Applied Statistics with R paves the way for developing new methods for interactive data analysis. R is getting popular in the market as it is serving as a better tool for statistical analysis. Using R, you can perform software development and Data Analysis. R is highly flexible as it is dependent on former computer language. Join the course to learn and analyze statistics in the context of real-world problems.
Applied Statistics with R allows the students to get started on their applied statistical problems.
A statistical program which is available on the internet is referred as R and provides an environment for performing statistical computing and producing graphics. The letter "R" refers to two things programming language and software environment for statistical computing, which is free and open-source.
The advantage of R is the support for Graphical User Interface (GUI) that makes interactions easier thought it is a programming language. R also allows you to copy and paste text from other applications. R web page provides a library of basic commands. You can copy and paste using these commands into R for performing a variety of statistical analysis.
Applied Statistics with R paves the way for developing new methods for interactive data analysis.
R is a very easy programming language and allows to create a high-quality analysis. R is very flexible, and the vast ecosystem is the strongest feature in R. R supports excellent graphics and charting capabilities. It has grown rapidly and supports an extensive collection of packages.
The instructions for obtaining R largely depend on the user's hardware and operating system. There are base packages that come with R automatically and contributed packages that you need to download for installation.
This chapter introduces you to R, the programming language which is a free and open source for statistical computing and graphics. This session will teach you to download R, the R studio script and setup the R environment required for learning. R supports several basic data structures. A data structure is either homogeneous in which all elements are of the same data type or heterogeneous in which elements can be of more than one data type. Numeric, Integer, Complex and Logical are the data types available in R.
The R environment supports graphical facility for displaying a large variety of statistical graphs and build new types of graph. You can use these graphical facilities in both interactive and batch modes. This lesson teaches you about the Plotting commands in R which are of three types namely High-level plotting functions to create new plot on the graphic devices, low-level plotting function to include more information to an already existing plot and Interactive graphics to interactively add or extract information from an existing plot.
This session explains about the R commands and operations. Objects are the entities on which R operates. Multiple collections of data entries are called an Array and R allows simple and easy methods to create and handle multiple arrays. The named data structures used in R is the vector, and it is an entity that consists of ordered collection of numbers.
This chapter teaches you how to create objects of mode function. R functions get stored in a special internal form for using them in expressions. R is a powerful and convenient language that allows you to learn and write useful functions which result in a comfortable and productive development. Many functions like mean(), var() and postscript() are available in R themselves, and hence they do not differ from user written functions.
This section explains the theory of power calculations and sample size choice. Based on the null hypothesis value, we conclude decisions about the acceptance and rejection regions. This decision allows you to identify the probability of test statistic which falls into rejection region. The tests described here allows you to find out the degrees of freedom of the distribution in the null hypothesis
The concept of models is applicable for explaining relationships and predict observations. Small and interpretable models help in explaining and models which make most minor errors possible without overfitting helps for predict. Both the feature mentioned above is made possible by Linear models.
Probabilistic statements are derived based on the distribution. R supports many functions for determining the density, distribution and random values. A set of statistical tables is available in R which help to calculate the cumulative distribution function, the probability density function, and the quantile function.
R provides an interlocking suite of facilities which make statistical models very simple. The most basic statically tests include comparing continuous data either between two groups or against a previously stipulated value. This lesson teaches you how to perform the t-test, and these t-tests assume that the data comes from normal distribution.
This chapter shows how to perform regression analysis. Regression analysis, in general, includes plots for model checking, confidence and prediction intervals display. This lesson also explains about the correlation in both parametric and non-parametric variants.
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