-K) test and Sen’s slope estimator are recommended instead of Mann-Kendall (M-K) and Sen’s slope estimator.

Possible GeV gamma-ray emission from the pulsar wind nebula in CTA 1

ABSTRACT We report a detection of GeV γ-ray emission potentially originating from the pulsar wind nebula in CTA 1 by analysing about 15 yr of Fermi Large Area Telescope data. By selecting an energy range from 50 GeV to 1 TeV to remove contamination from the γ-ray pulsar PSR J0007+7303, we have discovered an extended γ-ray source with a test statistic value of ∼44.94 in the region of CTA 1. The obtained flux is measured to be 6.71 ± 2.60 × 10−12 erg cm−2 s−1 with a spectral index of 1.61 ± 0.36, which allows for a smooth connection with the flux in the TeV band. CTA 1 is also considered to be associated with 1LHAASO J0007+7303u, which is an ultra-high-energy source listed in the recently published catalogue of the Large High Altitude Air Shower Observatory. We assume that the radiation originates from the pulsar wind nebula and that its multiwavelength spectral energy distribution can be explained well with a time-dependent one-zone model.

The Expected Behaviors of Posterior Predictive Tests and Their Unexpected Interpretation.

Poor fit between models of sequence or trait evolution and empirical data is known to cause biases and lead to spurious conclusions about evolutionary patterns and processes. Bayesian posterior prediction is a flexible and intuitive approach for detecting such cases of poor fit. However, the expected behavior of posterior predictive tests has never been characterized for evolutionary models, which is critical for their proper interpretation. Here, we show that the expected distribution of posterior predictive P-values is generally not uniform, in contrast to frequentist P-values used for hypothesis testing, and extreme posterior predictive P-values often provide more evidence of poor fit than typically appreciated. Posterior prediction assesses model adequacy under highly favorable circumstances, because the model is fitted to the data, which leads to expected distributions that are often concentrated around intermediate values. Nonuniform expected distributions of P-values do not pose a problem for the application of these tests, however, and posterior predictive P-values can be interpreted as the posterior probability that the fitted model would predict a dataset with a test statistic value as extreme as the value calculated from the observed data.

Pengaruh Girl Group Twice sebagai Brand Ambassador Scarlett Whitening terhadap Keputusan Pembelian Konsumen melalui Mediasi Brand Image

One of the company's strategies in influencing consumer purchasing decisions is by using brand ambassadors. Scarlett Whitening is one of the beauty care products from Indonesia that uses the brand ambassador of Korean artists, namely the Girl Group Twice. This study uses the Theory of Source Credibility. The purpose of this study was to analyse the effect of Girl Group Twice as the brand ambassador of Scarlett Whitening beauty products on consumer purchasing decisions through brand image mediation in Pekanbaru. The research method used is explanatory quantitative. The population of this study are consumers who have purchased Scarlett Whitening beauty products whose numbers are unknown so that the sampling technique used is convenience sampling. Based on the sample determination table from Isaac and Michael, the sample in this study was 272 respondents. The results showed that the Twice brand ambassador had an effect on brand image of 34.5%, brand ambassadors had an effect on purchasing decisions by 30.5%, and brand image had an effect on purchasing decisions by 37.5%. Based on the sobel test, the sobel test statistic value obtained is tcount 6.454&gt; ttable 1.96 with a significance level of 5%. It can be concluded that the Girl Group Twice as a brand ambassador is significant in influencing consumer purchasing decisions for Scarlett Whitening products through brand image.

