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R is a language and environment developed by bell laboratories for statistical computing and graphics. In its group of operations, the statistical linear and non-linear model is also included. Technical computing and graphics are now more comfortable with R programming, dimensional needs, which is outlined omitting. Here are different graphical techniques that are offered by R, and are there are also extensible. This R language is easy to study and learn and accessible and used in different programs.
Assignment 2 Part 03 - Programming: 1. 2. Using the provided dataset on grades, repeat the steps on the slide deck about R (the Rprimer). You can also use your own dataset or the one on wages (but would have to adjust the variable names, obviously). 3. The slides show several distributions: binomial, Poisson, normal, Chi Squared, F, and the t distribution. Choose at least 3 distributions o Preferably in R, simulate data (e.g. 5000 draws) from a Random Variable that follows this distribution. o What is the Expectation and the Variance of your simulated variable? o Draw a histogram based on your simulated data. o Try to plot the distribution using qplot o As in the problem set, think: • What in the code or its output is the analogon of the random variable? Comment how you reach your conclusion. • What in the code or its output is the analogon of an outcome? Comment how you reach your conclusion. • What in the code or its output contain the analogon of a population/sample? Comment how you reach your conclusion. o Calculate the sample mean and the sample variance. • Compare them to the theoretical mean and variance that you programmed. Are they exactly the same? Why/Why not?
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