Regression Parameters Research Articles

Using the Bayesian method to estimate and comparison the regression of the exponential and gamma survival (Simulation)

Parametric regression models It is one of the oldest and most common regression models and can be defined as one of the statistical methods that is used to describe and estimate the relationship between a dependent random variable and explanatory random variables. These models have types, including quantitative models, of their linear and non-linear types, and qualitative models that The dependent variable is a binary response variable. These models are characterized by the fact that all cases that are studied are assumed to be normally distributed and measurable, and that the regression function determines the parameters that cannot be changed except by changing the number of variables included in the study, relying on probability distributions, which are the exponential distribution and the Gamma, we will obtain parametric regression models, which are the exponential regression model, and the gamma regression model, relying on the Cox regression model to be used in forming these models, which are models that require the presence of information and hypotheses about the distribution of the population used in the test that is appropriate for the units or data, as well as the variables that enter. The test or experiment on which the survival function is based, in this thesis, it was proposed to estimate the parameters of these models using the Bayesian method. The simulation method was used to generate data that follows parametric survival regression models according to various factors. The simulation results showed that the third model of the exponential probability distribution of the survival function shows the lowest value according to the MSE criterion. This indicates that Increasing the sample size typically reduces the standard mean error (MSE), which is used as an indicator of the differences between expected and actual values in statistical estimates. Simply put, the larger the sample size, the more accurate the statistical predictions are and the lower the standard deviation from the actual values, indicating that larger samples often contribute to improving the accuracy of statistical estimates.

Modeling and forecasting carbon dioxide emission in Pakistan using a hybrid combination of regression and time series models

Carbon dioxide (CO2) emissions continue to rise globally despite efforts to combat climate change. Energy industry emissions are a pressing global issue, causing devastating impacts. Hence, it is vital to accurately and efficiently forecast CO2 emissions. Thus, this study comprehensively analyzes forecasting CO2 emissions by comparing various hybrid combinations of regression and time series methods to explore the CO2 emissions in Pakistan. First, divide the yearly time series of CO2 emissions into the long-run curve trend series and the residual subseries. The long-run curve trend subseries is modeled using parametric and nonparametric regression methods, while various standard time series models are used to forecast the residual subseries. However, the forecasts of each subseries will be combined to obtain the final forecast of CO2 emissions. This work used four different accuracy mean errors, a statistical test, and a graphical analysis as performance measures to evaluate the proposed hybrid forecasting technique. The findings confirmed that the proposed hybrid combination forecasting technique is highly accurate and efficient in forecasting CO2 emissions. Likewise, according to the proposed final optimal hybrid combination forecasting model, Pakistan's per capita CO2 emissions will be 1.130215 metric tons in 2030. Pakistan's escalating emission trend signals that creative solutions must be implemented to curb it. Thus, the government must price carbon footprints, regulate electricity from zero-carbon sources, reduce population, encourage afforestation in densely populated areas, adopt clean technology, and fund research.

Using neural biomarkers to personalize dosing of vagus nerve stimulation

BackgroundVagus nerve stimulation (VNS) is an established therapy for treating a variety of chronic diseases, such as epilepsy, depression, obesity, and for stroke rehabilitation. However, lack of precision and side-effects have hindered its efficacy and extension to new conditions. Achieving a better understanding of the relationship between VNS parameters and neural and physiological responses is therefore necessary to enable the design of personalized dosing procedures and improve precision and efficacy of VNS therapies.MethodsWe used biomarkers from recorded evoked fiber activity and short-term physiological responses (throat muscle, cardiac and respiratory activity) to understand the response to a wide range of VNS parameters in anaesthetised pigs. Using signal processing, Gaussian processes (GP) and parametric regression models we analyse the relationship between VNS parameters and neural and physiological responses.ResultsFirstly, we illustrate how considering multiple stimulation parameters in VNS dosing can improve the efficacy and precision of VNS therapies. Secondly, we describe the relationship between different VNS parameters and the evoked fiber activity and show how spatially selective electrodes can be used to improve fiber recruitment. Thirdly, we provide a detailed exploration of the relationship between the activations of neural fiber types and different physiological effects. Finally, based on these results, we discuss how recordings of evoked fiber activity can help design VNS dosing procedures that optimize short-term physiological effects safely and efficiently.ConclusionUnderstanding of evoked fiber activity during VNS provide powerful biomarkers that could improve the precision, safety and efficacy of VNS therapies.

