Goodness-of-fit Test Research Articles

A formal goodness-of-fit test for spatial binary Markov random field models.

Binary spatial observations arise in environmental and ecological studies, where Markov random field (MRF) models are often applied. Despite the prevalence and the long history of MRF models for spatial binary data, appropriate model diagnostics have remained an unresolved issue in practice. A complicating factor is that such models involve neighborhood specifications, which are difficult to assess for binary data. To address this, we propose a formal goodness-of-fit (GOF) test for diagnosing an MRF model for spatial binary values. The test statistic involves a type of conditional Moran's I based on the fitted conditional probabilities, which can detect departures in model form, including neighborhood structure. Numerical studies show that the GOF test can perform well in detecting deviations from a null model, with a focus on neighborhoods as a difficult issue. We illustrate the spatial test with an application to Besag's historical endive data as well as the breeding pattern of grasshopper sparrows across Iowa.

New Bounded Probability Model: Properties, Estimation, and Applications

The article introduces a new unit distribution, an extension of Tiessier distribution, characterized by a versatile hazard function capable of adopting diverse shapes such as bathtub or N-shaped curves. An exploration of the fundamental properties of this distribution is undertaken, accompanied by the implementation of the maximum likelihood estimation technique and eleven alternative methods to approximate its parameters effectively. Through a simulation study, the article effectively demonstrates the precision of these parameter estimation methods, even when dealing with small sample sizes. Two datasets are employed to apply the novel distribution, subjecting it to a comprehensive evaluation against established models utilizing a range of model selection criteria and goodness of fit tests. Notably, the article illustrates that the performance of the new distribution surpasses that of existing models in effectively capturing data patterns. Beyond its empirical contributions, the article highlights the potential cross-disciplinary applications of the new distribution in many fields while concurrently advancing the realms of probability theory and statistical inferences.

Goodness-of-fit testing for normality where alternative distributions have undefined or constant skewness and excess kurtosis

The aim of the paper is to examine goodness-of-fit testing for normality where alternative distributions have undefined or constant skewness and excess kurtosis. The first step involves collecting a set of normality-oriented goodness-of-fit tests (GoFTs) recommended for use in the source literature. The second step is to create a family of distributions with undefined or constant skewness and excess kurtosis. For greater clarity, the alternative distributions have been divided into three groups. Group I consisted of symmetric alternatives with undefined excess kurtosis, group II of symmetric alternatives with constant non-zero excess kurtosis, and group III comprised asymmetric alternatives with constants non-zero skewness and non-zero excess kurtosis. An appropriate similarity measure (SM) of a given alternative distribution to the normal distribution was applied. The third step involved a Monte Carlo simulation. The obtained results indicate that the GoFT power for the analysed alternatives and for a given sample size remain relatively unchanged despite the significant decrease in SM. Several GoFTs proved incapable of making a distinction between normal distribution and the alternatives. This situation occurs even when SM equals 0.15. If SM equals 1, then the probability density functions (PDFs) are identical. The best tests for group I are the Gel-Miao-Gastwirth (SJ) and Robust Jarque- Bera (RJB) tests (positive excess kurtosis) as well as the Coin and Chen-Shapiro (CS) tests (negative excess kurtosis), for group II the SJ, 1st Hosking and RJB tests, while the Zhang-Wu (ZA) and CS tests are best for group III.

Behavior of Decision Making of Purchasing the One District One Product (ODOP) Product from Champhon District, Savannakhet Province

