Table 7 - uploaded by Rakesh Kumar Rana
Content may be subject to copyright.
Excerpts from the Chi-square distribution table 

Excerpts from the Chi-square distribution table 

Source publication
Article
Full-text available
In medical research, there are studies which often collect data on categorical variables that can be summarized as a series of counts. These counts are commonly arranged in a tabular format known as a contingency table. The chi-square test statistic can be used to evaluate whether there is an association between the rows and columns in a contingenc...

Contexts in source publication

Context 1
... we want to test our hypothesis at 5% level of significance than our predetermined alpha level of significance is 0.05. Looking into the Chi-square distribution table [ Table 7] with 1 degree of freedom and reading along the row we find our value of χ² (3.42) lies between 2.706 and 3.841. The corresponding probability is between the 0.10 and 0.05 probability levels. ...
Context 2
... we want to test our hypothesis at 5% level of significance than our predetermined alpha level of significance is 0.05. Looking into the Chi-square distribution table [ Table 7] with 1 degree of freedom and reading along the row we find our value of χ² (3.42) lies between 2.706 and 3.841. The corresponding probability is between the 0.10 and 0.05 probability levels. ...

Similar publications

Article
Full-text available
This paper introduces JASP, a free graphical software package for basic statistical procedures such as t tests, ANOVAs, linear regression models, and analyses of contingency tables. JASP is open-source and differentiates itself from existing open-source solutions in two ways. First, JASP provides several innovations in user interface design; specif...