Correlation adjusted debiased Lasso: debiasing the Lasso with inaccurate covariate model

Abstract We consider the problem of estimating a low-dimensional parameter in high-dimensional linear regression. Constructing an approximately unbiased estimate of the parameter of interest is a crucial step towards performing statistical inference. Several authors suggest to orthogonalize both the variable of interest and the outcome with respect to the nuisance variables, and then regress the residual outcome with respect to the residual variable. This is possible if the covariance structure of the regressors is perfectly known, or is sufficiently structured that it can be estimated accurately from data (e.g. the precision matrix is sufficiently sparse). Here we consider a regime in which the covariate model can only be estimated inaccurately, and hence existing debiasing approaches are not guaranteed to work. We propose the correlation adjusted debiased Lasso, which nearly eliminates this bias in some cases, including cases in which the estimation errors are neither negligible nor orthogonal.

Bayesian Parametric Survival Analysis of Patients With Non-Hodgkin’s Lymphoma in Yazd, Iran, During 2009-2019

Background and aims: Non-Hodgkin’s lymphoma (NHL) is one of two main types of lymphoma. This present study aimed to investigate the survival rate in patients with NHL. Methods: This analytical survival-type study examined 372 patients with NHL referred to Shahid Sadoughi Hospital in Yazd in 2009-2019. The data were extracted from patients’ files. STATA/17 software, the Kaplan-Meier method, and Bayesian parametric survival analysis were used for data analysis. Results: The overall survival rate and the mean overall survival time were 65% and 129±17.94 months, respectively. According to the Kaplan-Meier method, the survival rates of 1, 2, 3, 5, and 10 years were 86%, 80%, 76%, 69.5%, and 52%. The results revealed that men, people over 70 years old, patients with the NHL.T-cell subtype, patients with involvement in the chest, inguinal, and abdominal-pelvic regions, and patients in stage III or IV of the disease had a shorter survival time. In addition, survival time was shorter in patients with disease recurrence at the primary site. The variables of gender (hazard ratio [HR]=0.65; 95% confidence interval [CI]: 0.44‒0.93), type of treatment (HR=3.43; 95% CI: 1.58‒6.16), stage of disease (HR=2.87; 95% CI: 2.09‒3.57), and involvement site (HR=1.45:95%; CI: 1.03‒1.99) had a significant effect on survival time using the Bayesian parametric regression model. Conclusion: If patients are identified at an early stage, they have the best chance of survival. In chronic lymphocytic leukemia (CLL) patients, the best survival is achieved without treatment. At the same time, in other types of cancer, chemotherapy offers the best survival.

Collaborative learning from distributed data with differentially private synthetic data

BackgroundConsider a setting where multiple parties holding sensitive data aim to collaboratively learn population level statistics, but pooling the sensitive data sets is not possible due to privacy concerns and parties are unable to engage in centrally coordinated joint computation. We study the feasibility of combining privacy preserving synthetic data sets in place of the original data for collaborative learning on real-world health data from the UK Biobank.MethodsWe perform an empirical evaluation based on an existing prospective cohort study from the literature. Multiple parties were simulated by splitting the UK Biobank cohort along assessment centers, for which we generate synthetic data using differentially private generative modelling techniques. We then apply the original study’s Poisson regression analysis on the combined synthetic data sets and evaluate the effects of 1) the size of local data set, 2) the number of participating parties, and 3) local shifts in distributions, on the obtained likelihood scores.ResultsWe discover that parties engaging in the collaborative learning via shared synthetic data obtain more accurate estimates of the regression parameters compared to using only their local data. This finding extends to the difficult case of small heterogeneous data sets. Furthermore, the more parties participate, the larger and more consistent the improvements become up to a certain limit. Finally, we find that data sharing can especially help parties whose data contain underrepresented groups to perform better-adjusted analysis for said groups.ConclusionsBased on our results we conclude that sharing of synthetic data is a viable method for enabling learning from sensitive data without violating privacy constraints even if individual data sets are small or do not represent the overall population well. Lack of access to distributed sensitive data is often a bottleneck in biomedical research, which our study shows can be alleviated with privacy-preserving collaborative learning methods.

Analysis of risk factors and clinical implications for diabetes in first-degree relatives in the northeastern region of China.