This research aims to explore the buying behavior of One District One Product (ODOP) and analyze the factors that affect the buying behavior of One District One Product in Champhon District, Savannakhet Province, which includes 3 types of products: natural incense, saving stove and ancient salt. It is a question of opinions from the people of Kaisonpomvihan city, Savannakhet province, using a questionnaire to interview buyers and shopkeepers. Also, there is a mobilization of ideas to promote one city, one product to become a business from producer groups, group heads and village heads. The statistics used for analysis include descriptive statistics: frequency, percentage and inferential statistics used Binary logit regression analysis. The results of the study found that most of the people of KaisonPhomvihan City focus attention on Intellectual products at a moderate level for 48.9%, most of them prefer to buy the saving stove from Buang Thong Village for 72.2%, buy natural incense from Nook Kok Village for 27.8% and 61.1% were introduced by friends, followed by family and relatives for 26.7%. The decision to buy the product is wise because the features of the product are suitable for household needs for 75.6% who will buy immediately when they see the product, the place of purchase is mostly from the grocery store in the merchant's house directly because of the convenience of travel. They also buy at Sawannxay Market and Samakyxai Market in Kaisonphomvihan City, Savannakhet Province cover for 37.8%. The findings of an examination of the variables gender, age, and educational level that influence a one district one product's purchasing behavior in Champhon District, Savannakhet Province; Product, price, location, sales promotion, and customer understanding are factors that influence purchase behavior. The test of model suitability, which resulted in a statistical value of Chi-squared 78.45, Prob = 0.00, indicated that all of the initial variables had an effect on the opportunity to buy a single product in a single district. According to the model's goodness of fit test, 31.2% of the factors influencing (Odop) product's purchasing behavior can be predicted with 100% accuracy using all of the initial variables. The behavior simulation model's forecast, along with the variables influencing the purchasing decisions of a single district's worth of products, is 83.96% accurate. In Champhon District, factors including age, educational level, and customer awareness influence one district one product purchasing behavior. A bachelor's degree or higher will increase the likelihood of a customer purchasing an Odop product by 17.3%, and when a customer has a total of one point of opinion and understanding, the likelihood of a customer purchasing an Odop product in Champhon will increase by 1.3% at the level of statistical significance. These are the results of taking into account each variable.

A system of ODEs for representing trends of CGM signals

Diabetes Mellitus is a metabolic disorder which may result in severe and potentially fatal complications if not well-treated and monitored. In this study, a quantitative analysis of the data collected using CGM (Continuous Glucose Monitoring) devices from eight subjects with type 2 diabetes in good metabolic control at the University Polyclinic Agostino Gemelli, Catholic University of the Sacred Heart, was carried out. In particular, a system of ordinary differential equations whose state variables are affected by a sequence of stochastic perturbations was proposed and used to extract more informative inferences from the patients’ data. For this work, Matlab and R programs were used to find the most appropriate values of the parameters (according to the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC)) for each patient. Fitting was carried out by Particle Swarm Optimization to minimize the ordinary least squares error between the observed CGM data and the data from the ODE model. Goodness of fit tests were made in order to assess which probability distribution was best suitable for representing the waiting times computed from the model parameters. Finally, both parametric and non-parametric density estimation of the frequency histograms associated with the variability of the glucose elimination rate from blood were conducted and their representative parameters assessed from the data. The results show that the chosen models succeed in capturing most of the glucose fluctuations for almost every patient.

A nomogram with Nottingham prognostic index for predicting locoregional recurrence in breast cancer patients.

The Nottingham prognostic index (NPI) has been shown to negatively impact survival in breast cancer (BC). However, its ability to predict the locoregional recurrence (LRR) of BC remains still unclear. This study aims to determine whether a higher NPI serves as a significant predictor of LRR in BC. In total, 238 patients with BC were included in this analysis, and relevant clinicopathological features were collected. Correlation analysis was performed between NPI scores and clinicopathological characteristics. The optimal nomogram model was determined by Akaike information criterion. The accuracy of the model's predictions was evaluated using receiver operating characteristic curves (ROC curves), calibration curves and goodness of fit tests. The clinical application value was assessed through decision curve analysis. Six significant variables were identified, including age, body mass index (BMI), TNM stage, NPI, vascular invasion, perineural invasion (P<0.05). Two prediction models, namely a TNM-stage-based model and an NPI-based model, were constructed. The area under the curve (AUC) for the TNM-stage- and NPI-based models were 0.843 (0.785,0.901) and 0.830 (0.766,0.893) in training set and 0.649 (0.520,0.778) and 0.728 (0.610,0.846) in validation set, respectively. Both models exhibited good calibration and goodness of fit. The F-measures were 0.761vs 0.756 and 0.556 vs 0.696, respectively. Clinical decision curve analysis showed that both models provided clinical benefits in evaluating risk judgments based on the nomogram model. a higher NPI is an independent risk factor for predicting LRR in BC. The nomogram model based on NPI demonstrates good discrimination and calibration, offering potential clinical benefits. Therefore, it merits widespread adoption and application.

