Value Of Test Statistic Research Articles

HUBUNGAN JENJANG KARIR DENGAN KEPUASAN KERJA PERAWAT

Job satisfaction is an employee's attitude towards work related to work situations, cooperation between employees, rewards received at work, and matters relating to physical and psychological factors. In Indonesia, there are 55.8% of nurses in government hospitals experiencing low job satisfaction. There are several factors that influence job satisfaction including salary earned, organizational culture, relationships with coworkers, supervision, and career development. Career level is one of the efforts to realize nurse job satisfaction. Nursing career level is the implementation of several career plans that have been determined to provide nurses with positions/levels that are in accordance with the education, abilities, skills and competencies of nurses. The purpose of this study was to find out the career level relationship with Job Satisfaction of nurses at Sari Mutiara General Hospital Lubuk Pakam. The research design used is descriptive correlation with cross sectional design. The population in this study were all nurses at Sari Mutiara General Hospital Lubuk Pakam. The sample in this study amounted to 56 people obtained by using total sampling. The data collection tool uses a questionnaire sheet. The statistical test used was the chi square test. The results showed the majority of respondents had a high career level 69,6% and respondents’ performance satisfaction was satisfied as much as 67,8%. Based on the results of the Chi-Square statistical test value = 0,017 (p <0,05) which means that there is arelationship of career level with the satisfaction of nurse performance at Sari Mutiara General Hospital Lubuk Pakam It is recommended for nurses to improve their skills through training and education facilitated by hospitals.

“THE STUDY TO ASSESS AN INCIDENCE OF FEVER IN POST-OPERATIVE PATIENTS ADMITTED IN SURGICAL ICU AT NATIONAL CANCER INSTITUTE, DHARAMPETH, NAGPUR.”

INTRODUCTION: Postoperative fever is one of the most common problems seen in the postoperative ward. Most cases of fever immediately following surgery are self-limiting. The appearance of postoperative fever is not limited to specic types of surgery. Fever can occur immediately after surgery and seen to be related directly to the operation or may occur sometime after the surgery as a result of an infection at the surgical site or infections that involve organs distant from the surgery. Objectives: To study the common causes of post operative fever in general surgery patients. To study the correlation between the cause and the day of onset of fever. To study the risk factors associated with post operative fever. Material and Method: In this study Descriptive Research Design was used. The samples were 30 Post operative patients which fulls inclusion criteria. Setting of the study was surgical ICU, National cancer Institute, Dharampeth, Nagpur. RESULTS:-The result of this study shows that There 6 patients in the age group of 41yrs to 60 yrs having increase WBC count. Fisher exact test statistic value is 0.0449. The result is signicant at p < .05. so the post operative fever is signicantly associated with gender of the patient, Types of surgery and increase WBC count in Patient.

Effect Of Return On Asset And Debt To Equity Ratio To Tax Avoidance In Company

The debt equity ratio shows the t-count statistical test value of 0.405, which is smaller than the t-table of 2.032, while the sig value of 0.688 is greater than 0.050, this indicates that the debt equity ratio has no effect on tax avoidance. The results of the research on return on assets and debt equity ratio together show the calculated F statistic value of 5.143 which is greater than the F table value of 3.280 while the sig value of 0.011 is smaller than 0.05, this indicates a significant influence between return on assets and debt equity ratio to tax avoidance simultaneously. This shows that return on assets has a positive and significant effect on tax avoidance. The debt equity ratio shows the t-count statistical test value of 0.405, which is smaller than the t-table of 2.032, while the sig value of 0.688 is greater than 0.050, this indicates that the debt equity ratio has no effect on tax avoidance. The results of the research on return on assets and debt equity ratio together show the calculated F statistic value of 5.143 which is greater than the F table value of 3.280 while the sig value of 0.011 is smaller than 0.05, this indicates a significant influence between return on assets and debt equity ratio to tax avoidance simultaneously. This shows that return on assets has a positive and significant effect on tax avoidance. The debt equity ratio shows the t-count statistical test value of 0.405, which is smaller than the t-table of 2.032, while the sig value of 0.688 is greater than 0.050, this indicates that the debt equity ratio has no effect on tax avoidance. The results of the research on return on assets and debt equity ratio together show the calculated F statistic value of 5.143 which is greater than the F table value of 3.280 while the sig value of 0.011 is smaller than 0.05, this indicates a significant influence between return on assets and debt equity ratio to tax avoidance simultaneously. 405 is smaller than t table of 2.032 while the sig value of 0.688 is greater than 0.050, this shows that the debt equity ratio has no effect on tax avoidance. The results of the research on return on assets and debt equity ratio together show the calculated F statistic value of 5.143 which is greater than the F table value of 3.280 while the sig value of.

