To determine the effect of quarantine on eating habits and weight change, as well as the primary changes in weight and eating habits among King Saud University students. This study aims to identify the effects of quarantine on eating behaviours and weight changes. Also, it determines the main changes in eating behaviours and weight among KSU students in Riyadh from March 23 to June 21, 2020. This is a cross-sectional study of a random sample of KSU students. Saudi male and female (non-pregnant females) bachelor’s degree students at KSU in the Riyadh region, who had not tested positive for COVID-19, satisfied the selection criteria. The total number of responses to the questionnaire was 1053; after the elimination of 320 responses that met the exclusion criteria, 733 students were included in the study. The current study results confirmed as 52.4% of students ate more of snacks. Among students’ most consumed food items during the quarantine were starches, coffee, dairy, and poultry. Contrastingly, the least consumed food items were energy drinks, fish, and soft drinks. Further, 53.7% of the students gained weight, which was associated with anxiety, boredom, and consumption of red meat and eggs. However, weight loss among students was associated with concerns regarding weight gain, changes in food quantity, changes in appetite, and the consumption of vegetables. Although lockdowns are an important safety measure to protect public health, the findings of this study show that quarantine affects eating and Emotional Eating (EE) behavior such as consuming more starch, dairy, and poultry among students at KSU. Furthermore, this study can help the Saudi authorities develop guidelines to direct Saudi food markets to increase advertising and promote healthy foods during situations like the COVID-19 pandemic.
Statisticians use to classify Statistics into two main parts, namely Descriptive and Inferential Statistics. Here, we suggest reclassifying Inferential Statistics into two parts, namely Diagnostic Statistics and Predictive Statistics. Based on that we will have four levels to analyze data (Descriptive, Diagnostic, Predictive and Perspective Statistics). Descriptive statistics mainly related to Graphs, Frequency tables, Measures of Central Tendency, Measures of Variation and Measures of Shape. Diagnostic statistics mainly related to the effects of the Independent variables (inputs) on the Dependent (Target) variable based on the Tests of Correlation or Association, Tests for Means differences and Tests for Classification. Predictive statistics mainly related to Estimation, Regression techniques and Time series Analysis for the Dependent (Target) variable. Perspective statistics mainly related to the previous three levels and acts as a prescription to how to solve or prevent the problem. In this paper, we will clarify the statistical tests used in each level of statistical analysis and will give an example on a real data related to Gynecologic Cancer
The structure equation model (SEM) is a multivariate technique for studying relationships among a set of substantively meaningful variables. The question as to which model best fits the data well, reflects the underlying theory, known as model fit, and is by no means much agreed upon. The present study aims at studying the effects of weights used in transforming the original data to improve model fit indices within the framework of SEM. The weighted values ranged from 0.1 to 0.95; and varying sample sizes (50, 100, 200, 500, and 1000) were used in the present study. Moreover, the study also examines the performance of the weighted values across the symmetric, positive, and negative skewed distributions. To achieve this goal, a Monte Carlo simulation study was carried out using Python, with 500 iterations performed for each weight and sample size. The results show that the $p$-value gets better in the weighted data in comparison with the values in the case of the original data, with the weights $W=[0.2,0.8]$ for the left-skewed distributions, $W=[0.3,0.9]$ for the symmetrical distributions and $W=[0.75,0.85]$ for the right-skewed distributions. Also, the results show that the larger the sample size, the greater the number of cases that achieve a better $p$-value with the weighted data. The results also show that the values of goodness of fit index (GFI) and root mean square error of approximation (RMSEA) get better in the weighted data in comparison with the values of the case of the original data, with the weights $W=[0.1,0.2] \cup W=[0.8,0.9]$ for the left-skewed distributions, $W=[0.3,0.15] \cup W=[0.9,0.95]$ for the symmetrical distributions and $W=[0.7,0.85]$ for the right-skewed distributions. Also, the results show that the larger the sample size, the lower the number of cases that achieve better GFI and RMSEA with the weighted data. Received: June 17, 2023Revised: July 30, 2023Accepted: August 5, 2023
The traditional way when teaching statistics is that Statistics has two main branches, namely Descriptive and Inferential statistics. The Descriptive statistics are brief descriptive coefficients that summarize a given data set, which can be either a representation of the entire or a sample of a population, While Inferential statistics are based on a random sample of data taken from a population to describe and make inferences about the population. The modern way we suggest for teaching statistics is to divide the Statistics into three branches. Namely Descriptive, Diagnostic and Predictive Statistics. In this paper, we will re-classify the inferential Statistics tests to Diagnostic Statistics tests and Predictive Statistics tests and give an applied example
The four levels of data analytics techniques (descriptive, diagnostic, predictive, and perspective) were used as a methodology. We also used data mining techniques to predict Gynecologic cancer before any lab test or surgical intervention. Influencing and associating between factors are used to cover hidden relationships or unknown patterns. We focused on three types of Gynecologic cancer (cervical, endometrial, and ovarian cancer). We collected an initial examination of 513 (228 benign and 285 malignant) patients from King Abdulaziz University Hospital (Saudi Arabia). Data were collected during the period of 16 years (2000-2016). After examining many models, we found that the classification trees C5 and CHAID beside the Support Vector Machine (SVM) algorithm give the highest accuracy, with the values of 87.33 %, 79.53%, and 78.36 % respectively. The sensitivity and specificity were found to be 86.18 % and 89.00 % for C5. The corresponding values for CHAID were found to be to equals to 82.20 % and 76.72 % while for support vector machine (SVM) the values were found to be 83.74 % and 77.10 %.
