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Introduction to analytics and statistics in R

Nowadays business decisions heavily rely on such a great and powerful tool as statistics. No quality analytics is possible without it. This course will give you the basis how the appropriate use of obtained knowledge from different spheres of life can lead to deep insightful conclusions.

Every decision is based on a big amount of inferences, but of course not every inference is important. That is why analysis of all available information becomes the art. And the end of each masterpiece of this art is the ability to present your findings – presentation and visualization, which we also take a look at.

Programming is indispensable part of the modern world. You will see that R programming language allows you to make your data analysis easy, fast, fun, beautiful and creative. More then, R is free. The use of it becomes more popular because of the many reasons, which we will also investigate.

Next topics will be considered in the course:

1.       Advantages of using R in statistical research.

2.       Data. Variables and observations. Data matrix, frequency table, cumulative frequency table, contingency table. Numeric analysis of univariate data (measures of central tendency, spread and shape). R language. RStudio. Some types of objects in R (vectors, matrices, factors, lists, data frames, functions). Receiving help in R. R packages.

3.       Important discrete and continuous probability distributions, its means and variances (Bernoulli, binomial, hypergeometric, Poisson, normal, chi-squared, t-distribution, F, exponential, gamma). Working with probability distributions in R. Visual investigation of univariate data in R (empirical cumulative distribution function, histogram, density plot, boxplot, quantile graphs). Reading data into R.

4.       Inferential statistics (estimators, confidence intervals, hypothesis testing). T test.

5.       Basic visualization techniques and tips.

6.       Simple linear regression analysis.

7.       Definition of stationarity.

The course will consist of 7 lectures and 7 tutorials. It concludes with an exam – multiple choice questions test and practical assignment at computer. Besides students must complete the home assignment. More information you can receive at the introduction lecture or per email.

The introduction lecture is from 9am till 11am on the 11th of April.

The room is GD 203.

The first lecture is from 11am till 1pm on the 11th of April.