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R-Programming is a language and condition created for statistical computing and graphics developed by Bell Laboratories prominently known as AT&T. The measurable direct and non-liner model is likewise included for its gathering of statistical assignments. Benefiting R programming homework help in Australia from the specialists is valuable to find out about R-language and programming alongside its uses.

R is a GNU venture that is like the S language and condition which was created at Bell Laboratories, presently known as Lucent Technologies, by John Chambers during the 80s. R can be estimated as an alternate execution of S. There are some huge contrasts between the two, yet the greater part of the code is composed for S language likewise run under R consummately.

Simple Linear Regression |
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Logistic Regression |
Multivariate analysis |

Time series |
Econometrics |

Non-parametric tests |
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Probability Theory |
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Sampling |
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Scatter plot |

Biostatistics |
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Statistical process control |
Parametric tests |

Markov chains analysis |
Central Limit Theorem |

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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). 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.

- What is the Expectation and the Variance of your simulated variable?
- Draw a histogram based on your simulated data.
- Try to plot the distribution using qplot
- As in the problem set, think:
- What is the code or its output is the analogon of the random variable? Comment how you reach your conclusion.
- What is the code or its output is the analogon of an outcome? Comment how you reach your conclusion.
- What is the code or its output contains the analogon of a population/sample? Comment how you reach your conclusion.
- 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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