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Grupo de Análise de Mercado

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Making Numbers Count By Chip Heath Pdf



A lively, practical, first-of-its-kind guide to understanding cold, clinical data and harnessing it to tell a persuasive story.__________How many hours' worth of songs are on your Spotify Wrapped this year?How much is your commute time really worth?How do you work out how likely you are to get Covid based on the official statistics?How do your viewing hours track against the most popular shows on Netflix?Whether you're interested in global problems like climate change, running a business, or just grasping how few people have washed their hands between visiting the bathroom and touching you, this book will help math-lovers and math-haters alike translate the numbers that illuminate our world.Until very recently, most languages had no words for numbers greater than five - anything from six to infinity was known as 'lots'. While the numbers in our world have become increasingly complex, our brains are stuck in the past. Yet the ability to communicate and understand numbers has never mattered more. How can we more effectively translate numbers and stats - so fundamental to the next big idea - to make data come to life?Drawing on years of research into making ideas stick, Chip Heath and Karla Starr outline six critical principles that will give anyone the tools to communicate numbers with more transparency and meaning. Using concepts such as simplicity, concreteness and familiarity, they show us how to transform hard numbers into their most engaging form, allowing us to bring more data, more naturally, into decisions in our schools, our workplaces and our society.




Making Numbers Count by Chip Heath Pdf



As RNA-seq quantification is based on read counts that are absolutely or probabilistically assigned to transcripts, the first approaches to compute differential expression used discrete probability distributions, such as the Poisson or negative binomial [48, 54]. The negative binomial distribution (also known as the gamma-Poisson distribution) is a generalization of the Poisson distribution, allowing for additional variance (called overdispersion) beyond the variance expected from randomly sampling from a pool of molecules that are characteristic of RNA-seq data. However, the use of discrete distributions is not required for accurate analysis of differential expression as long as the sampling variance of small read counts is taken into account (most important for experiments with small numbers of replicates). Methods for transforming normalized counts of RNA-seq reads while learning the variance structure of the data have been shown to perform well in comparison to the discrete distribution approaches described above [55, 56]. Moreover, after extensive normalization (including TMM and batch removal), the data might have lost their discrete nature and be more akin to a continuous distribution. 041b061a72


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