Распространение финансового заражения в странах Европы под воздействием глобальных шоков

This article examines the impact of global shocks of various nature on the development of the European economy within the framework of the theory and methodology of financial contagion. The content of the concept of “financial contagion” is revealed. The variety of methods of its detection is considered. It is shown that the methodology of financial contagion is useful in the study of financial crises. Based on data on local crises, the crisis burden indicator for the European region is calculated for the period from 1960 to 2019. In Europe, compared with other regions, this indicator takes on the lowest values, so the European economy is more resilient to crises. At the same time, the analysis of some empirical studies shows numerous episodes of financial contagion in the European zone. The results of original investigation which is based on large scale array of empirical data on stock indices and international trade are presented in the article. An advanced dynamic financial contagion test is used for analysis in this study. For situations caused by four global shocks contagion estimates are calculated which show different susceptibility levels of European countries to these shocks. On the other hand, the results of the analysis suggest that the COVID&amp;#8209;19 pandemic has the strongest impact on European countries because the calculated values of test statistics more often and significantly exceed the threshold level during this period (compared with other shocks such as the global financial crisis, BREXIT and the energy crisis). The main channel of contagion is the stock market, not trading. In addition, it can be noted that large European economies are not immune to financial contagion. They are as much its recipients as the weaker countries of the region. At the same time, they play the role of a damper, i. e. they do not transmit financial contagion within the European zone. Finally, the study identifies some directions of financial contagion counteractions in European countries that are linked to its transmission channels. Examples of the transformation of European economic policy caused by the impact of global shocks, contagion effects and stressful situations are given.

Securities Litigation Event Studies in the Covid Volatility Regime

Abstract Event studies play a central role in class action securities fraud litigation. Event studies are used to assess market efficiency, price impact, loss causation, and damages, all of which are essential elements of a securities fraud claim. However, a substantial increase in market-wide volatility, such as what happened in 2020 with the Covid-19 pandemic, can render the standard event study methodology unreliable. In these periods, the usual distributional assumptions for t-tests of significance produce too many false positives. In this paper, we examine the extent of the problem and propose a solution that employs the empirical distribution of the t-statistic during the Covid period to adjust the critical test statistic value. We demonstrate this methodology with an application using the 19 constituents of the consumer durables index S5CODU in PreCovid and Covid period samples, and with an evaluation of earnings announcement effects on stock prices. We show the methodology restores correct test size, eliminating excess spurious significance, while preserving substantial test power to correctly identify significant events in the Covid period.

A Reliable Pipeline Leak Detection Method Using Acoustic Emission with Time Difference of Arrival and Kolmogorov–Smirnov Test

This paper proposes a novel and reliable leak-detection method for pipeline systems based on acoustic emission (AE) signals. The proposed method analyzes signals from two AE sensors installed on the pipeline to detect leaks located between these two sensors. Firstly, the raw AE signals are preprocessed using empirical mode decomposition. The time difference of arrival (TDOA) is then extracted as a statistical feature of the two AE signals. The state of the pipeline (leakage/normal) is determined through comparing the statistical distribution of the TDOA of the current state with the prior normal state. Specifically, the two-sample Kolmogorov–Smirnov (K–S) test is applied to compare the statistical distribution of the TDOA feature for leak and non-leak scenarios. The K–S test statistic value in this context functions as a leakage indicator. A new criterion called leak sensitivity is introduced to evaluate and compare the performance of leak detection methods. Extensive experiments were conducted using an industrial pipeline system, and the results demonstrate the excellence of the proposed method in leak detection. Compared to traditional feature-based indicators, our approach achieves a significantly higher performance in leak detection.

A User’s guide for inference in models defined by moment inequalities

Models defined by moment inequalities have become a standard modeling framework for empirical economists, spreading over a wide range of fields within economics. From the point of view of an empirical researcher, the literature on inference in moment inequality models is large and complex, including multiple survey papers that document the non-standard features these models possess, the main novel concepts behind inference in these models, and the most recent developments that bring advances in accuracy and computational tractability. In this paper we present a guide to empirical practice intended to help applied researchers navigate all the decisions required to frame a model as a moment inequality model and then to construct confidence intervals for the parameters of interest. We divide our template into four main steps: (a) a behavioral decision model, (b) moving from the decision model to a moment inequality model, (c) choosing a test statistic and critical value, and (d) accounting for computational challenges. We split each of these steps into a discussion of the “how” and the “why”, and then illustrate how to take these steps to practice in an empirical application that studies identification of expected sunk costs of offering a product in a market.