New methods for multiple testing in permutation inference for the general linear model.

Permutation methods are commonly used to test the significance of regressors of interest in general linear models (GLMs) for functional (image) data sets, in particular for neuroimaging applications as they rely on mild assumptions. Permutation inference for GLMs typically consists of three parts: choosing a relevant test statistic, computing pointwise permutation tests, and applying a multiple testing correction. We propose new multiple testing methods as an alternative to the commonly used maximum value of test statistics across the image. The new methods improve power and robustness against inhomogeneity of the test statistic across its domain. The methods rely on sorting the permuted functional test statistics based on pointwise rank measures; still, they can be implemented even for large data. The performance of the methods is demonstrated through a designed simulation experiment and an example of brain imaging data. We developed the R package GET, which can be used for the computation of the proposed procedures.

Search for Line-like and Box-shaped Spectral Features from Nearby Galaxy Clusters with 11.4 Years of Fermi Large Area Telescope Data

Abstract Sharp spectral structures in the γ-ray band are an important dark matter (DM) signature. Previously, a tentative line feature at ∼43 GeV was reported in 16 nearby galaxy clusters (GCls) with 7.1 yr of Fermi Large Area Telescope (LAT) data, whose test statistic (TS) value is ∼16.7. In this work, we search for line signals and box-shaped structures using the stacked data from those 16 GCls with 11.4 yr of Fermi-LAT P8R3 data. There is still a hint at ∼42 GeV, dominated by the radiation of the Virgo and Ophiuchus clusters. Though the TS value was high up to 21.2 in 2016 October, currently it has dropped to 13.1. Moreover, the TS value at ∼42 GeV decreases to 2.4 when the EDISP2 data are excluded from the analysis. Consequently, we do not find any statistically significant line-like signal and then set up the 95% confidence level upper limits on the thermally averaged cross section of DM annihilating into double photons. The same line search has been carried out for an alternative GCl sample from the Two Micron All-Sky Survey, but no evidence has been found. We also search for box-shaped features in those 16 baseline GCls. No signal is found, and the corresponding upper limits on the annihilation cross section are given.

PENGARUH PENGALAMAN KERJA, AKUNTABILITAS DAN BESARAN FEE AUDIT TERHADAP KUALITAS AUDIT PADA KAP DI INDONESIA TAHUN 2021

A public accountant (auditor) who has a lot of experience, has an attitude towards what is reported or is reported to be responsible in the company's financial statements is very necessary at this time. That way, an auditor who has a high attitude of responsibility will be trusted to provide good audit quality. The data obtained in this study came from a questionnaire filled out by 50 respondents who are auditors or public accountants (auditors) registered at the Indonesian Institute of Certified Public Accountants. The results show that the variability of audit quality that can be explained by work experience, accountability and the amount of audit fees is only 35%. The results of the statistical test value stated that if work experience had a significance of 0.1825 (>0.05), accountability had a significance value of 0.007 (0.05). The result is that work experience, accountability and the amount of audit fees together have an effect on audit quality. Partially, only accountability has a positive effect on audit quality, while work experience and the amount of audit fees have no effect on audit quality.

