Influential Observations Research Articles

A new parametric quantile regression model based on an Owen distribution

Quantile regression is a versatile statistical tool for modeling conditional quantiles, offering a comprehensive view of the relationship between explanatory and response variables. This technique is widely used in fields like economics, finance, environmental science, and psychology. This paper introduces a new quantile regression model based on the Owen distribution, which provides flexibility in capturing quantile-specific relationships. The model parameters are estimated using maximum likelihood estimation, with a detailed discussion on the estimation process. Diagnostic measures, including a pseudo- R 2 statistic, quantile residuals, and generalized Cook’s distance, are proposed to assess model adequacy and identify influential data points. Monte Carlo simulations are employed to evaluate the estimators' performance. The model’s effectiveness is illustrated through an application to Chilean household income data, highlighting its advantages over conventional quantile regression models. Implemented in the R programming language, this model is accessible for practical use, offering a valuable addition to the quantile regression field and suggesting promising directions for further research and application across disciplines.

Identifying influential observations in concurrent functional regression with weighted bootstrap

Identifying influential observations in concurrent functional regression with weighted bootstrap

ROBUST LEAST MEDIAN OF SQUARE MODELLING USING SEEMINGLY UNRELATED REGRESSION WITH GENERALIZED LEAST SQUARE ON PANEL DATA FOR TUBERCULOSIS CASES

Tuberculosis, primarily affecting the lungs and other organs, was the leading cause of death worldwide before the COVID-19 pandemic and continues to be a significant health concern. This research examined tuberculosis (TB) using a panel dataset. As a consequence, the datasets may contain outliers and contemporaneous correlations. A Robust Least Median of Square (LMS) model was developed in this research by combining Seemingly Unrelated Regression (SUR) with Generalized Least Square (GLS) on panel data to provide an analysis overview to overcome outliers and contemporaneous correlations. Based on secondary data obtained from the Central Bureau of Statistics of the Gorontalo Province and the Ministry of Health of the Gorontalo Province, this research examines TB cases between 2017 and 2021. The Chow test result suggests that CEM is the most appropriate model for analyzing panel data for TB cases in Gorontalo Province between 2017 and 2021. Due to the presence of outliers and influential observations in the data, robust LMS is employed. Furthermore, there is a problem of contemporaneous correlation in this research. Each regency or city can mitigate this problem by implementing robust LMS using SUR with GLS.

Lifetime minimally invasive assessment of iron in the pig liver

Microelements are an integral part of the mammalian body, and their content in organs and tissues is associated with other components of a complex biological system. Based on this, it is feasible to evaluate the concentration of specific chemical elements within the structures of the body in non-invasive or minimally invasive methods. The meat and by-products of farm animals serve as a readily assimilateable source of iron, which is one of the reasons for potentially defining the quality of agricultural products in conditions of widespread iron deficiency. Landrace pigs were raised in standard conditions at an industrial complex located in the Altai Territory in order to fatten up to a live weight of 100 kg. Venous blood was collected using the acute method from the jugular vein in accordance with the principles of asepsis and pre-analytical guidelines. The hematologic and biochemical examination of the blood and serum of animals was performed by apparatus. After slaughter, liver samples were collected, and the method of atomic emission spectral analysis using inductively coupled plasma on iCAP-PRO equipment (Thermo Fisher Scientific) was used to estimate the iron level in them. To manipulate the data, Microsoft Office Excel software and RStudio data analysis environment version 2023.03.1 (RStudio, PBC) were employed. For regression analysis, the least squares approach was used. The model was fitted using a stepwise selection of predictors in both directions using the Akaike information criterion, Bayesian information criterion, and adjusted coefficient of determination. The linear regression assumptions were evaluated. The final regression model used for determining iron levels in pig liver contains mean hemoglobin content in erythrocytes, hemoglobin, and serum inorganic phosphorus as predictors. There is no evidence that there is multicollinearity between the predictors of the final model. The proposed model satisfies the requirements for a normal distribution of residuals, the absence of their correlation, and influential observations. The proposed multiple regression model has the capability to estimate iron levels in pig liver in vivo for various purposes.

