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Your results from a hypothesis test are statistically significant. Bravo! Are these results important? Not so. Significance not necessarily mean the results are practically significant in real world Statistical Analysis. In this blog post, I will list out few examples of real world statistical analyses that do not use p-value and significance testing. Before that, let us understand what exactly the p-value mean? Statswork is one among the country’s leader in providing Data Analysis Services and Statistical Consulting Services. Contact Statswork for availing our services.
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This is how it would look, if the world were represented by 100 people. China has 19 people out of 100 in world population which is followed by India with 17 people. More than 35% of world's population is occupied by both China and India. https://www.statsmonkey.com/per100/26508-world-population-count-statistics-by-country-2014-based-on-per-100.php
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Copy Link : gooread.fileunlimited.club/pw24/3030890090 | PDF Multivariate Statistical Machine Learning Methods for Genomic Prediction 1st ed. 2022 Edition Ipad This book is open access under a CC BY 4.0 licenseThis open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers
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Copy Link : gooread.fileunlimited.club/pw24/3030890090 | PDF Multivariate Statistical Machine Learning Methods for Genomic Prediction 1st ed. 2022 Edition Ipad This book is open access under a CC BY 4.0 licenseThis open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers