Missing data is one of the most common problems in almost all statistical analyses. If the data is not available for all the observations of variables in the model, then it is a case of ‘missing data’.
Markov chain and its use in solving real world problems
Markov chain is one of the most important tests in order to deal with independent trials processes. There are two major principal theorems for these processes. The first one is the ‘Law of Large Numbers’ and the second one is the ‘Central Limit Theorem’.
How to perform bootstrap and jackknife analysis?
Bootstrap and jackknife are superficially similar statistical techniques that involve re-sampling the data. They are nonparametric and specific resampling techniques that can estimate standard errors and confidence intervals of a population parameter.
How to perform cross validation on a data set?
Thus to assess the model, a common practice in data science is to iterate over various models and select the most appropriate model. In other words it is important to test the same model with different values of parameters.This is called the cross validation method.
How to perform and apply Monte Carlo simulation?
Monte Carlo simulation is an extension of statistical analysis where simulated data is produced. This method uses repeated sampling techniques to generate simulated data.