B-112 Regression Analysis Techniques for Assessing Calibration Reproducibility Within and Between Instruments. A Practical Example with Blood Ca²⁺ Assayed with Two cobas® b 221Analyzers

Abstract Background Reproducibility of the calibration curve within the analytical measurement range (AMR) is crucial for ensuring reliable patient values within and between laboratory instruments to detect shifts and trends of clinical significance. In this practical example for ionized calcium (Ca²⁺) assayed with two cobas b221® instruments, the equality of calibration lines and the verification with patient specimen comparison study was assessed with regression analysis techniques. Methods Instruments: Two cobas b 221 (Roche Diagnostics). Calibration material: COOX/MSS™ (Five levels, Roche Diagnostics). This was independently assayed either three or five times in parallel with both instruments. The patient specimens were obtained by venipuncture in Na/Li-heparin BD™ tubes (Becton-Dickinson). Upon receipt, specimens were assayed with both instruments within two minutes. The data were transferred to Minitab® (Version 21, Minitab Inc) statistical software and analyzed with polynomial ordinary least squares (POLS), orthogonal and locally weighted scatterplot smoother (lowess) regression analysis models. The aptness of the model was assessed with standardized residuals (std. res.) diagnostics. For acceptable total error the CLIA’s criterion target value ± 10% was adopted. Results While the POLS model did not show probabilistically significant differences between instruments (P=0.8), it showed probabilistically significant differences between years (P=0.003). However, this was due to a small MSE (= 0.003 mg/dL), which is not clinically significant. The test for linearity showed probabilistically significant lack-of-fit (P<0.0001) due to a small MS pure error (<0.0001 mg/dL). The lowess for the standardized deleted residuals by the fitted value showed a quasi-linear relationship. The multiple comparisons (P=0.3) and the Levene’s (P=0.4) tests did not detect probabilistically significant heteroscedasticity of the variance by reference value. The plot of the std. res. by the leverage showed two possible outliers (std. res.>|3|) and no influential observations (Hi<0.5). These diagnostics proved the aptness of the POLS model. The harmonization between the two instruments was verified with patient specimens. The orthogonal regression model showed in the interval 3-6 mg/dL a quasi-linear relationship between the values as obtained with the two instruments. The orthogonal regression line (y=0.1+1.02 n) and the OLS regression line (y=0.03+601 n) were very similar. There was no probabilistically significant lack-of-fit (P=0.5). The plot of the std. res. by the leverage showed one possible outlier (std.res.>|3|) and no influential observations. Additionally, the plot of the relative differences between the values as obtained with b221#9371 and b221#24796 by the values as obtained with b221#9371 showed only one value exceeding half of the total allowable error. Conclusions The POLS model clearly showed that the calibration lines for the two instruments were quasi-linear in the interval 2-10 mg/dL and reproducible within and between instruments. Additionally, this reliable performance was verified with patient specimens in the interval 3-6 mg/dL. The differences between values as obtained with each instrument were within half of the CLIA’s total allowable error. This clearly showed that equality of calibration lines ensured interchangeable patient values between instruments. Finally, electronic transfer of data and the availability of statistical software like Minitab was crucial for conducting this study.

A-331 Monitoring Operators Proficiency at Point of Care is Crucial for Reliable Patient Values. A Practical Example with Patient Blood Chloride, Potassium and Sodium Measurands Assayed with i-STAT®