Advanced soft computing techniques for predicting punching shear strength

Accurate prediction of punching shear strength is crucial for ensuring the structural integrity and safety of reinforced concrete (RC) flat slab-column connections. However, estimating punching shear strength in complex configurations, such as RC flat slabs, remains a challenging task due to intricate interactions between various parameters. To address this challenge, this study introduces an innovative approach that combines machine learning techniques with hyperparameter optimization strategies to enhance the precision and efficiency of predicting punching shear strength. Specifically, a comprehensive investigation is conducted to assess the performance of XGBoost optimized by different hyperparameter optimization algorithms such as the forensic-based investigation (FBI), Bayesian optimization (BO), grid search (GS), random search (RS), and default setting (DS). The experimental results of 40 runs on three different datasets confirmed the FBI-XGBoost model as the most appropriate for predicting punching shear strength in terms of efficiency and computation time by achieving the highest RMSE, MAPE, MAE, and R2 values. In addition, the statistical results exposed the infeasibility of using RS for the XGBoost model. While the GS method was able to configure the XGBoost model well, doing so required extensive computational time, while the BO-FBI model achieved acceptable prediction accuracy in a reasonable training time. The FBI-XGBoost model was further compared with empirical methods, including ACI-318-08, BS8110-97, and CEB-FIP-90, based on the predictive-to-actual ratio. FBI-XGBoost was identified as the best model by attaining the ratio value closest to 1 and the lowest standard deviation (1.011 ± 0.108). Overall, the statistical experimental results and hypothesis test values firmly support the FBI-XGBoost model as the most reliable free-formulae method for predicting punching shear strength in RC flat slab-column systems.

A pilot study of alterations of the gut microbiome in canine chronic kidney disease.

Gut dysbiosis has been noted in humans and animals with chronic kidney disease (CKD). However, little is known about the gut microbiome in canine patients with CKD. This study aimed to analyze and compare the gut microbiome profiles of healthy and CKD dogs, including differences in the gut microbiome between each CKD stage. The study was conducted on 29 client-owned dogs who underwent physical examination, complete blood count (CBC), serum biochemistry, and urinalysis. The gut microbiome profile of healthy dogs (n = 10) and dogs with CKD (n = 19) was analyzed employing 16S rRNA sequencing. Significant differences were seen in the composition of the gut microbiome, with increased operational taxonomic units from the phylum Proteobacteria (p = 0.035), family Enterobacteriaceae (p < 0.001), and genus Enterococcus (p = 0.002) in dogs with CKD, and a decrease in the genus Ruminococcus (p = 0.007). Furthermore, an increase in both the progression of CKD and abundance of genus Klebsiella (Jonckheere-Terpstra test statistic value (JT) = 2.852, p = 0.004) and Clostridium (JT = 2.018, p = 0.044) was observed. Our study demonstrated that in dogs with CKD, the composition of the gut microbiome varied depending on the stage of CKD. Alterations in gut microbiome composition observed in CKD patients are characterized by an increase in proteolytic bacteria and a decrease in saccharolytic bacteria. These findings suggest specific gut microbiota could be targeted for clinical management of uremic dogs with CKD.

Pengaruh Gaya Kepemimpinan terhadap Kinerja Karyawan dengan Motivasi Kerja sebagai Variabel Intervening (Studi Kasus pada Karyawan Kanca Bank Syariah Indonesia Palembang)

This study aims to determine the effect of leadership style on employee performance with work motivation as an intervening variable (study on employees of Bank Syariah Indonesia Palembang branch office). The respondents of this study were 26 people. This study uses two testing methods, namely simple regression analysis and Sobel Test. The first hypothesis, the calculated F value is 0.118 with a Sig value. = 0.734 &gt; 0.05. That is, there is no significant influence of leadership style on employee performance. The second hypothesis, in the first test, the significance value of the Leadership Style variable is 0.070 (&gt; 0.05). So it can be concluded that leadership style has no significant effect on work motivation. Based on the Sobel Test, the a value is -0.412, the b value is 0.620, the Sa value is 0.0218, the Sb value is 0.158. The Test Statistic value is -1.702 and the P-Value is 0.088. That is, there is no significant effect of Leadership Style on Employee Performance with Work Motivation as an Intervening Variable.