Distribution of Rain Intensity: Daily Maximum Rainfall Data in The Province of South Sumatera and West Sumatera

The high intensity of rain in May 2024 caused flooding in the Sumatra region, especially Padang City and Palembang City. The region of West Sumatra is also known as the ethalae of disasters with frequent earthquakes, floods, and landslides. In Indonesia, many frequency analyses are conducted using the Gumbel distribution without testing data and clear hydrological reasons. It is feared that this method is considered as a routine way, thus causing the risk of unwanted deviations, resulting in many disasters and errors in the planning of water buildings. This research aims to determine and know the type of distribution that is representative for the maximum daily rainfall frequency in West Sumatra and South Sumatra Provinces as a reference for development and disaster mitigation in the region. The research location is in West Sumatra and South Sumatra. The data used for this study were taken from daily maximum rainfall data from rain stations for 35 to 40 years from 24 rain stations in West Sumatra Province, and 16 rain stations for South Sumatra region. This type of research is development research with the 4D development model (Define, Design, Develop, Disseminate). The development method consists of four different phases: Initial Investigation, Design, Realisation and Development. The data of each station was then organised into two types of data series, namely the annual maximum data series and the annual minimum data series. The results of this data series test are expected to follow one or more types of distributions commonly used in Hydrology and Drainage. The distribution results obtained for the South Sumatra region are Gama-III and LP-III, while for the West Sumatra region the distribution types are Gama-III, LP-III Normal Log and Gembel. The conclusion obtained is that West Sumatra does not have only one type of distribution. For the type of test using the SPSS.22 application, the results obtained are goodness of fit test, parametric test, Chi-Square test, Kolmogorov Smirnov test and Anderson-Darling test and Histrogram (visual) method.

A New Goodness of Fit Test for Complete or Type II Right Censored Samples

This study proposes a new goodness-of-fit test based on the empirical distribution function for complete or type II right-censored random samples, which are drawn from either the exponential or log-normal distributions. Some simulation studies were conducted to compare the newly proposed test with some of the well-known goodness-of-fit tests, such as Kolmogorov-Smirnov, Cramer-von Mises, and Anderson-Darling, in terms of power over various sample sizes and censoring rates. The simulation results show that the newly proposed goodness of fit test generally seems to perform well compared to the other goodness of fit tests considered. In addition, the newly proposed test and the other goodness of fit tests are illustrated by applying them to some real data sets obtained from the relevant literature.

PENGARUH E-WOM DAN KERAGAMAN PRODUK TERHADAP MINAT BELI DAN KEPUTUSAN PEMBELIAN PRODUK MAKANAN PADA PENGGUNA GOFOOD, DENGAN PRICE DISCOUNT SEBAGAI VARIABEL PEMODERASI

This research aims to determine the significant effect of E-WoM and product diversity on purchasing intentions and purchasing decisions, as well as the significant impact of price discounts in moderating the impact of E-WoM on purchasing decisions. The data source of this research was obtained from the answers of 95 respondents. The sample determination technique used non-probability sampling, namely purposive sampling. The object of this research is Gofood users of Geprek Lek Djo and Zest Chicken. The data analysis used SmartPLS by conducting several tests namely Convergent Validity Test, Reliability Test, Classical Assumption Test, Goodness of Fit Test, Coefficient of Determination Test, Structural Equation Analysis, and Research Hypothesis Test. Test results showed that E-WoM had a positive but not significant effect on purchase intention (H1 was rejected), product diversity had a significant positive effect on purchase intention (H2 was accepted), E-WoM had a positive but not significant effect on purchase decision (H3 was rejected), product diversity had a positive but not significant effect on purchasing decisions (H4 was rejected), purchase intention had a significant positive effect on purchasing decision (H5 was accepted), price discounts had a positive but not significant effect in moderating the impact of E-WoM on purchasing decision (H6 was rejected), E-WoM had a positive but not significant effect on purchasing decision through purchase intention (H7 was rejected), product diversity had a significant effect on purchasing decision through purchase intention (H8 was accepted).

A Monte Carlo resampling framework for implementing goodness‐of‐fit tests in spatial capture‐recapture models