Abstract Background While i-STAT® cartridges offer the Physician a spectrum of analytical methods at the point of care for prompt diagnosis and interventions, these methods have to be harmonized with the laboratory methods in order to reliably detect shifts and trends. With this practical example we illustrate the importance of a program of routine comparisons between the i-STAT® and laboratory methods to detect analytical differences exceeding the CLIA’s criterion and potentially having adverse clinical implications. Methods Patient blood chloride (Cl), potassium (K) and sodium (Na) values as obtained by nurses with i-STAT Chem 8 ™ cartridges (Abbott Laboratories) at the point of care, where daily compared with the values as obtained with the Laboratory method (two cobas 6000®, Roche Diagnostics) using green top BD® lithium heparin tubes (Becton Dickinson) collected within thirty minutes of the i-STAT assay. The data were electronically stored in RALS™ (RALS Informatics) and transferred to Minitab® (Version 21, Minitab Inc.) statistical software. The two analytical methods were compared using the orthogonal and the polynomial ordinary least squares (POLS) regression models and their graphic representations. The aptness of the methods was evaluated with the standardized residuals diagnostics for normality, independence, outliers (Standardized residual >|3|) and influential observations (Hi>0.5). For acceptance of the differences the CLIA’s total error criterion was employed. (CI: target value ± 5%; K: target value ± 0.5 mmol/L; Na: target value ± 4 mmol/L). Results In the first four months of the study the number of differences exceeding the CLIA’s criterion was not acceptable. [Cl: 10% (165/1564); K: 7% (109/1593); Na: 4% (66/1586)]. These differences were occurring throughout the analytical range (AMR). Consequently, the operator’s technique was suspected and the POCT senior technologists (CA, FA) adopted operator specific interventions. This strategy was effective in the following month and the improvements were maintained for seven consecutive months. Monthly differences exceeding the CLIA’s criterion decreased significantly [Cl:1.9% (52/2777): K:0.9% (25/2868): Na:0.5% (15/2896)]. This was clearly illustrated with the dot plot by date and the parallel box plots by month. Regression analysis showed that the orthogonal and the OLS models were equivalent. Cl: Orthogonal y=1.7+0.99x, OLS y=12+0.88x. K; Orthogonal y=0.2+1.2x, OLS y=0.1+0.96x. Na: orthogonal y=3+0.98x, OLS y=14+0.9x. Additionally, the POLS model showed a linear relationship within the AMR intervals with few outliers (Cl=11; K=7; Na=9), no influential observations (Hi<0.5) and equality of monthly regression lines (P>0.05). Conclusions The implementation of daily comparisons between patient values as obtained with the i-STAT method and those as obtained by the Laboratory method in the interval of thirty minutes, allowed the identification of individual operators requiring additional training. This strategy ensured reliable performance for eight consecutive months. This study clearly showed that the addition of continuous supervision and training of operators to quality control and quality assurance practices, ensured the interchangeability of patient values as obtained with i-STAT and those as obtained with the Laboratory method. Electronic collection of data and analysis with appropriate statistical software, such as Minitab, was crucial for the implementation of this program.

A-145 Are EDTA and Citrate Collection Devices Interchangeable for Estimating Blood Platelets Values using Sysmex XN™Analyzers ?

Abstract Background In the Sentara Healthcare System patient blood platelets are estimated using Sysmex XN™ analyzers. The blood specimens are collected in devices with EDTA anticoagulant. In the Emergency Departments, upon the first assessment of a patient a number of collection devices, including one with Na citrate anticoagulant, are obtained in case repeated testing is required. To prevent the collection of an additional EDTA device we investigated whether devices with Na citrate anticoagulant could be used interchangeably with EDTA devices for platelets values estimation. Methods Instruments: Sixteen Sysmex XN analyzers (Siemens) located in several institutions. Specimens: Seven-hundred nighty-eight patient blood specimens were obtained with both EDTA and citrate BD™ collection devices. The specimens, platelets numbers between 5000 and 800,000 platelets/mcL, were assayed in parallel within two minutes, after receipt in the laboratory, and then at intervals to reach 20 hours. The data were transferred electronically to Minitab® (version 21, Minitab Inc) statistical software. The paired data were analyzed with orthogonal, ordinary least squares (OLS), weighted polynomial ordinary least squares (WPOLS) and locally weighted scatterplot smoother (lowess) regression models and their graphic representations. The CLIA’s total error criterion (target value ± 25%) was used for evaluating acceptable variability. Results The parallel box plot of the differences between specimens as obtained with EDTA and those as obtained with Na citrate, showed that the distribution of the differences was similar between instruments and within the allowable total error of ± 25%. The orthogonal and the OLS models showed similar regression lines (Orthogonal: Y=3.3+0.94X: OLS: Y=-1+0.93X) and increase in variance for increasing platelets values. Consequently, a WPOLS model was employed for additional analyses. The ANOVA table of the WPOLS showed that there were no probabilistically significant differences between regression lines for either platelet’s values (P = 0.5) or specimen age (P = 0.1). Tridimensional surface plots clearly illustrated that the differences were distributed within -12.5% and the zero lines. The aptness of the WPOLS model was demonstrated by the standardized residual (std. res.) analysis showing quasi-normal distribution, with a few outliers (std. res. > |3|) and two influential observations for (Hi > 0.5) which were for platelets values less than 10,000 platelets/mcL. Conclusions The differences between EDTA and citrated specimens were within zero and -12.5%, this represented half of the CLIA’s criterion. The weighted polynomial regression model was employed due to the heteroscedasticity of the variance, The simultaneous evaluation by platelets values and the age of the specimen did not detect probabilistically significant differences by either platelets values or age of the specimen. This limited study suggested that EDTA and citrate anticoagulated specimens may be used interchangeably for patient care for specimens with platelets values between 10,000 and 800,000/mcL and within twenty hours from collection. However, the limited number of patients precluded to evaluate whether the detected outliers represented group(s) of patients with a particular medical condition and or pharmacotherapeutic treatment. Finally, the electronic collection and transfer of specimens to statistical software, such as Minitab, was crucial for performing this study.

