You’re working with timeseries data, and you’re at the initial discovery stage. You’re trying to figure out all the different ways that the data varies in time. There may be constant trends, whereby the data is constantly increasing or decreasing in time. There may be completely random variations. …
How do neural networks really work? I will show you a complete example, written from scratch in Python, with all the math you need to completely understand the process.
I will explain everything in plain English as well. You could just follow along, read just the text and still get…
You have a data sample. From it, you want to calculate a confidence interval for the population mean value. What’s the first thing you think about? It’s usually a t-test.
But the t-test has several requirements, one of which is that the sampling distribution of the mean is nearly normal…
But how was that number calculated? Turns out, the basic value is pretty easy. I’ll show you how to do that, and then I will make an estimate for how confident we are that the value is right.
To test a vaccine, you need to do a randomized blind trial…
This is part 2 of this article:
To recap: there’s a pandemic going on, 1% of people have the virus. There’s a test that can detect the virus, and the test is 99% reliable (for both positive and negative results).
But this time, when you take the test, the result…
Let’s say there’s a virus pandemic sweeping through the population, and 1% of people have the virus. Let’s say there’s a test for this condition, and the test is 99% reliable, meaning — out of 100 tested cases, the test will be correct in 99 cases, and will be wrong…
Sometimes trends need to be removed from timeseries data, in preparation for the next steps, or part of the data cleaning process. If you can identify a trend, then simply subtract it from the data, and the result is detrended data.
If the trend is linear, you can find it…
Math is hard, let’s go shopping — for tutorials, that is. I definitely wish I had read this tutorial before trying some things in Python that involve extremely large numbers (binomial probability for large values of n) and my code started to crash.
But wait, I hear you saying, Python…
