 So, welcome to dealing with materials data. In this course we are looking at the analysis, collection and interpretation of data from material science and engineering and we have looked at several probability distributions and we are continuing with the probability distributions module and now I want to describe a few more distributions which are of importance to us and one bunch of distributions which are very useful for us is the chi-squared students t and f distribution and they are very useful for estimating confidence intervals and for doing modeling that is for doing regression and chi sqt and f are the commands in r for dealing with these distributions and we are not going to do any tutorial with them right away but when we do the estimation of confidence intervals and regression and ANOVA and things like that we will come back and look at these distributions and how they are useful in analyzing data in material science and engineering. So, that we are going to do. One more class of distributions which are very important and some of you might have heard some of these is the Maxwell Boltzmann Fermi Dirac and Bohr-Seinstein distributions. These give probabilities that a particle is in an energy state E, a Maxwell Boltzmann is a classical statistics and it looks at identical but distinguishable particles and the probability distribution function for Maxwell Boltzmann is 1 by A exponential E by kT and k is the Boltzmann constant T is the absolute temperature. So, this is the probability distribution function. Fermi Dirac is a quantum probability distribution and it looks at identical and indistinguishable particles with half integer spins. So, Fermi Dirac distribution is described by 1 by 1 plus A exponential E by kT and Bohr-Seinstein is also a quantum distribution and it looks at identical and indistinguishable particles but the spins are integer spins. So, things like radiation then has to be described using this distribution and the probability distribution function is 1 by 1 minus A exponential E by kT. So, it is 1 plus and 1 minus and here there is no 1. So, that is the difference and we will come back and look at Boltzmann distribution in this course in one of the modules to understand some of the simulations that are done and the calculations that are done based on these simulations in statistical thermodynamics and mechanics. So, I am going to stop this session here. So, we just mentioned a few more distributions. Three of them are very useful for doing data analysis, hypothesis testing, regression and things like that. Three of them are very important in statistical mechanics, classical and quantum statistical mechanics. So, because they are also probability distributions, so in this session we have also looked at them and we will continue with the probability distribution session. We are almost at the end of this probability distribution module and we still have not looked at uniform probability distribution which can be both discrete and continuous. So, that will be the last probability distribution we will look at before we conclude this session on probability distributions. Thank you.