Empirical evaluation of the gamma-Pareto regression residuals for influence diagnostics

ABSTRACT For the reliable results of regression analysis, influence diagnostic methods play an important role. The performance of various proposed residuals for the gamma-Pareto regression model (G-PRM) is presented in this study. These proposed residuals are partitioned into standardized and adjusted residual forms. For the detection of influential observations, difference of fits (DFFITS) was used in both standardized and adjusted residuals. To detect influential points, these residuals are compared through Monte Carlo Simulation also real data (Ardennes data) are analysed to show the benefits of the proposed methods. The likelihood residuals performed better for small dispersion than others. Additionally, all forms of adjusted residuals are the same, not much more efficient than the standardized form. All standardized residuals behave in the same way for wider dispersion, and they are superior to the likelihood residuals for the detection of influential points.

Characteristics of moxibustion in Dragon Gate Stone Inscription prescriptions

The Dragon Gate Stone Inscription prescriptions are the earliest surviving stone inscriptions of medical formulas in China, covering various departments such as internal medicine, surgery, gynecology, pediatrics and ophthalmology. By reviewing 28 moxibustion prescriptions recorded in the Dragon Gate Stone Inscriptions, the following application characteristics are summarized: moxibustion mainly treats acute diseases such as mania, deficiency-cold syndrome, and jaundice; in terms of point selection, specific points such as the thirteen ghost points, eight influential points, and front-mu points are used, emphasizing the extraordinary meridians such as governor vessel and conception vessel, as well as specific single acupoints with unique therapeutic effects; in clinical application, it follows the principle of treating according to syndrome differentiation, uses multiple acupoints simultaneously, employs food-medicine homology, and adjusts the moxibustion dosage according to individual conditions. The Dragon Gate Stone Inscription prescriptions reflect that the application of moxibustion therapy during the Northern Wei to Tang Dynasty period had already reached a relatively mature level, indicating a high level of proficiency in moxibustion techniques during that time.

Identifying Influential Observations in Multiple Regression

Identifying Influential Observations in Multiple Regression

Local Influence for the Thin-Plate Spline Generalized Linear Model

Thin-Plate Spline Generalized Linear Models (TPS-GLMs) are an extension of Semiparametric Generalized Linear Models (SGLMs), because they allow a smoothing spline to be extended to two or more dimensions. This class of models allows modeling a set of data in which it is desired to incorporate the non-linear joint effects of some covariates to explain the variability of a certain variable of interest. In the spatial context, these models are quite useful, since they allow the effects of locations to be included, both in trend and dispersion, using a smooth surface. In this work, we extend the local influence technique for the TPS-GLM model in order to evaluate the sensitivity of the maximum penalized likelihood estimators against small perturbations in the model and data. We fit our model through a joint iterative process based on Fisher Scoring and weighted backfitting algorithms. In addition, we obtained the normal curvature for the case-weight perturbation and response variable additive perturbation schemes, in order to detect influential observations on the model fit. Finally, two data sets from different areas (agronomy and environment) were used to illustrate the methodology proposed here.

A Student-Centered Exploration of Influential Points in Linear Regression Using Desmos

Linear regression, an introductory statistics topic, typically involves finding and evaluating the line of best fit as well as making predictions based on it. However, the subtopic of influential points is often neglected. We present and report on findings of a series of four interactive student-centered activities in Desmos that facilitate student discovery of potential influential points and determine the conditions that would result in them having a large effect on the position and strength of a linear relationship. Placing these activities in the Desmos environment enables students to test their conjectures about leverage points and points with large residuals as they receive immediate feedback that promotes the development of understanding of influential points. Supplementary materials for this article are available online.

Analysing Determinants of Institutional Delivery in NFHS-5: A Comprehensive Study in India with a Focus on Caste Dynamics

The benefit of institutional delivery not only impacts maternal health but also reduces newborn mortality and increases overall well-being. This study focused on differences in the determinants of delivering at institution settings in India, with specific importance given to mothers within different caste groups. This is one area that was given less importance in the existing literature, particularly in studies that focused on the determinants of institutional delivery in India. Using data from the National Family Health Survey 5 (NFHS-5), the research explores factors influencing the choice between institutional and non-institutional delivery. Using logistic regression, and after removing influential and large residual observations (outliers), the analysis reveals that sociocultural determinants, including caste, religion, economic status and maternal education, significantly impact delivery preferences. Mothers from Scheduled Castes and Other Caste categories exhibit a higher likelihood of opting for institutional delivery. Economic factors, partner support, family size, maternal education, health-seeking behaviours, age, perceived distance to health facilities and media engagement also play crucial roles. Comparative analyses across caste groups emphasize the influence of wealth and education on institutional delivery choices. The findings underscore the need for having targeted interventions addressing diverse sociocultural contexts to improve maternal healthcare outcomes and reduce health disparities in India. Efforts should focus on awareness campaigns and overcoming challenges to promote the benefits of institutional delivery.

