International Conference on Statistical Methods for Analyzing Engineering Data

  Research team creates statistical model to predict COVID-19 resistance Proof-of-concept study shows promise for machine-learning system that uses electronic health data to make its predictions "If we can identify which people are naturally able to avoid infection by SARS-CoV-2, we may be able to learn -- in addition to societal and behavioral factors -- which genetic and environmental differences influence their defense against the virus," says lead study author Karen (Kai-Wen) Yang, a biomedical engineering graduate student in the Translational Informatics Research and Innovation Lab at The Johns Hopkins University. "That insight could lead to new preventive measures and more highly targeted treatments." For its study, the research team set out to determine if a machine-learning statistical model could use health characteristics stored in electronic health records -- providing patient data such as comorbidities (other medical conditions) and prescribed medication

  Different labour market opportunities for native and foreign born The labour market situation for native and foreign born differs. The results also show differences between Sweden and the EU regarding native and foreign born people. The educational attainment, parents' educational background and language skills play a large role in employment and job opportunities. An EU-regulated supplementary survey (Ad Hoc module) to the Labour Force Survey (LFS) has been used to compare how factors such as educational attainment, language skills affect employment. Comparisons are made between native and foreign born for year 2021 and differences between Sweden and the other countries in the European Union (EU) as an average are highlighted. Fewer foreign born outside the labour force Sweden The report shows that employment was higher among native born people than among foreign born people, this applies to both Sweden and the EU. The proportion of employed foreign born persons was higher in S

                                                      statistical significance   In  statistics ,  statistical significance  is a "term indicating that the results obtained in an analysis of study data are unlikely to have occurred by chance, and the null hypothesis is rejected. When statistically significant, the probability of the observed results, given the null hypothesis, falls below a specified level of probability (most often P < 0.05)." [2]  The P-value, which is used to represent the likelihood the observed results are due to chance, is defined at "the probability, under the assumption of no effect or no difference (the null hypothesis), of obtaining a result equal to or more extreme than what was actually observed. Hypothesis testing Usually, the null hypothesis is the there is no difference between two samples in regard to the factor being studied. Choosing a statistical method The choice of statistical method to use in an analysis is determined by: ·  

                               Statistics theory   Statistics theory is a mathematical approach to describe something, predict events, or analyse the relationship between things. "Statistics" is a broader concept that also includes the collection, analysis and presentation of numerical data. [1] Statistical analysis can, for example, describe the average income of a population, test whether two groups have the same average income, or analyse factors that might explain the income level for a particular group. The application of mathematical theory to statistics makes it possible to test relationships between two or more groups or to test how observations compare to a prediction. Some of the statistical concepts include mean (average), standard deviation (how concentrated or spread out things are), and correlation (how related two different variables are). These concepts are further explained in this article. Statistics theory is used in a very wide variety of fields. [2] [3