Citations

... This test primarily assesses the association between two categorical responses or differences between the study groups in the proportion of the risk factor of interest. Data were arranged in contingency tables for analysis, and a p-value of < 0.05 was considered statistically significant (Shih and Fay 2017;Rana and Singhal 2015). ...
Article
Full-text available
This study is an assessment of the effects of outdoor air pollution and extreme weather events on the health of outdoor workers in Delhi, including auto rickshaw drivers, street vendors, and sweepers. To carry it out, a cross-sectional and perception-based epidemiological research design was used, and the primary tool used for data collection was a questionnaire. Two hundred twenty-eight people participated in the survey, and a pulmonary function test (PFT) was performed on 63 participants. Most of the respondents from different occupational groups complained about headaches/giddiness, nausea, and muscular cramps during extreme heat events due to the physically demanding nature of their jobs in the outdoor environment. Furthermore, autorickshaw drivers reported the highest prevalence of ophthalmic symptoms, such as eye redness (44%) and eye irritation (36%). In comparison, vendors reported a higher prevalence of headaches (43%) and eye redness (40%) due to increased exposure to vehicular emissions. Among sweepers, musculoskeletal problems like joint pain (40%), backache (38%), and shoulder pain (35%) were most prevalent due to occupation-related ergonomic factors. In addition, the majority of autorickshaw drivers (47%), vendors (47%), and sweepers (48%) considered that air quality had a severe impact on their health. PFT results showed that most respondents had restricted lung function. Binary logistic regression analysis showed that lung function impairment had a significant association with smoking (p = 0.023) and age (0.019). The odds ratio for smoking, which was around 4, indicated that respondents who smoked had a nearly four times greater risk of developing lung impairment. The study also highlighted the need for using personal protective equipment and developing guidelines to reduce their exposure level.
... Prior to the analysis, we used Cronbach's alpha to calculate the internal reliability of the three categories (socio-cultural, economic, and environmental). The chi-square test is a statistical test for assessing whether two variables are associated with each other (Ugoni and Walker 1995;Rana and Singhal 2015). The chi-square test was used for evaluating whether the prioritization of coastal management and involvement in tourism sector-related occupations could be associated with local residents' perceptions of tourism impacts. ...
... The study data were collected through face-to-face interviews with in-depth interactions; each interview, which utilized a questionnaire translated into Bahasa, had a duration of 40 to 60 min. The sample size of 47 was large enough for the chi-square test to be applied validly, which requires a minimum sample size that varies from 20 to 50 and has no expected cut-off (Rana and Singhal 2015). Table 1 summarizes the respondents' sociodemographic characteristics. ...
Article
Full-text available
In Indonesia, tourism has become a promising major economic sector, particularly because of its contributions toward developing the economy and creating employment opportunities for local communities with rich coastal ecosystems. However, the balance between the environmental, social, and economic realms has come into question, as unsustainable tourism practices continue to be promoted in Indonesia. To address such challenges, it is important to identify tourism impacts and provide sustainable policies and plans. Communities often record tourism impacts through their perceptions and act as important stakeholders in the process of sustainable tourism development. We examined tourism impacts on coastal ecosystems in Karimunjawa from the perspective of local communities. More comprehensively, we investigated their perceptions from three perspectives: socio-cultural, economic, and environmental. The study results revealed that the respondents held positive perceptions about tourism's impact on socio-cultural and economic sectors and negative perceptions about its impact in the environmental domain. A chi-square test and Spearman's correlation analysis indicated that the respondents' educational attainment and tourism involvement influenced their perceptions on these issues. The current study results could be used as a baseline reference for contextualizing sustainable tourism plans regarding small island ecosystems in Indonesia. Supplementary information: The online version contains supplementary material available at 10.1007/s11852-022-00852-9.
... In this research work, an attempt has been made to understand the temporal change detection analysis of vegetation health; the researchers calculated the rate of change (%) from January to May and May to December 2020 of each class of different indices, including NDVI, SAVI and LSWI. The chi-square technique (Rana and Singhal 2015) has been applied to determine if the rate of change is uniform or not. Chi-square has been calculated using the below formula: Map 2.11 LSWI map of the study area in January 2020 Source: Compiled from USGS Platform and Prepared by the Researchers Because of the rapidly increasing population, the built-up area also increased, and as a result, the vegetative area decreased. ...
Chapter