Exploring the characteristics of the flare from PMN J0218–2307 by Fermi-LAT

PMN J0218–2307 (4FGL J0218.9–2305) is classified as a blazar candidate with unknown type (BCU) in the fourth source catalog from the Fermi Large Area Telescope (Fermi-LAT). With the updated Fermi-LAT Pass 8 data, the γ-ray flaring activity toward PMN J0218–2307 is detected. The test statistic (TS) value of PMN J0218–2307 in energy band of 100 MeV–500 GeV is 133.893 with a significance level of 10.96σ. The maximum-likelihood photon flux is (8.131 ± 1.359) × 10−9 ph cm−2 s−1. A significant γ-ray flare in the period from 2008 August 4 to 2019 August 25 is found from the source. The spectral characteristics of GeV energy band of PMN J0218–2307 is similar to that of flat-spectrum radio quasars (FSRQs) in the local Universe.

Forensic Accounting and Fraud Reduction Strategies in Developing Economy

This paper examined forensic accounting capability and useful strategies to minimize the effects of financial crimes and other related frauds in Nigeria economy. Various points at which preventive controls can be established were identified.Data were collected from two hundred and fifty five (255) respondents in the south-west geo-political zone of Nigeria and response on capability and suitability of using forensic accounting principles and measures to prevent undue practice that often arises via financial crimes in the economy were analyzed with Analysis of Variance Method. The results showed that the estimated Z-statistic was 115.3736.The critical table value of the Z-statistic at 5% level of significance (95% confidence level) was obtained as 1.645. Since the estimated test statistic (Z-statistic) value exceeds the critical table value at 95% confidence level, the null hypothesis is rejected in favour of the alternative hypothesis. Thus, the study concludes that forensic Accounting principles is capable of eliminating all types of financial frauds in any economy. It was also empirically found that forensic accounting can be used to locate diverted funds or assets, identify misappropriated assets and identified reversible insider transactions, thus, forensic accounting is useful as effective fraud detection tool, detects suspicious fraudulent transactions and covers risk assessment processes. Based on the above the study recommends the adoption of forensic accounting principles, effective appropriation of financial resources and external auditors employment.

Optimization of hydrogen production via catalytic autothermal reforming of crude glycerol using response surface methodology and artificial neural network

In this work, the central composite design of the response surface methodology (RSM) and artificial neural network (ANN) were deployed to investigate process variables and optimize hydrogen gas production via autothermal reforming of crude glycerol using 5%Ni/CeZrCa catalyst in a packed bed tubular reactor. The input variables studied are reaction temperature, feed flow rate, steam to carbon ratio (S/C), oxygen to carbon ratio (O/C), and catalyst weight. The neural network analysis of these input variables measured their individual significance and contribution in the autothermal reforming process prior to the development of a quadratic polynomial model with RSM for predicting hydrogen yield as well as optimization. Statistical analyses showed that the mathematical model developed excellently represents the data with high model adequacy and no significant lack of fit. The results of the neural network input variable significance ranking approach showed that the reaction temperature contributed more than any other independent variable at a ranking weight of 53% while the O/C ratio showed the least impact at 3%. The results of the optimization gave the optimum parameters for hydrogen gas yield as follows; Temperature: 650°C, crude glycerol flow rate: 3.3 mmol C/min, S/C: 2.34, O/C: 0.052, and catalyst weight of 0.15 g with a maximum feed conversion of approximately 92% and 97% H2 yield. ANN provided a better regression with excellent statistical test values as follows R2: 0.999, MSE: 10−8, AAD: 0.34%, and RMSE: 3.5 × 10−4%. It is clear from the results of the model adequacy and error calculations that ANN is superior and has shown a better model fit than the RSM.

PCM Transformation: Properties and Their Estimation

In the present piece of work, we are going to propose a new trigonometry based transformation called PCM transformation. We have been obtained its various statistical properties such as survival function, hazard rate function, reverse-hazard rate function, moment generating function, median, stochastic ordering etc. Maximum Likelihood Estimator (MLE) method under classical approach and Bayesian approaches are tackled to obtain the estimate of unknown parameter. A real dataset has been applied to check its fitness on the basis of fitting criterions Akaike Information criterion (AIC), Bayesian Information criterion (BIC), log-likelihood (-LL) and Kolmogrov-Smirnov (KS) test statistic values in real sense. A simulation study is also being conducted to assess the estimator’s long-term attitude and compared over some chosen distributions.

Evaluation of Psychiatric Morbidity in COVID-19-Positive Inpatients Referred to Consultation Liaison Psychiatry in a Tertiary Care Hospital.

