 Hi everyone it's MJ and welcome to the introductory course on point estimation. Now a very quick refresher, we know that the whole point of statistics is that we have some data and we want to calculate some information in order to answer questions and optimize processes. Now the information that we want is we want the parameters and the distribution of the random variable that generated the data in the first place. Now what point estimation is interested in, it's interested in calculating what these parameters should be. And the idea that it's saying point estimation is we're coming up with a single value and this is going to be very different to the next chapter on confidence intervals where we're going to be looking at a range of values. So in this chapter we're looking at a single value and we're going to see that there's two main methods in order to do your point estimation. They're something known as the method of moments and there's also the method of maximum likelihood. And we sometimes see that in the actual exams the method of maximum likelihood is a favorite for examiners and there are a lot of marks for doing this calculation. But the big idea in this course is to compare these methods and say well which one's better between the method of moments and the method of maximum likelihoods. And one way to compare them is to realize that both of these point estimations are in their own right random variables which means they have their own distributions and their own set of parameters and that is something else that we look at in this course. We kind of say well what distribution do they follow and what sort of parameters do they look at. And the interesting thing is the statistician who did a lot of this work is currently still alive. He's like 102 years old but he is still alive. So this is very very recent mathematics and statistics so it is quite an exciting field because it is so modern. Anyway we're going to talk more about all of this stuff in the course. I hope this has been a nice little introduction and as always feel free to ask any questions. Keep well. Cheers.