Abstract Spatial capture‐recapture (SCR) models provide estimates of animal density from spatially referenced encounter data and has become the most widely adopted approach for estimating density. Despite the rapid growth in the development and application of spatial capture‐recapture methods, approaches for assessing model fit have received very little attention when compared to other classes of hierarchical models in ecology. Here, we develop an approach for testing goodness‐of‐fit (GoF) for frequentist SCR models using Monte Carlo simulations. We derive probability distributions of activity centres from the fitted model. From these, we calculate the expected encounters in the capture history based on the SCR parameter estimates, propagating the uncertainty of the estimates and the activity centre locations via Monte Carlo simulations. Aggregating these test statistics result in count data, allowing us to test fit with Freeman‐Tukey tests. These tests are based on summary statistics of the total encounters of each individual at each trap (FT‐ind‐trap), total encounters of each individual (FT‐individuals) and total encounters at each trap (FT‐traps). We assess the ability of these GoF tests to diagnose lack of fit under a range of assumption violating scenarios. FT‐traps had the strongest response to unmodelled spatial and trap heterogeneity in detection probability (power = 0.53–0.56), while FT‐ind‐traps had the strongest responses to random individual variation in detectability (power = 0.88) and non‐spatial discrete variation in (power = 0.35). The tests, designed to diagnose poor fit in the detection parameters, were insensitive to unmodelled heterogeneity in density (power = &lt;0.001). They also demonstrated low false positive rates (&lt;0.001) when the correct models were fitted; therefore, it is very unlikely that they will provide false indications of poor model fit. We demonstrate that these GoF tests are capable of detecting lack‐of‐fit when unmodelled heterogeneity is present in the detection sub‐model. When used jointly, the combinations of test results are also able to infer the type of lack‐of‐fit in certain cases. Our Monte Carlo sampling methods may be extended to a wider range of GoF tests, thereby providing a platform for developing more GoF methods for SCR.

Kombinasi Model Trip Generation-Modal Split (G-MS) Dalam Perencanaan Bus Bandara Terintegrasi

The modal split model can be varied to other model in the concept of four step models transportation. This approach model can be varied depending on the purpose of transport planning, such as the modal split planning process which is carried out at the trip generation calculation stage, and in this process the movement of public transport can be directly separated from private transport with the assumption that someone who will travel can choose the model that is influenced by socioeconomic valiable. The model combined experiment (G-MS) was carried out on the route to and from Jambi’s Sultan Thaha Airport, which is one of the transportation nodes that plays an important role in regional development in Jambi City. The aim of this research is to analyze potential demand using a combination of the Trip Generation-Modal Split (G-MS) model. Based on the results of the analysis using the binomial ratio logit model with the value Y = Proportion of passengers/days. The passenger distribution area is devided base on zones where the zones that have the potential to be passenger gathering point are zone 9, zone 10 and zone 11. Calculation of the chi-square test (goodness of fit test) shows that the calculation X2 value is 11.583 and the table X2 is 14.067 because X2 count &lt;X2 table, then H0 is accepted which means the observation results are the same as the average model results so that model validation is declared successful, so this research can be used to calculate passenger demand to plan airport buses.

Using the Weibull Distribution to Analyze the Reliability of Water Turbidity Removal and the Amount of Chlorine Remaining in the Water After Treatment

All water treatment centers in Iraq operate according to the standards specified in the legal regulations. Therefore, periodic evaluations must be conducted to study and analyze the quality of the outputs of these centers. The research used the two-parameter Weibull distribution to evaluate the reliability of turbidity removal and residual chlorine in the water at the Rashidiya water center in Baghdad, Iraq. The distribution parameters were estimated using the Maximum Likelihood method with the Matlab 2022 program, and the chi-square test for Goodness of Fit was used at a significant level of 0.05. The study found that the mean (and median = 9 NTU) of turbidity values were 9.47 NTU, which is within the permissible limit, and the mean (and median = 0.03 mg/L) of chlorine values were 0.0295 mg/L, much less than the permissible limit. The study also found that the average efficiency of turbidity removal was 72.179%, which is relatively acceptable, but needs improvement. The analysis showed that the value of reliability for the removal of turbidity was 0.99 ,and for the remaining amount of chlorine was 0.99, indicating that the Rashidiya water center was reliable during the study period. The study recommended several recommendations, the most important of which is the evaluation of all water treatment standards in the Rashidiya water center in particular and in all water treatment centers in Baghdad in general. Maintain continuous turbidity and chlorine levels monitoring to ensure compliance with permissible limits. Make efforts to improve the turbidity removal efficiency. Fostering collaboration between water treatment centers, researchers and regulatory agencies. Develop public awareness campaigns to educate the public on the importance of clean and safe water.

Maternal perception of decreased fetal movements is independent of infant size.