Background:The COVID-19 pandemic has led to the risk of common mental illnesses. Consultation liaison psychiatry has been one of the most requested services in the face of this pandemic. We aimed to assess (a) the prevalence of psychiatric illness, (b) different types of psychiatric diagnoses, (c) presenting complaints, (d) reasons for psychiatric referrals, and (e) psychiatric intervention done on COVID-19 positive inpatients referred to consultation liaison psychiatry at tertiary care hospital.Method:This was a retrospective study of data collected from April 1, 2020, to September 15, 2020. Total 300 patients were referred and diagnosed with clinical interview and Diagnostic and Statistical Manual for Mental Disorder Fifth Edition criteria. Analysis was done using chi-square test, Kruskal–Wallis test, and fisher exact test.Results:Out of 300 patients, 26.7% had no psychiatric illness. Adjustment disorder was the commonest psychiatric diagnosis (43%), followed by delirium (10%). Statistically significant differences were found for parameters like Indian Council of Medical Research Category 4 of the patient, (hospitalized severe acute respiratory infection) (P value < 0.001), medical comorbidity (P value = 0.023), and past history of psychiatric consultation (Fisher exact test statistic value <0.001). Behavioral problem (27.6%) was the commonest reason for psychiatric referral. Worrying thoughts (23.3%) was the most frequent complaint. A total of 192 (64.3%) patients were offered pharmacotherapy.Conclusions:Psychiatric morbidity was quite high (73.3%) among them and adjustment disorder was the commonest (43%) psychiatric diagnosis followed by delirium (10%). Pharmacotherapy was prescribed to 64.3% patients and psychosocial management was offered to most of the referred patients.

English

The molecular diversity of Tomato spotted wilt virus (TSWV) isolates from Pakistan was investigated based on nucleocapsid protein (N) gene. It revealed that the Pakistani isolates shared 98.2–99.3% (nt) and 95.7–97.4% (aa) identities with each other and 97.2–99.3% (nt) and 92.7–97.8% (aa) with other TSWV isolates retrieved from Genbank. In a neighbour-joining phylogenetic reconstruction, four Pakistani isolates clustered with South Korean and Turkish isolates and one with Australian and South African isolates. All the Pakistani isolates shared maximum genetic diversity (0.024–0.039 and 0.026–0.029) with South African (AJ296600) and Australian (AY879109) isolates and the lowest values of genetic diversity (0.009–0.013) was recorded with Czech Republican isolate (AJ296599). The gene differentiation co-efficient value (Fst) was 0.33 less than standard value noticed as 0.05145, between globally reported and Pakistani TSWV isolates considering it a frequent gene flow. The significant values of statistical tests based on three genetic differentiation analysis; Ks⃰; 4.86231, Z; 34.27778 and Snn; 0.55556 were observed. The negative values were recorded according to statistical test as Fu, &amp; Li’s D ⃰ (-2.55465), Fu, &amp; Li’s F ⃰ (-2.78103) and one positive value from Tajima’s D (2.10697). These results indicate low polymorphism frequency in TSWV population of Pakistan. © 2021 Friends Science Publishers

Small Sample Adjustment for Hypotheses Testing on Cointegrating Vectors

Abstract Johansen’s (2000. “A Bartlett Correction Factor for Tests of on the Cointegrating Relations.” Econometric Theory 16: 740–78) Bartlett correction factor for the LR test of linear restrictions on cointegrated vectors is derived under the i.i.d. Gaussian assumption for the innovation terms. However, the distribution of most data relating to financial variables is fat-tailed and often skewed; there is therefore a need to examine small sample inference procedures that require weaker assumptions for the innovation term. This paper suggests that using the non-parametric bootstrap to approximate a Bartlett-type correction provides a statistic that does not require specification of the innovation distribution and can be used by applied econometricians to perform a small sample inference procedure that is less computationally demanding than it’s analytical counterpart. The procedure involves calculating a number of bootstrap values of the LR test statistic and estimating the expected value of the test statistic by the average value of the bootstrapped LR statistic. Simulation results suggest that the inference procedure has good finite sample property and is less dependent on the parameter space of the data generating process.

