Statistics

16 posts
What is R, what it was, and what it will become

Roger Peng provides a lesson on the roots of R and how it got to where it is now: Chambers was referring to the difficulty in naming and characterizing the S system. Is it a programming language? An environment? A statistical package? Eventually, it seems they settled on “quantitative programming environment”, or in other words, “it’s all the things.” Ironically, for a statistical environment, the first two versions did not...

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Amazon stores voice recordings indefinitely

Alfred Ng for CNET: Sen. Chris Coons, a Democrat from Delaware, sent a letter to Amazon CEO Jeff Bezos in May, demanding answers on Alexa and how long it kept voice recordings and transcripts, as well as what the data gets used for. The letter came after CNET’s report that Amazon kept transcripts of interactions with Alexa, even after people deleted the voice recordings. The deadline for answers was June...

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Machine learning to steal baseball signs

Mark Rober, who is great at explaining and demonstrating math and engineering to a wide audience, gets into the gist of machine learning in his latest video: Tags: baseball, machine learning, Mark Rober

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Unproven aggression detectors, more surveillance

In some public places, such as schools and hospitals, microphones installed with software listen for noise that sounds like aggression. The systems alert the authorities. It sounds useful, but in practice, the detection algorithms might not be ready yet. For ProPublica, Jack Gillum and Jeff Kao did some testing: Yet ProPublica’s analysis, as well as the experiences of some U.S. schools and hospitals that have used Sound Intelligence’s aggression detector,...

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Book review: Visualizing Baseball

I requested a copy of Jim Albert’s Visualizing Baseball book, which is part of the ASA-CRC series on Statistical Reasoning in Science and Society that has the explicit goal of reaching a mass audience. The best feature of Albert’s new volume is its brevity. For someone with a decent background in statistics (and grasp of basic baseball jargon), it’s a book that can be consumed within one week, after which...

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Machine boss

For The New York Times, Kevin Roose on the possibility of machines becoming your boss: The goal of automation has always been efficiency, but in this new kind of workplace, A.I. sees humanity itself as the thing to be optimized. Amazon uses complex algorithms to track worker productivity in its fulfillment centers, and can automatically generate the paperwork to fire workers who don’t meet their targets, as The Verge uncovered...

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Myth of the impartial machine

In its inaugural issue, Parametric Press describes how bias can easily come about when working with data: Even big data are susceptible to non-sampling errors. A study by researchers at Google found that the United States (which accounts for 4% of the world population) contributed over 45% of the data for ImageNet, a database of more than 14 million labelled images. Meanwhile, China and India combined contribute just 3% of...

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Simulation of fan emotions during a basketball game

During a game, the range of emotions can vary widely across a crowd. Will Hipson, making use of some emotion dynamics, simulated how that range can change through a game: What I’m striving to simulate are the laws of emotion dynamics (Kuppens & Verduyn, 2017). Emotions change from moment to moment, but there’s also some stability from one moment to the next. Apart from when a basket is scored, most...

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Using statistics for basketball efficiency

Ivana Seric is a data scientist for the Philadelphia 76ers who tries to improve player effectiveness by analyzing tracking data. Aki Ito for Bloomberg: I really want to see the relationship of winning and teams who more deeply follow statistics. Is it at a place yet where this actually helps or is still more about gut and heart? Tags: basketball, Bloomberg

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Seeking simplicity in complex data: Bloomberg’s dataviz on UK gender pay gap

Bloomberg featured a thought-provoking dataviz that illustrates the pay gap by gender in the U.K. The dataset underlying this effort is complex, and the designers did a good job simplifying the data for ease of comprehension. U.K. companies are required to submit data on salaries and bonuses by gender, and by pay quartiles. The dataset is incomplete, since some companies are slow to report, and the analyst decided not to...

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