Particle-size correlation and prediction models of atmospheric heavy metals

The infinitely variable size of atmospheric particulate matter raises a huge challenge for accurately assessing the health risks of pollutants of different particle sizes. Currently, the majority of studies have concentrated on PM2.5-bound heavy metals (PM2.5-HMs) and their associated health risks, greatly ignoring the risks caused by heavy metals in other particle sizes such as PM10 and total suspended particulate (TSP). In this study, Pearson correlation analysis was used for the first time to explore the concentration correlation of 10 heavy metals (HMs) (Cd, Cr, As, Pb, Zn, Cu, V, Mn, Ni, Co) in PM1, PM2.5, PM10, and TSP, utilizing 91876 PM samples from China from 2000 to 2022. The results of the Pearson correlation analysis revealed that the correlation coefficients between PM2.5-HM and PM10-HM, PM10-HM and TSP-HM, and PM2.5-HM and TSP-HM were all above 0.82, with statistical significance (P < 0.001). Due to the sample size limitation of PM1-HM (n < 15), the Pearson correlation coefficients of PM1-HM with other particle-size HM may not be robust enough. Then generalized linear models (GLM) and generalized additive models (GAM) were used to exclude the interference of covariates (sampling time and sampling city), and 16 univariate inter-prediction models which are not affected by temporal and spatial factors for PM2.5, PM10, and TSP binding HM concentration were further constructed. The regression diagnostics of these prediction models indicate normally distributed data and strong linear trends, and the influential points in the modeled data were all within acceptable limits (n ≤ 1). The R2s of the prediction models were almost greater than 0.8, indicating that the prediction models were effective and robust. This study reveals for the first time the particle-size correlation of PM2.5-HM, PM10-HM, and TSP-HM, offering a novel approach for the prediction of heavy metal concentration and health risk assessment of atmospheric particle-size fractionated heavy metal.

Olfactory tract/bulb metal concentration in Manganese-exposed mineworkers

BackgroundManganese (Mn) is an essential micronutrient as well as a well-established neurotoxicant. Occupational and environmental exposures may bypass homeostatic regulation and lead to increased systemic Mn levels. Translocation of ultrafine ambient airborne particles via nasal neuronal pathway to olfactory bulb and tract may be an important pathway by which Mn enters the central nervous system. ObjectiveTo measure olfactory tract/bulb tissue metal concentrations in Mn-exposed and non-exposed mineworkers. MethodsUsing inductively coupled plasma-mass spectrometry (ICP-MS), we measured and compared tissue metal concentrations in unilateral olfactory tracts/bulbs of 24 Mn-exposed and 17 non-exposed South African mineworkers. We used linear regression to investigate the association between cumulative Mn exposures and olfactory tract/bulb Mn concentration. ResultsThe difference in mean olfactory tract/bulb Mn concentrations between Mn-exposed and non-Mn exposed mineworkers was 0.16 µg/g (95% CI −0.11, 0.42); but decreased to 0.09 µg/g (95% CI 0.004, 0.18) after exclusion of one influential observation. Olfactory tract/bulb metal concentration and cumulative Mn exposure suggested there may be a positive association; for each mg Mn/m3-year there was a 0.05 µg/g (95% CI 0.01, 0.08) greater olfactory tract/bulb Mn concentration overall, but −0.003 (95% CI −0.02, 0.02) when excluding the three influential observations. Recency of Mn exposure was not associated with olfactory tract/bulb Mn concentration. ConclusionsOur findings suggest that Mn-exposed mineworkers might have higher olfactory tract/bulb tissue Mn concentrations than non-Mn exposed mineworkers, and that concentrations might depend more on cumulative dose than recency of exposure.