The Six Sigma methodology has become a frequently used term in discussions regarding quality management and it is considered to be an important management philosophy, which supports organizations in their efforts to obtain satisfied customers. In this study, we proposed Six Sigma methodology to improve the Job Performance in the Technical and Vocational Training Corporation (TVTC) in Saudi Arabia. We focus on three dimensions in our study; first: preparations for the application of the Six Sigma methodology in TVTC, second: the application requirements of Six Sigma methodology, and third: the application of the Six Sigma methods affects Improve Job performance in TVTC. Since the neural of Six Sigma methodology depends on the mediator statistical analysis, we suggest using the Structural Equation Modeling (SEM) to test the proposed Six Sigma model in TVTC. From SEM results, we find that the Six Sigma preparations have a significant effect on the application requirements and that the application requirements have a significant effect on the Job performance in TVTC. Based on that, we recommend using the Six Sigma methodology in TVTC to improve the performance of these corporations.
The studies carried in the world regarding the possible significant influence of climate change on the health and safety of outdoor workers has not been given the due consideration (especially in the least developed and developing countries). Hundreds and thousands of outdoor workers are exposed to elevated temperatures, humid environments and climate extremes in combination with urban air pollution; which is ultimately impacting their safety and well-being. The statistics show that in the past few years, due to the rise in temperature on earth and frequent heat waves within urban settlements, an abrupt increase has been observed in the rate of heat-related health problems. Exposure to extreme heat (exceeding 40 ºC)causes many direct and indirect health hazards, which include vector-borne diseases and exposure to certain harmful chemicals. Currently, the climatic and heat-related effects are decreasing the working capacity of workers and in the future it is projected that the frequency and magnitude of these effects will increase. With the rise in temperature and the occurrence of frequent heat waves in urban areas, the number of health issues due to high (maximum average)temperature has increased rapidly. This article discusses the impacts of heat exposure and climatic change on productivity,health and safety of outdoor workers by summarizing findings from the literature, and eventually recommends control measures for reducing heat exposure at the outdoor work areasand climatic adaptations. In addition, it argues that there is a need for more research about the impacts on health and economic conditions due to heat and climate change in the workplace on global level (especially in developing countries).
The four levels of data analytics techniques (descriptive, diagnostic, predictive, and perspective) were used as a methodology.We also used data mining techniques to predict Gynecologic cancer before any lab test or surgical intervention.Influencing and associating between factors are used to cover hidden relationships or unknown patterns.We focused on three types of Gynecologic cancer (cervical, endometrial, and ovarian cancer).We collected an initial examination of 513 (228 benign and 285 malignant) patients from King Abdulaziz University Hospital (Saudi Arabia).Data were collected during the period of 16 years (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016).After examining many models, we found that the classification trees C5 and CHAID beside the Support Vector Machine (SVM) algorithm give the highest accuracy, with the values of 87.33 %, 79.53%, and 78.36 % respectively.The sensitivity and specificity were found to be 86.18% and 89.00 % for C5.The corresponding values for CHAID were found to be to equals to 82.20 % and 76.72 % while for support vector machine (SVM) the values were found to be 83.74 % and 77.10 %.
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