November 2017
Beginner
286 pages
8h 13m
English
book
Statistics for Data Science
by James C. Mott, Rajprasath Subramanian, Shaikh Salamatullah, James D. Miller, Vijayakumar Ramdoss
Content preview from Statistics for Data Science
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Bias
Let's start out with a discussion on the statistical bias.
A statistic is biased if it is calculated in such a way that it is analytically dissimilar to the population parameter being estimated.
One of the best explanations for bias that I've come across is the concept of a scale that is off zero by a small amount. In this scenario, the scale will give slightly over-estimated results. In other words, when someone steps on the scale, the total weight may be over or understated (which might make that person conclude that the diet they are on is working better than it really is).
In statistics, data scientists need to recognize that there are actually several categories that are routinely used to define statistical bias. The next section ...
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I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.