The prevalence of diabetes has risen fast with a considerable weighted prevalence of undiagnosed diabetes or uncontrolled diabetes. Then it becomes more necessary to timely screen out and monitor high-risk populations who are likely to be ignored during the COVID-19 pandemic. To classify and find the common risks of undiagnosed diabetes and uncontrolled diabetes, it's beneficial to put specific risk control measures into effect for comprehensive primary care. Especially, there is a need for accurate yet accessible prediction models. Based on a cross-sectional study and secondary analysis on the health examination held in Changchun City (2016), we aimed to evaluate the factors associated with hyperglycemia, analyze the management status of T2DM, and determine the best cutoff value of incidence of diabetes in the first-degree relatives to suggest the necessity of early diagnosis of diabetes after first screening. A total of 5658 volunteers were analyzed. Prevalence of T2DM and impaired fasting glucose were 8.4% (n=477) and 11.5% (n=648), respectively. There were 925 participants (16.3%) with a family history of T2DM in their first-degree relatives. Multivariable analysis demonstrated that family history was associated with hyperglycemia. Among the 477 patients with T2DM, 40.9% had not been previously diagnosed. The predictive equation was calculated with the following logistic regression parameters with 0.71 (95% CI: 0.67-0.76) of the area under the ROC curve, 64.0% of sensitivity and 29% of specificity (P < 0.001): P = \frac{1}{1 + e^{-z}}, where z = -3.08 + [0.89 (Family history-group) + 0.69 (age-group)+ 0.25 (BMI-group)]. Positive family history was associated with the diagnosis of T2DM, but not glucose level in the diagnosed patients. The best cutoff value of incidence of diabetes in the first-degree relatives was 9.55% (P < 0.001). Family history of diabetes was independently associated with glucose dysfunction. Classification by the first-degree relatives with diabetes is prominent for targeting high-risk population. Meanwhile, positive family history of diabetes was associated with diabetes being diagnosed rather than the glycemic control in patients who had been diagnosed. It's necessary to emphasize the linkage between early diagnosis and positive family history for high proportions of undiagnosed T2DM.

A Method for Roof Wireframe Reconstruction Based on Self-Supervised Pretraining

Abstract. In this paper, we present a two-stage method for roof wireframe reconstruction employing a self-supervised pretraining technique. The initial stage utilizes a multi-scale mask autoencoder to generate point-wise features. The subsequent stage involves three steps for edge parameter regression. Firstly, the initial edge directions are generated under the guidance of edge point identification. The next step employs edge parameter regression and matching modules to extract the parameters (namely, direction vector and length) of edge representation from the obtained edge features. Finally, a specifically designed edge non-maximum suppression and an edge similarity loss function are employed to optimize the representation of the final wireframe models and eliminate redundant edges. Experimental results indicate that the pre-trained self-supervised model, enriched by the roof wireframe reconstruction task, demonstrates superior performance on both the publicly available Building3D dataset and its post-processed iterations, specifically the Dense dataset, outperforming even traditional methods.

Low salinity water flooding: estimating relative permeability and capillary pressure using coupling of particle swarm optimization and machine learning technique

The reservoir’s properties are required for proper reservoir simulation, which also involves uncertainties. Experimental methods to estimate the relative permeability and capillary pressure data are expensive and time-consuming. This study aims to determine the relative permeability and capillary pressure functions of a sandstone core in the presence and absence of clay during low-salinity water floods. The data were provided by automatic history matching the results from previously lab-reported studies through coupling a simulator with the particle swarm optimization algorithm. Correlations were proposed using multiple-linear regression for relative permeability and capillary pressure parameters at low-salinity conditions. They were validated against experimental results of no clay and clayey formation with regression of 95% and 97%. To assign one curve of relative permeability and capillary pressure to the grid cells of the simulator, averaging techniques were implemented. The effect of salinity and clay content on the obtained curves was investigated. Changing salinity from 42000 to 4000 ppm, the reduction in water relative permeability appeared to be higher than the oil relative permeability increment. Moreover, a noticeable shift in the relative permeability curves toward the highest saturations related to the clay content was observed. The proposed hybrid method could be a suitable tool to estimate the relative permeability and capillary pressure functions of the water-based EOR methods.