Climate change has put tremendous impact on the environment in the current scenario. The consequences are extensive consequences on the atmosphere, agronomy, water resources, biome, natural reserves, budget, biodiversity, and social security. Odisha, lying in the Eastern Coast of India connecting the Bay of Bengal, has a stretch of 480 km of coastline and is always vulnerable to climate change in terms of heavy storm like cyclones, beach erosion, coastal flooding, storm swell, and denudation. This state has scores of agro-climatic sectors which require improvement in the shape of diverse reworking approaches keeping pace with the ongoing scenario of climate change. Adaptation strategies such as agriculture, fisheries and animal husbandry, water, health, and coastal and disaster risk management have been formulated looking at the vulnerability, food security, and other parameters. Major steps have been initiated to mitigate the impact of climate change; still a lot of further strategies need to be dealt with to keep the region safe and disaster free. These include energy, urban development, transport, industries, and waste disposal. Proper attention must be adhered to embracing judicious policies on energy efficiency based on enactment, modifying state building codes and development codes to improve LULC, transportation, and energy productivities, establishment of new renewable energy policies, assortment and related criterions and reinforce multi-segment parameters to cater to the upcoming challenges related to reduction in poverty and increasing the adaptive capacity. Several initiatives have been undertaken in the government, private, and NGO sectors, but still lack of proper vision, appropriate mission, and slow pace of implementation has jeopardized the entire development.
... In this research work, an attempt has been made to understand the temporal change detection analysis of vegetation health; the researchers calculated the rate of change (%) from January to May and May to December 2020 of each class of different indices, including NDVI, SAVI and LSWI. The chi-square technique (Rana and Singhal 2015) has been applied to determine if the rate of change is uniform or not. Chi-square has been calculated using the below formula: Map 2.11 LSWI map of the study area in January 2020 Source: Compiled from USGS Platform and Prepared by the Researchers Because of the rapidly increasing population, the built-up area also increased, and as a result, the vegetative area decreased. ...
Chapter
Surface water and ground water are used for agricultural, industrial, and domestic purposes. Rainfall and the corresponding runoff generated are important hydrological processes which depend on the local physiographic, climatic, and biotic factors. Remotely sensed data provide valuable and real-time spatial information on natural resources and physical parameter. Due to climate changes and human interference to the river systems, flood risks have also increased. Flood losses can be reduced by proper floodplain management. Watershed means a naturally occurring hydrologic unit that contributes storm runoff to a single waterway classified on the basis of its geographical area. The aim of the study is to throw some light on the importance of watershed management using geospatial techniques. In this analysis, studies of the slope, contour, and terrain profile of study area and behavior of stream segments, drainage direction, flow accumulation, Land Use Land Cover (LULC), drainage map, etc. were carried out using QGIS-ArcMap 10.1. There are two river basins in upper Tapi region—one is Tapi River and the other is Purna River. Results show the depletion of both ground and surface water in the watershed. Green cover is considerably reduced in the region, and hence, the watershed is less humid and dry. Study also reveals that due to change in land use and land cover, there are more wastelands in the watershed. The study also provides an indication to restore the vegetation cover and will be able to help policy and decision-makers to understand the importance of watershed and need for its characteristics analysis.
... Further statistical analysis was conducted using Pearson's chi-square test. The chi-square test is a nonparametric test used to determine the hypothesis of no association between variables (Singhal & Rana, 2015). In this context, this is a practical test to determine the relationship between the general influencing elements (age, occupation, years attended, campus) and the study's derived criteria. ...
... 40 features were selected and extracted simultaneously after experimentation with simple feature selection methods such as Uni-variate feature selection, tree based feature selection etc. and using PCA to extract the features. The chi-squared test ( ), is a summation of the square of difference between expected frequency (E i ) and observed frequency (O i ) divided by expected value as shown in equation (2) [21]. ...
Article
The Intelligent Transportation System (ITS) is said to revolutionize the travel experience by making it safe, secure and comfortable for the people. Although vehicles have been automated up to a certain extent it still has critical security issues that require thorough study and advanced solutions. The security vulnerabilities of ITS allows the attacker to steal the vehicle. Therefore, the identification of drivers is required in order to develop a safe and secure system so that the vehicles can be protected from theft. There are two ways in which a driver can be identified 1) face recognition of the driver and 2) based on driving behavior. Face recognition includes image processing of 2-D images and learning of the features, which require high computational power. Drivers are known to have unique driving styles, whose data can be captured by the sensors. Therefore, the second method identifies drivers based on the analysis of the sensor data and it requires comparatively lesser computational power. In this paper, an optimized deep learning model is trained on the sensor data to correctly identify the drivers. The Long Short Term Memory (LSTM) deep learning model is optimized for better performance. The novelty of the approach in this work is the inclusion of hyperparameter tuning using a nature-inspired optimization algorithm, which is an important and essential step in discovering the optimal hyperparameters for training the model which in turn increases the accuracy. The CAN-BUS dataset is used for experimentation and evaluation of the training model. Evaluation parameters such as accuracy, precision score, F1 score and ROC AUC curve are considered to evaluate the peformance of the model.
... The frequencies of categorical variables were tabulated, and they were analyzed using the chi-square tests for contingency tables. More specifically, this statistical analysis was used to determine whether there was any difference between the study groups in the proportions of the risk factor of interest [24]. This analysis tests the following four null hypotheses: ...