Fetal movements are often used as a surrogate for fetal wellbeing. Previous research suggests a link between maternal perception of decreased fetal movements (DFM) and small for gestational age (SGA) infants. The aim of this study was to investigate the association between maternal presentation with DFM and birthweight centile categories at a large Australian perinatal center. This was a 5-year cohort study (January 2016 to October 2020) of non-anomalous singleton infants born at the Mater Mothers' Hospital in Brisbane, Australia at 28+0 weeks gestation or later. The primary outcome for this study was the rate of DFM by birthweight centile categories. Maternal demographics included age, body mass index, ethnicity, parity, medical conditions, and previous stillbirths. The effect of decreased fetal movements on birthweight centile was evaluated with adjusted multinomial regression models. Adjusted logistic regression models were then used to evaluate whether decreased fetal movements resulted in birthweight centiles <5 and <10. Robust standard errors were used to account for clustering at the patient level. Wald tests, Hosmer and Lemeshow's goodness of fit tests, Akaike's and Bayesian Information Criteria were used to evaluate models. Over the 5-year study period, 45042 women met the inclusion criteria. Of these, 6690 (14.9%) women presented with DFM. Of the DFM cohort, 80.9% (5411/6690) had only one presentation with DFM, whilst 19.1% (1279/6690) had >2 presentations. The overall stillbirth rate was similar in women with DFM (0.1%, 8/6690) and without DFM (0.1%, 50/38352). There was no difference in rates of DFM (either single or multiple) vs. no DFM and infant birthweight centile categories. There was no association between DFM (either single or multiple) and infant birth weight centile. The results of this study suggest that presentation with DFM is not associated with infant size. Clinicians should consider additional risk factors and the overall clinical context in deciding appropriate management. DFM is not necessarily an indication for an immediate or urgent ultrasound scan to assess fetal size. This article is protected by copyright. All rights reserved.

ANALISIS PENGARUH FAKTOR KINERJA PEGAWAI DINAS PARIWISATA DAN KEBUDAYAAN KABUPATEN SOLOK SELATAN

Employee Performance shows the work performance achieved by employees in accordance with their responsibilities. The aim of the research is how organizational culture, leadership and rewards together influence the performance of employees at the Tourism and Culture Office of South Solok Regency. This study uses quantitative methods. In this research, the population is all Civil Servants at the South Solok Regency Tourism and Culture Office, totaling 35 people. The sample is all members of the population used as a sample, namely 35 civil servants at the South Solok Regency Tourism and Culture Office. The data analysis technique used in this research is multiple regression analysis. The results of this research show that Organizational Culture, Leadership, and Rewards together have a significant positive effect on Employee Performance. Next, to see whether this model is good or not can be seen from the goodness of fit test. The determinant coefficient (R2) is good, shown by the contribution of the independent variables consisting of Organizational Culture, Leadership, Rewards to the Performance variable of 66.9% and the contribution of other factors is 33.1%.

A study on refractive error patients in chennai city using structural equation modelling

A lot of individuals in Chennai are suffering from refractive errors. Myopia, hyperopia, astigmatism, and presbyopia are among the majority prevalent forms of corneal abnormalities. The existence of blurred vision in both the left and right eyes in people who use contact lenses is found by 17 independent and 2 dependent variables using Structural Equation Modelling (SEM). The results are interpreted by Tests for goodness of fit in models, test for individual Parameter estimate for both independent and dependent variables and the linear relationship between the variables were performed.

Analysis of Factors Influencing the Transformation of the Workforce from the Agricultural Sector to the Non-Agricultural Sector in Bagik Polak Village, West Lombok Regency

This study aims to analyze the influence of income, education, age and gender on the transformation of the workforce from the agricultural sector to the non-agricultural sector in Bagik Polak Village. The type of research used in this study is the associative quantitative method. The test was carried out on 94 respondents spread across 7 hamlets, namely Karang buncu lauq, Karang buncu Barat, Karang buncu daye, Rerot, Karang kebon timur, Karang kebon Barat and Enjak. Data was obtained by distributing questionnaires and sampling research using the purposive sampling method. The analysis tool used is logistic regression (logit) which has three tests including Assessing the Overall Model Fit, Testing the Feasibility of the Regression Model (Goodness of Fit Test) and Determination Coefficient (Nagelkerke's R Square). As well as statistical tests, namely the Likelihood Ratio Test (Simultaneous Test F) and the Wald Test (Partial Test t). The results of the analysis show that purely of the four variables used in this study, there are two variables that have a positive and significant effect, namely income with a significant value of 0.045&lt; 0.05; and education with a significant value of 0.019 &lt; 0.05. Meanwhile, the age variables with a significant value of 0.796 &gt; 0.05 and gender with a significant value of 0.851 &gt; 0.05 did not have a significant effect on the transformation of the workforce from the agricultural sector to the non-agricultural sector in Bagik Polak Village. Meanwhile, simultaneously that together, the independent variable (X) has an effect on the transformation of the workforce from the agricultural sector to the non-agricultural sector with a significant chi-square value of 0.004 &lt; 0.05. Meanwhile, Nagelkerke's R2 value shows that all independent variables are able to explain the dependent variable by 42.7% and the remaining 57.3% are explained by other variables that are not included in the model.