African forex markets: Modeling their predictability and the asymmetric effects of oil and geopolitical risk

This study investigates the African forex (FX), geopolitical risk (GPR) and oil associations, resorting to the quantile-based causality, parametric quantile regression (QR), and non-parametric quantile-on-quantile regression (QQR) methodologies. We explore 16 major African currency markets. The sample contains daily data and spans from July 2000 through August 2023. Our results zoom on the differences in the FX-GPR-oil interactions at different market conditions of the dependent and independent variables, proxied for each case by 19 quantiles from 0.05 to 0.95. We find that the African FX returns are significantly asymmetrically predicted by both WTI oil and GPR. Our tail-based causality analysis uncovers the directional causal flow from GPR and Oil (WTI) to foreign exchange rates. We also report on salient asymmetries in the responsiveness of country-specific African FX returns to oil and geopolitics-driven shocks. In addition, the results of our QR analysis show a mix of positive and negative GPR and Oil effects, characterized by varying degrees of significance for different currencies. Our QQR analysis also reveals that GPR and WTI oil exert asymmetric effects on African FX returns. The returns of several African forex markets demonstrate negative relationships with GPR, especially at the lower tails of GPR and/or WTI oil, as usually the extreme-tailed conditions are driven by diverse kinds of crises, well covered by the analyzed dataset. Our outcomes cater relevant knowledge for scholars, FX investors, traders and regulatory bodies.

Influence of Communication of Team Leadership on Team Effectiveness of Collaborative Partnerships in International Research Organizations in Kenya

The objective of the study was to evaluate the influence of communication by team leadership on the effectiveness of collaborative partnerships in international research organizations in Kenya. The study was grounded on positivist research philosophy. The study adopted the correlational research design. The anticipated respondents from the 12 international research organizations included 164 program leaders, project managers and heads of departments. A census was conducted. Structured questionnaires were used to collect the data. SPSS version 25 was used to organize, code, and analyze information and generate the quantitative report. The data was analyzed using descriptive and inferential statistics. The correlation results showed that communication by team leadership is positively and significantly related to team effectiveness. The regression (R2) showed that 50.1% of the variations of team effectiveness of collaborative partnerships in international research organizations in Kenya were explained by the communication by team leadership R2 = .501. The regression parameter estimates showed that communication by team leadership is positively and significantly related to team effectiveness (β =.743, t (151) = 12.269, p&lt;.05). Thus, communication of team leadership significantly influences the team effectiveness of collaborative partnerships in international research organizations in Kenya. The study recommends effective communication through team leadership is crucial for international research organizations in Kenya to enhance their effectiveness, achieve success, and address various factors influencing collaborative partnerships.

Herding behaviour surrounding the Russo–Ukraine war and COVID-19 pandemic: evidence from energy, metal, livestock and grain commodities

PurposeThis study examines herding behaviour in commodity markets amid two major global upheavals: the Russo–Ukraine conflict and the COVID-19 pandemic.Design/methodology/approachBy analysing 18 commodity futures worldwide, the study examines herding trends in metals, livestock, energy and grains sectors. The applied methodology combines static and dynamic approaches by incorporating cross-sectional absolute deviations (CSAD) and a time-varying parameter (TVP) regression model extended by Markov Chain Monte Carlo (MCMC) sampling to adequately reflect the complexity of herding behaviour in different market scenarios.FindingsOur results show clear differences in herd behaviour during these crises. The Russia–Ukraine war led to relatively subdued herding behaviour in commodities, suggesting a limited impact of geopolitical turmoil on collective market behaviour. In stark contrast, the outbreak of the COVID-19 pandemic significantly amplified herding behaviour, particularly in the energy and livestock sectors.Originality/valueThis discrepancy emphasises the different impact of a health crisis versus a geopolitical conflict on market dynamics. This study makes an important contribution to the existing literature as it is one of the first studies to contrast herding behaviour in commodity markets during these two crises. Our results show that not all crises produce comparable market reactions, which underlines the importance of the crisis context when analysing financial market behaviour.

