I've just discovered the awesome Brandon Rohrer and his blog while trying to find an intelligible article on Bayesian inference. What a goldmine - this guy is a born educator! Thank you for sharing your knowledge - it is well-appreciated!

Poisson vs Exponential distributions

Related yet different, here's how... A quick note on the "preliminary terrors" of notation: e is Euler's number - you'll find the e on your calculator or the EXP() function in Excel The parameter is conventionally written as λ (pronounced lambda). Poisson Exponential Number of events that occur in an interval of time Time taken between 2... Continue Reading →

Law of total probability – worked examples

According to Wikipedia the law of total probability "expresses the total probability of an outcome which can be realized via several distinct events". We can also think of this as the marginal probability: irrespsective of what road we took to get to this outcome, what is the total likelihood of the outcome occurring? Example 1... Continue Reading →

Expected value refresher

The expected value of an event is its most likely outcome. Assign each potential result a probability. The expected value is sum of all the potential results x their respective probabilities: ∑ (potential_result1 x probability1,… potential_resultn x probabilityn) Consider the simplest example possible, the coin flip. You'll be paid R10 if you pick tails, but... Continue Reading →

Multivariate regression

So: with linear regression (aka simple linear regression) we have one feature which we are using to predict a dependent value (for example number of rooms as a predictor of house price). With multivariate regression (aka multiple linear regression) we simply have multiple features which could be used to predict that dependent value (for example... Continue Reading →

T testing – a worked example

A simple one-sample T-test This variant on hypothesis testing is used when you have limitations, specifically: The population standard deviation (σ) is unknown and your sample size (n) is <30 The fundamentals The formula is a variant of what we've seen thus far, where x̄ = your sample mean, μ = a hypothesized population mean,... Continue Reading →

Proportion testing

Using everything we've learned so far about the central limit theorem, the z-score, and hypothesis testing, we can now also perform proportion testing! There are just a few new concepts to add into the mix: The preliminary terrors - notation & terminology p = the proportion of items that falls into H0 q = the... Continue Reading →

Hypothesis testing basics

A simple example of hypothesis testing is where we know what "normal" is, and we want to evaluate whether some sample conforms to our understanding of "normal", or is so unusual that it's indicative of an actual shift in behaviour or pattern. Make your hypothesis statement If I…(do this to an independent variable)….then (this will... Continue Reading →

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