Article
Full-text available
The spread of coronavirus worldwide has affected consumer behavior in many ways. This paper tries to investigate the impact of the SARS-CoV2 (COVID-19) on food consumption behavior of consumers. Food consumption motivation data were assessed and compared before, during, and after the quarantine. An online survey was conducted among about 900 people from 54 different cities in Turkey, between April and May 2020, trying to understand consumers’ changing behavior in their food choices, preferences, and habits during the pandemic period. The aim of this paper is (i) to examine how consumer preferences were influenced by the COVID-19 quarantine period, using an ordered probit analysis, and (ii) to identify differences in the preferences for the food itself, food disinfection and cooking, and shopping preferences before and during the quarantine. Finally, as per the consumers’ body mass index (BMI), correlation with their mood and eating frequencies was observed. The findings indicate that, under stress conditions, like the quarantine period, food preferences and eating behavior changed, and consumers put all those emotions and information into their consumption process.
... 2 and 3). Here we calculated the correlation coefficient between numerical and nominal columns as the Coefficient and the Pearson's chi-square value39 . ...
Article
Full-text available
Endometriosis—a systemic and chronic condition occurring in women of childbearing age—is a highly enigmatic disease with unresolved questions. While multiple biomarkers, genomic analysis, questionnaires, and imaging techniques have been advocated as screening and triage tests for endometriosis to replace diagnostic laparoscopy, none have been implemented routinely in clinical practice. We investigated the use of machine learning algorithms (MLA) in the diagnosis and screening of endometriosis based on 16 key clinical and patient-based symptom features. The sensitivity, specificity, F1-score and AUCs of the MLA to diagnose endometriosis in the training and validation sets varied from 0.82 to 1, 0–0.8, 0–0.88, 0.5–0.89, and from 0.91 to 0.95, 0.66–0.92, 0.77–0.92, respectively. Our data suggest that MLA could be a promising screening test for general practitioners, gynecologists, and other front-line health care providers. Introducing MLA in this setting represents a paradigm change in clinical practice as it could replace diagnostic laparoscopy. Furthermore, this patient-based screening tool empowers patients with endometriosis to self-identify potential symptoms and initiate dialogue with physicians about diagnosis and treatment, and hence contribute to shared decision making.
... This method is generally used for checking the independence of two variables. Equation (2) is used for calculating the chi-square values [43]: ...
Article
Full-text available
Fused deposition modelling (FDM)-based 3D printing is a trending technology in the era of Industry 4.0 that manufactures products in layer-by-layer form. It shows remarkable benefits such as rapid prototyping, cost-effectiveness, flexibility, and a sustainable manufacturing approach. Along with such advantages, a few defects occur in FDM products during the printing stage. Diagnosing defects occurring during 3D printing is a challenging task. Proper data acquisition and monitoring systems need to be developed for effective fault diagnosis. In this paper, the authors proposed a low-cost multi-sensor data acquisition system (DAQ) for detecting various faults in 3D printed products. The data acquisition system was developed using an Arduino micro-controller that collects real-time multi-sensor signals using vibration, current, and sound sensors. The different types of fault conditions are referred to introduce various defects in 3D products to analyze the effect of the fault conditions on the captured sensor data. Time and frequency domain analyses were performed on captured data to create feature vectors by selecting the chi-square method, and the most significant features were selected to train the CNN model. The K-means cluster algorithm was used for data clustering purposes, and the bell curve or normal distribution curve was used to define individual sensor threshold values under normal conditions. The CNN model was used to classify the normal and fault condition data, which gave an accuracy of around 94%, by evaluating the model performance based on recall, precision, and F1 score.
... The Chi-Square test is a non-parametric statistical significance analysis method that is suitable for analyzing the significance of independent variables. To state mathematically (Singhal and Rana, 2015;McHugh, 2013), ...
Preprint
Full-text available
Landslides have been a regular occurrence and an alarming threat to human life and property in the era of anthropogenic global warming. An early prediction of landslide susceptibility using a data-driven approach is a demand of time. In this study, we explored the eloquent features that best describe landslide susceptibility with state-of-the-art machine learning methods. In our study, we employed state-of-the-art machine learning algorithms including XgBoost, LR, KNN, SVM, Adaboost for landslide susceptibility prediction. To find the best hyperparameters of each individual classifier for optimized performance, we have incorporated the Grid Search method, with 10 Fold Cross-Validation. In this context, the optimized version of XgBoost outperformed all other classifiers with a Cross-validation Weighted F1 score of 94.62%. Followed by this empirical evidence, we explored the XgBoost classifier by incorporating TreeSHAP and identified eloquent features such as SLOPE, ELEVATION, TWI that complement the performance of the XGBoost classifier mostly and features such as LANDUSE, NDVI, SPI which has less effect on models performance. According to the TreeSHAP explanation of features, we selected the 9 most significant landslide causal factors out of 15. Evidently, an optimized version of XgBoost along with feature reduction by 40%, has outperformed all other classifiers in terms of popular evaluation metrics with a Cross-Validation Weighted F1 score of 95.01% on the training and AUC score of 97%.