Prediction Research and Application of Financial Time Series Based on Big Data

Financial risk is conductive in the market. In this paper, correlation function is used to model the relevant structure of the financial market. The j-B test statistic value of four stocks is far greater than the critical value of 6.7325, and the critical value of LJung-Box-Pierce test is 32.1342. At the same time, it is proved that the selection of edge distribution function is independent of the dependent structure, but the risk value estimated by semi-parametric POT model is higher than that estimated by kernel density function.

Gambaran Kepuasan Hidup Pelajar Sekolah Menengah Di Masa Pandemi COVID-19

Tujuan dari penelitian ini adalah untuk mengetahui gambaran kepuasan hidup pelajar sekolah menengah dimasa pandemi Covid-19 berdasarkan aspek-aspek yang telah diuraikan dalam angket penelitian. Metode penelitian menggunakan kuantitatif deskriptif dengan teknik pengumpulan data menggunakan Multidimensional Student’s Life Satisfaction Scale (MSLSS) dengan 40 butir pernyataan. Jumlah sampel pada penelitian ini adalah 125 orang dengan teknik sampel yang digunakan adalah probability sampling. Pada uji prasayarat analisis menggunakan kolmogrov-smirnov dan lilliefors dengan menggunakan SPSS microsoft for windows versi 25 menunjukkan nilai test statistic 0,762 &gt; 0,05 dan sig. Lilliefors 0,606 &gt; 0,05 dinyatakan data berdistribusi normal. Berdasarkan hasil penelitian diperoleh hasil sangat tinggi sebanyak 5% (6 peserta didik), kategori tinggi sebanyak 34% (42 peserta didik), kategori cukup sebanyak 41% (51 peserta didik), kategori rendah sebanyak 13% (17 peserta didik), dan kategori sangat rendah sebanyak 7% (9 peserta didik) pada kepuasan hidup peserta didik usia remaja di masa pandemi COVID-19. Sehingga dapat dikatakan bahwa secara keseluruhan peserta didik SMP Muhammadiyah 16 Jakarta, MTs Kafila Jakarta, dan SMP Global Islamic School Jakarta; memiliki kepuasan hidup yang cukup, ini menandakan bahwa di masa pandemi COVID-19 yang mengalami perubahan situasi serta keterbatasan akses dalam berbagai aspek kehidupan, peserta didik masih mampu bertahan dan beradaptasi.

On the Generalized Odd Transmuted Two-Sided Class of Distributions

In this paper, a general class of two-sided lifetime distributions is introduced via odd ratio function, the well-known concept in survival analysis and reliability engineering. Some statistical and reliability properties including survival function, quantiles, moments function, asymptotic and maximum likelihood estimation are provided in a general setting. A special case of this new family is taken up by considering the exponential model as the parent distribution. Some characteristics of this specialized model and also a discussion associated with survival regression are provided.A simulation study is presented to investigate the bias and mean square error of the maximum likelihood estimators. Moreover, two examples of real data sets are studied; point and interval estimations of all parameters are obtained by maximum likelihood and bootstrap (parametric and non-parametric) procedures. Finally, the superiority of the proposed model over some common statistical distributions is shown through the different criteria for model selection including loglikelihood values, Akaike information criterion and Kolmogorov-Smirnov test statistic values.

Hubungan Kunjungan Trimester 1 dengan Screening Anemia pada Ibu Hamil di Era Peradaban Baru

The Covid-19 pandemic gave birth to a new civilization era where people were asked to adapt to this pandemic condition. This also applies to maternal and child health services, especially antenatal care. The highest cause of death in Indonesia in 2019 is due to bleeding, which can be influenced by low hemoglobin levels or what is commonly known as anemia. The purpose of this study was to analyze the relationship between first trimester visits and anemia screening in pregnant women. The research method used was cross sectional, using google form media to fill out questionnaires and screen shots of the MCH book. The statistical test in this study used Pearson. The results showed that there was no relationship between the first trimester visit and anemia screening, which was obtained by the Pearson statistical test value sig = 0.189&gt; 0.05, which means that there was no relationship between the first trimester visit and the examination of hemoglobin levels in pregnant women. The conclusion that can be drawn is that even though we are now entering a new civilization era where there are several regions that implement Large-Scale Social Restrictions and which in Maternal and Child Health services, especially pregnancy examinations, can still be implemented, government policies in the new civilization era during the 1st trimester visit are focused on screening of maternal health conditions for complications in pregnancy, and especially regarding referrals if there are complications in her pregnancy and management of pregnant women with Covid-19, so that this can prevent transmission of Covid-19.

