The subject of statistics is rife with misleading terms. I have written about this before in such posts as Teaching Statistical Language and It is so random. But the terms sampling error and non-sampling error win the Dr Nic prize for … Continue reading →
In an earlier video, I showed how to calculate expected counts in a contingency table using marginal proportions and totals. (Recall that expected counts are needed to conduct hypothesis tests of independence between categorical random variables.) Today, I want to share a second video of calculating expected counts – this time, using joint probabilities. This method uses […]
The Hardy-Weinberg law is a fundamental principle in statistical genetics. If its 7 assumptions are fulfilled, then it predicts that the allelic frequency of a genetic trait will remain constant from generation to generation. In this new video tutorial in my Youtube channel, I explain the math behind the Hardy-Weinberg theorem. In particular, I clarify […]
A common task in statistics and biostatistics is performing hypothesis tests of independence between 2 categorical random variables. The data for such tests are best organized in contingency tables, which allow expected counts to be calculated easily. In this video tutorial in my Youtube channel, I demonstrate how to calculate expected counts using marginal proportions […]
I first started my video tutorial series on survival analysis by defining the hazard function. I then explained how this definition leads to the elegant relationship of . In my new video, I derive 6 useful mathematical relationships that exist between any 2 of the 3 quantities in the above equation. Each relationship allows one quantity […]
According to the central limit theorem, if random variables, , are independent and identically distributed, is sufficiently large, then the distribution of their sample mean, , is approximately normal, and this approximation is better as increases. One of the most remarkable aspects of the central limit theorem (CLT) is its validity for any parent distribution of […]
In an earlier video, I introduced the definition of the hazard function and broke it down into its mathematical components. Recall that the definition of the hazard function for events defined on a continuous time scale is . Did you know that the hazard function can be expressed as the probability density function (PDF) divided by the […]
Finally, here it is, check out the video below as Stephen Wolfram showcases the Wolfram language, From my previous post, I said that I used Wolfram Mathematica for about a year before I embrace R. And frankly, I've been in love with Mathematica; it nev...
The hazard function is a fundamental quantity in survival analysis. For an event occurring at some time on a continuous time scale, the hazard function, , for that event is defined as , where is the time, is the time of the occurrence of the event. However, what does this actually mean? In this Youtube […]
Khan academy probability videos and exercises aren’t good either Dear Mr Khan You have created an amazing resource that thousands of people all over the world get a lot of help from. Well done. Some of your materials are not … Continue reading →