Evaluating methods for logging the pore structure of tight sandstone reservoir in Chang 6 member of the Yanchang Formation, Ordos Basin

Pore structure is a key parameter used to evaluate reservoir quality. At present, experimental method is the most important method to analyze reservoir pore structure. However, coring data may be limited, and it is not possible to perform experimental analyses on all cores. Therefore, the researchers explored the use of logging techniques to study the pore structure of reservoirs. The relationship between pore geometry index (PGI), pore permeability and mercury injection parameters was analyzed based on mercury injection experiment, thin slice analysis, production test data and well logging data. These results can then determine the response characteristics of the logging parameters that correspond to different pore structures and establish a method of modeling the PGI through a multiple parameter regression and neural network method. This research shows that: (1) PGI can quantitatively characterize pore structure, and the maximum pore throat radius, displacement pressure and flow unit index have the highest correlation with PGI, which can be accurately characterized by practical formulas; (2) Natural gamma ray, natural potential amplitude difference, acoustic transit time, density, compensated neutron, deep and shallow resistivity logging data can reflect the quality of the reservoir pore structure. However, there are limitations in evaluating reservoir pore structure with a single logging parameter. Multi-parameter regression method and neural network method realize the quantitative calculation of pore structure from the perspective of multi-parameter and nonlinear. (3) The neural network method and multiple parameter regression method are used to study the pore structure of reservoir and realize the continuous quantitative calculation of pore structure index in a single well. It can be used in uncored analysis Wells and as one of the parameters to evaluate reservoir pore structure.

An boosting business intelligent to customer lifetime value with robust M-estimation

When a business concentrates too much on acquiring new clients rather than retaining old ones, mistakes are sometimes made. Each customer has a different value. Customer lifetime value (CLV) is a metric used to assess longterm customer value. Customer value is a key concern in any commercial endeavor. When there are variations in customer behavior, CLV forecasts the value of total customer income when the data distribution is not normal, and outliers are present. Robust M-estimation, a maximum likelihood type estimator, is used in this study to enhance CLV data. Through the minimization of the regression parameter from the residual value, robust Mestimation eliminates data outliers in customer metric data. With an accuracy of 94.15%, R-square is used to gauge model performance. This research shows that CLV optimization can be used as a marketing and sales strategy by companies.

Beta four parameter GLMM approach to evaluate paddy productivity

Abstract Indonesia is one of the top five counties in the world that produces rice. In average, Indonesia has a paddy productivity of 5.2 tons per hectare and continuously growing within 1.59% annual rate. Eventhough high, one of the main problems faced is that Indonesia is a vast archipelago country that has a diverse agro-climatic and socio-economic conditions which can significantly impact paddy productivity in an area. Given also that the paddy productivity in Indonesia has a certain minimum and maximum value, thus using a beta four parameter-based model is considered appropriate. We have proposed novel Beta Four Parameter GLMM that was an extension of the Beta Four Parameter regression model developed by Zou and Huang (2022). The model was applied in an empirical study based on farmers crop cutting survey in Central Kalimantan Indonesia. Results show that the proposed Beta Four Parameter GLMM has a better model fit. The model showed that pest attacks, pest handling, and resource allocation was considered the most influential variable affecting paddy productivity. Hence, it is suggested that the government must focus to construct a comprehensive strategy to improve paddy productivity by making it more resilient to pest and having more sufficient resource allocation management.

Bayesian fractional polynomial approach to quantile regression and variable selection with application in the analysis of blood pressure among US adults

Although the fractional polynomials (FPs) can act as a concise and accurate formula for examining smooth relationships between response and predictors, modelling conditional mean functions observes the partial view of a distribution of response variable, as distributions of many response variables such as blood pressure (BP) measures are typically skew. Conditional quantile functions with FPs provide a comprehensive relationship between the response variable and its predictors, such as median and extremely high-BP measures that may be often required in practical data analysis generally. To the best of our knowledge, this is new in the literature. Therefore, in this article, we develop and employ Bayesian variable selection with quantile-dependent prior for the FP model to propose a Bayesian variable selection with parametric non-linear quantile regression model. The objective is to examine a non-linear relationship between BP measures and their risk factors across median and upper quantile levels using data extracted from the 2007 to 2008 National Health and Nutrition Examination Survey (NHANES). The variable selection in the model analysis identified that the non-linear terms of continuous variables (body mass index, age), and categorical variables (ethnicity, gender, and marital status) were selected as important predictors in the model across all quantile levels.

Goodness-of-fit tests for modified Poisson regression possibly producing fitted values exceeding one in binary outcome analysis.