Changing prices after the reform of local public services: remunicipalization versus privatization

AbstractPrivatization and remunicipalization have been used as alternative options to reform the delivery of local public services; in both cases, mainly because of disappointment with the service performance, although ideological preferences might also play a role. The drivers and effects of water privatization have been widely studied, whereas little empirical evidence is available for remunicipalization, particularly regarding its effects. Using a sample of Spanish municipalities, this paper assesses the change in the price of urban water following remunicipalization as compared to privatization. The main finding is that remunicipalization leads to smaller increases in price; this outcome is, however, due to a few atypical municipalities with abnormally low prices before the policy reform. Once these influential observations are controlled for, whether the reform consists of remunicipalization or privatization makes no difference regarding price changes. It is also found that remunicipalization is much more likely in cities governed by extreme left-wing parties.

Is Fisher inference inferior to Neyman inference for policy analysis?

The increasing computational power has led to an increasing interest in Fisher’s test in social science. As the Fisher and Neyman inference are based on different principles there is also an increasing interest in understanding the differential features of the two procedures. For example, Young (2018) found that the Fisher test has better size properties than the Neyman test in the situation with influential observations. Ding (2017), on the other hand, showed that the asymptotic variance of the mean-difference estimator (MDE) under Fisher inference is larger than that under Neyman inference, and that the asymptotic Fisher test is less powerful than the t-test even for the simplest case of homogeneous effect. Since MDE plays an important role for policy evaluation, these latter results are a concern for using Fisher’s test as argued in Young (2018). With the aim of providing an understanding of the usefulness of the exact Fisher test for inference to the sample and to the population, this paper clarifies the results in Ding (2017). Using a novel Monte Carlo simulation following the same data generating processes as in Ding (2017), we demonstrate that the Fisher test has no worse power properties than the t-test even with heterogeneous effects.

Robust Hausman Pretest for Panel Data Model in the Presence of Heteroscedasticity and Influential Observations

The classical Hausman pretest (HT) is used to specified the right model between random and fixed effect panel data models. However, in the presence of heteroscedastic error variances and influential observations (IOs) in the data set, it may not correctly identify the right model. Therefore, this motivated us to proposed a new method termed Robust Hausman Test (RHTFIID) which employed residuals from weighted least square (WLS) instead of OLS in the construction of heteroscedasticity consistent covariance matrix (HCCM) estimator. The weighting method is based on an efficient High Leverage Points (HLPs) detection method called Fast Improvised Influential Distance (FIID) which down weight only vertical outliers and bad HLPs. The good HLPs were allowed in the estimation as they might contribute to the precision of the estimate. The result indicates that the new proposed RHTFIID outperformed the existing classical Hausman pretest by identifying the right model with and without heteroscedasticity and influential observations.

Identification of Influential Observations for High-Dimensional Regression

Identification of Influential Observations for High-Dimensional Regression

Demographic-environmental effect on dengue outbreaks in 11 countries.

Dengue outbreaks are common in tropical or temperate countries, and climate change can exacerbate the problem by creating conditions conducive to the spread of mosquitoes and prolonging the transmission season. Warmer temperatures can allow mosquitoes to mature faster and increase their ability to spread disease. Additionally, changes in rainfall patterns can create more standing water, providing a breeding ground for mosquitoes. The objective of this study is to investigate the correlation between environmental and demographic factors and the dissemination of dengue fever. The study will use yearly data from 2000 to 2021 from 11 countries highly affected by dengue, considering multiple factors such as dengue cases, temperatures, precipitation, and population to better understand the impact of these variables on dengue transmission. In this research, Poisson regression (PR) and negative binomial regression (NBR) models are used to model count data and estimate the effect of different predictor variables on the outcome. Also, histogram plots and pairwise correlation plots are used to provide an initial overview of the distribution and relationship between the variables. Moreover, Goodness-of-fit tests, t-test analysis, diagnostic plots, influence plots, and residual vs. leverage plots are used to check the assumptions and validity of the models and identify any outliers or influential observations that may be affecting the results. The findings indicate that mean temperature and log(Urban) had a positive impact on dengue infection rates, while maximum temperature, log(Precipitation), and population density had a negative impact. However, minimum temperature, log(Rural), and log(Total population) did not demonstrate any significant effects on the incidence of dengue. The impact of demographic-environmental factors on dengue outbreaks in 11 Asian countries is illuminated by this study. The results highlight the significance of mean temperature (Tmean), maximum temperature (Tmax), log(Urban), log(Precipitation), and population density in influencing dengue incidence rates. However, further research is needed to gain a better understanding of the role of additional variables, such as immunity levels, awareness, and vector control measures, in the spread of dengue.