Fitting latent growth models with small sample sizes and non-normal missing data

This study investigates the performance of robust maximum likelihood (ML) estimators when fitting and evaluating small sample latent growth models with non-normal missing data. Results showed that the robust ML methods could be used to account for non-normality even when the sample size is very small (e.g., N < 100). Among the robust ML estimators, “MLR” was the optimal choice, as it was found to be robust to both non-normality and missing data while also yielding more accurate standard error estimates and growth parameter coverage. However, the choice “MLMV” produced the most accurate p values for the χ2 test statistic under conditions studied. Regarding the goodness of fit indices, as sample size decreased, all three fit indices studied (i.e., comparative fit index, root mean square error of approximation, and standardized root mean square residual) exhibited worse fit. When the sample size was very small (e.g., N < 60), the fit indices would imply that a proposed model fit poorly, when this might not be actually the case in the population.

Prevalence and serotypes of Shiga toxin-producing Escherichia coli (STEC) in dairy cattle from Northern Portugal.

The prevalence of Shiga toxin (Stx)-producing Escherichia coli (STEC) was determined by evaluating its presence in faecal samples from 155 heifers, and 254 dairy cows in 21 farms at North of Portugal sampled between December 2017 and June 2019. The prevalence of STEC in heifers (45%) was significantly higher than in lactating cows (16%) (p<0.05, Fisher exact test statistic value is <0.00001). A total of 133 STEC were isolated, 24 (13.8%) carried Shiga-toxin 1 (stx1) genes, 69 (39.7%) carried Shiga-toxin 2 (stx2) genes, and 40 (23%) carried both stx1 and stx2. Intimin (eae) virulence gene was detected in 29 (21.8%) of the isolates. STEC isolates belonged to 72 different O:H serotypes, comprising 40 O serogroups and 23 H types. The most frequent serotypes were O29:H12 (15%) and O113:H21 (5.2%), found in a large number of farms. Two isolates belonged to the highly virulent serotypes associated with human disease O157:H7 and O26:H11. Many other bovine STEC serotypes founded in this work belonged to serotypes previously described as pathogenic to humans. Thus, this study highlights the need for control strategies that can reduce STEC prevalence at the farm level and, thus, prevent food and environmental contamination.

Finite Sample Change Point Inference and Identification for High-Dimensional Mean Vectors

AbstractCumulative sum (CUSUM) statistics are widely used in the change point inference and identification. For the problem of testing for existence of a change point in an independent sample generated from the mean-shift model, we introduce a Gaussian multiplier bootstrap to calibrate critical values of the CUSUM test statistics in high dimensions. The proposed bootstrap CUSUM test is fully data dependent and it has strong theoretical guarantees under arbitrary dependence structures and mild moment conditions. Specifically, we show that with a boundary removal parameter the bootstrap CUSUM test enjoys the uniform validity in size under the null and it achieves the minimax separation rate under the sparse alternatives when the dimension p can be larger than the sample size n.Once a change point is detected, we estimate the change point location by maximising the ℓ∞-norm of the generalised CUSUM statistics at two different weighting scales corresponding to covariance stationary and non-stationary CUSUM statistics. For both estimators, we derive their rates of convergence and show that dimension impacts the rates only through logarithmic factors, which implies that consistency of the CUSUM estimators is possible when p is much larger than n. In the presence of multiple change points, we propose a principled bootstrap-assisted binary segmentation (BABS) algorithm to dynamically adjust the change point detection rule and recursively estimate their locations. We derive its rate of convergence under suitable signal separation and strength conditions.The results derived in this paper are non-asymptotic and we provide extensive simulation studies to assess the finite sample performance. The empirical evidence shows an encouraging agreement with our theoretical results.