Modified Poisson regression, which estimates the regression parameters in the log-binomial regression model using the Poisson quasi-likelihood estimating equation and robust variance, is a useful tool for estimating the adjusted risk and prevalence ratio in binary outcome analysis. Although several goodness-of-fit tests have been developed for other binary regressions, few goodness-of-fit tests are available for modified Poisson regression. In this study, we proposed several goodness-of-fit tests for modified Poisson regression, including the modified Hosmer-Lemeshow test with empirical variance, Tsiatis test, normalized Pearson chi-square tests with binomial variance and Poisson variance, and normalized residual sum of squares test. The original Hosmer-Lemeshow test and normalized Pearson chi-square test with binomial variance are inappropriate for the modified Poisson regression, which can produce a fitted value exceeding 1 owing to the unconstrained parameter space. A simulation study revealed that the normalized residual sum of squares test performed well regarding the type I error probability and the power for a wrong link function. We applied the proposed goodness-of-fit tests to the analysis of cross-sectional data of patients with cancer. We recommend the normalized residual sum of squares test as a goodness-of-fit test in the modified Poisson regression.

External vs Internal Focus of Attention in Procedural Skills Learning: A Randomized Study.

The Optimizing Performance Through Intrinsic Motivation and Attention for Learning theory postulates that directing attention to the intended movement effect or outcome (external focus) is more effective than directing attention to the internal body motion or body part (internal focus). This study compared external vs internal focus of attention as novice students learned ultrasound-guided peripheral cannulation. The authors hypothesized that conditions promoting an external (vs internal) focus of attention would result in better performance on immediate and delayed (retention) testing. The authors conducted a randomized, experimental study from October 2022 to February 2023 comparing external and internal focus using ultrasound-guided peripheral venous cannulation followed by practice using an ultrasound training block. Undergraduate health science students performed 4 practice trials with instructions to focus on the needle tip (external focus) or their hands (internal focus), followed by an immediate assessment and a retention assessment 2 weeks later. The primary outcome was time to successful cannulation. Results were compared using Kaplan-Meier curves and parametric survival analysis regression (accelerated failure time) models. Seventy participants completed the immediate assessment. The external focus group completed successful cannulation a mean of 2.33 times faster (95% CI = 1.47, 3.67; P < .001) than those in the internal focus group (median time, 12.0 vs 29.5 seconds). Results also favored the external focus group during practice, with the external focus group successfully completing the task a mean of 1.83 times faster (95% CI = 1.04, 3.21; P = .04) than the internal focus group. In the retention assessment, the external focus group successfully completed the task a mean of 2.37 times faster (95% CI = 1.19, 4.74; P = .02) than the internal focus group. External focus of attention compared with internal focus of attention improves medical motor skill learning in novice learners.

Tension identification of bridge suspension rods based on multiple impedance parameters from self-inductive coils

ABSTRACT This study introduces a method for identifying tension in suspension rods based on impedance from self-inductive coils, grounded in electromagnetic induction and magnetic elasticity principles. The theoretical feasibility of this method was analysed through a high-frequency inductive coil model. Steel strand tensile tests, utilising self-inductive coil sensors at various frequencies, explored the relationship between resistance, inductance, and capacitance with tension. According to the experimental results, the sensitivity coefficient is used to determine the optimal prediction frequency of each impedance parameter. Within the optimal frequency range, a single impedance parameter regression prediction method based on the impedance difference ratio index was proposed. To improve the accuracy and stability of the predictions, a GA-BP neural network prediction model based on the fusion of multiple impedance parameters was proposed. The results indicate that the GA-BP neural network prediction method, which integrates multiple impedance parameters, achieves higher accuracy in identifying cable forces than the single impedance parameter regression prediction method. Specifically, at a frequency of 5 kHz, the relative error of the GA-BP neural network prediction method, which integrates multiple impedance parameters, was only 3.74%. This research offers a novel approach for future in-service tension identification in bridge suspension rods.

Detection of outliers in survey–weighted linear regression

ABSTRACT Regression diagnostics help identify influential data points in a model. Detecting outliers in complex survey design data involving stratification, clustering, and unequal probability sampling is difficult due to the presence of masking, where one outlier makes it hard to detect others. The masking factor for survey–weighted linear regression is developed and applied to analyzing the Household Consumer Expenditure dataset of 68th round of the National Sample Survey Organization survey of India. Regression parameters are calculated before and after detection and removal of outliers. The standard error of regression parameters for survey-weighted least squares models is reduced by 2% for the intercept, 5% for variable “meat” (X 5), 4% for “served processed food” (X 9), and 4% for “packaged processed food” (X 10). Inference alters the significance of regression coefficients of the variable “served processed food” (X 9) leading to the emergence of significance. There is no change in inference for other variables.