Predicting Indian electricity exchange‐traded market prices: SARIMA and MLP approach

AbstractThis research investigates the short‐term (ST) forecasting performance of the daily prices of the Indian exchange‐traded day‐ahead (DAM) market, divided into 13 bid areas, each consisting of states with varied fundamentals. Forecasts are built employing SARIMA (seasonal autoregressive integrated moving average) and MLP (multilayer perceptron) methods. Moreover, the robustness and performance of the model is compared using the lowest error and the Diebold–Mariano (DM) test statistic values. The results indicates that the SARIMA model has high prediction accuracy with error values ranging from 1% to 5% with Southern region having the highest error of 4.53% and Northern having the least error of 1.27%. However, validation by the DM test suggests no statistical significant difference between the two models. The power generators, distribution companies, traders, policymakers, strategists and managers could use the findings for effective power management through proper planning.

To Assess the Effectiveness of Hypertonic Saline Nebulized Suctioning On Airway Clearance Among Patients On Mechanical Ventilator In Selected Hospitals

To Assess the Effectiveness of Hypertonic Saline Nebulized Suctioning On Airway Clearance Among Patients On Mechanical Ventilator In Selected Hospitals

An adaptive binned kernel density estimation method for analysis of wave energy converters

ABSTRACT This research proposes a new adaptive binned Kernel Density Estimation (KDE) method to fit significant wave height data in order to more accurately calculate the sea state parameter distribution tails. The brief details of the proposed new methodology are to utilize the fast Fourier transform to perform the convolution of the data with the kernel to find the kernel density estimates on the bin nodes. The Crámer-von Mises test statistic for the proposed adaptive KDE distribution is calculated to be 0.0926 based on a measured wave data set. The Crámer-von Mises test statistic value for the fitted 3-parameter Weibull distribution based on the identical measured data set is 251.2463, indicating a much poorer fit to the empirical distribution function. The advantages of using a more reliable and accurate contour line derived using the proposed new method for long term reliability analysis of wave energy converters have been finally substantiated.

Pengaruh Tax Avoidance dan Profitabilitas terhadap Nilai Perusahaan pada Perusahaan Farmasi yang terhadap di Bursa Efek Indonesia

This study aims to determine the effect of tax avoidance and profitability on value value in pharmaceutical companies listed on the Indonesia Stock Exchange (IDX) for the 2017-2021 period. This research is a type of associative research. The sampling technique was carried out using a purposive sampling technique. So that the sample obtained is 7 pharmaceutical companies on the IDX in 2017-2021 with a total of 35 observational data for 5 years of observation. The data collection technique used is documentation. The data analysis technique used in this study is multiple linear regression analysis with SPSS 24 software tools. The results of the tax avoidance research show that the t-test statistic value of 1.458 is smaller than t-table of 2.034, while the significant value of tax avoidance is 0.155, which is greater than 0.05, this shows that tax avoidance has no effect on firm value. Profitability shows the statistical test value of tcount of 5.988 is greater than ttable of 2.034 while the significant value of profitability of 0.000 is smaller than 0.05, this indicates that profitability has a positive and significant effect on firm value. The results of the research on tax avoidance and profitability together show that the F-count statistic value of 18.572 is greater than the F-table value of 3.29. While the significant value of 0.000 is smaller than 0.05, this indicates that there is a significant influence between tax avoidance and profitability on firm value simultaneously.