Review on Distributions Generated Using Trigonometric Functions

In the present scenario, researchers are interested in developing new distributions based on trigonometric functions to model data. Previously, data modelling was commonly done with standard and existing distribution functions. Recent research has demonstrated that trigonometrically transformed functions can be used to model a variety of data types, including data from the medical, psychiatric, industrial, and economic sectors as well as studies on life time data, observations on seasonal patterns, and data from other fields. This review article discusses some trigonometric distributions and their applications across a range of fields. Using an actual dataset, a comparison analysis of a few selected models is conducted. Maximum likelihood estimates of the model parameters and various information criteria measures are obtained, and goodness of fit tests are performed for comparison.

Modeling Extreme Values of Non-Stationary Precipitation Data with Effects of Covariates

Background/Objectives: Climate change is one of the most challenging problems of recent decades as it is highly volatile and needs a very effective scientific approach to find a solution. Further, the changes in the climate especially extreme cases have a more negative influence on day to day affairs of society. Hence, developed countries pay much attention to climate change and make policies at a global level. To find the scientific solution for these kinds of climate change, Extreme value theory offers effective methods for estimating and quantifying these types of natural hazards associated with climate. Methods: Block (Annual) maxima and peak over threshold are two strategies employed in this theory. The data observed on precipitation are mostly having non-stationary characteristics along with covariates. The generalized extreme value distribution and generalized Pareto distribution are used to model this type of non-stationary stochastic process. Findings: This study proposes a pragmatic automated dual-phase threshold selection technique that employs the entropy method to combine the results from various goodness of fit tests into a single unified measure known as the evaluation indicator, resulting in an efficient threshold for capturing extreme values. This allows for a more comprehensive examination of various thresholds using evaluation indicators and avoids assessing each test criterion individually. Novelty: In contrast to the subjective results of threshold stability plots, the dual-phase technique is based on numerical computations, which reduce bias and improve decision-making objectivity. We illustrate the applicability of the proposed technique by analyzing a precipitation dataset that includes time and wind speed as covariates. The results of the comparative analysis reveal that the proposed automated dual-phase threshold approach outperforms the peaks over threshold and annual maxima methods. Keywords: Non-stationary, Extreme values, Annual maxima, Threshold selection, Evaluation indicator

Performance Evaluation through Value Added Ratios: A Case study of Ambuja Cement Ltd with Additive Method of Value Added

This paper is a case study of Ambuja Cement Ltd for the performance evaluation. Ambuja Cement Ltd is one of the leading Cement manufacturer company in India. This study is based on 5 years of period started from 2017-18 to 2021-22. To analyze its performance, Value Added Ratios (VARs) are used. Value Added Ratios (VARs) are very important in the analysis of Value related data. Total five important ratios are used and for statistical analysis Chi-Square test is used to know whether performance of the company is consistent or not. Chi-Square – goodness of fit test is used with an assumption that performance of the company is consistent if actual data “fits” expected data. Value Added (with the help of Additive method) and Value-Added Ratios (VARs) shows that Ambuja Cement Ltd is consistent in most of its financial activity and sound in the financial performance.

The four seasons of pancreatitis - Etiology of acute pancreatitis during the course of the year.

Whether seasonality is a factor that influences the incidence of acute pancreatitis (AP) is an under-investigated area. If seasonal incidence peaks can be detected, specifically with regard to biliary pancreatitis, has so far been answered in contradictory ways in the literature. All AP cases from two tertiary German referral centers were identified between 2016 and 2022 based on ICD-10 discharge codes. The χ2 test for goodness of fit was applied to test significant differences in monthly and seasonal distributions of AP admissions. In total, 3597 AP cases were included. We observed significantly more idiopathic and biliary cases in May to July (p-values 0.041 and 0.027, respectively). Furthermore, most drug-induced APs were identified during the winter months (p-value 0.006). Moreover, there was a significant peak of alcohol-induced pancreatitis in summer and fall (p-value 0.038). Our data indicate a seasonal impact on AP incidences for certain etiologies.