 # Statistics-R Programming

Statistics is a branch of mathematics derived from the foundations of probability theory and calculus. It is, however, one of the most applicable to various non-mathematical fields. The main task of statistics is to collect, analyze and interpret data of any kind. Depending on its focus, statistics can be descriptive or inferential.

Descriptive statistics is, as you might presume, invaluable in describing a sample using so-called features to summarize the information present in data. The features frequently used for descriptive statistics are measures of central tendency (mean, median, mod) and measures of variability (standard deviation / variance, data range, skewness, etc). Sometimes the descriptions are not quantitative, but purely visual, using graphs such as pie charts or box plots. The latter is the reason why R programming language and statistics are often combined to get a more powerful tool for data analysis and interpretation.

On the other hand, inferential statistics focuses on the population, and seeks the underlying probability distribution. It starts by supposing a model, or a probability distribution function, and testing whether the observed sample fits the estimation. The assumptions made can be confirmed or disproven in many ways, some of them being point estimate, confidence interval, clustering, and so on. Since R is described as “a language and environment for statistical computing and graphics", what better choice to develop and test statistical models?

Statistics implemented in R programming language is nowadays becoming an increasingly popular choice for the introductory courses to many disciplines in demand, such as data science, engineering, bioinformatics, computer science, medicine, life sciences, etc. Some of the most frequent topics in courses covering statistics in R are listed here:

• Random variables: definition, programming examples in R
• Bayesian statistics
• Mayor continuous and discrete probability distribution functions (PDF): normal (Gaussian), uniform, exponential, Pascal, binomial, etc; cumulative distribution (density) function (CDF)
• Linear univariate and multivariate regression and modelling
• Frequentist and Bayesian inference: p-values and confidence intervals
• Model selection; information criterions
• Non-parametric statistics
• Statistical tests, such as ANOVA, Student's t-test, F-test, Chi-square test
• Analysis of variance
• Exploratory Data Analysis (EDA)
• High-dimensional data analysis (Singular value decomposition (SVD), Principal component analysis (PCA))
• Data visualization techniques
• Basic Machine Learning concepts

The experts on 24houranswers can provide you help in these or other related topics, since our tutors come from different fields where statistics using R is necessary, such as theoretical and applied mathematics, data science, electrical engineering, or computer science. Our website already had the opportunity to help many students who required assistance in this topic. To mention only a few, requests were ranging from basic tasks such as sampling from the probability distribution functions, discovering statistical inference and Kaplan-Meier estimator, to hypothesis testing in medical research and real-world applications in reliability engineering.

Due to wide applications and the rising trend of data-driven industries, the Internet is rich in resources concerning statistics using R programming language. Whether you are more of an audiovisual type and like to hear online courses, or you have a practical task in R, or maybe just want to follow the latest topics in the field, here are some places to start looking:

https://www.r-statistics.com/

https://www.r-bloggers.com/

http://rseek.org/

https://www.stat.berkeley.edu/~brill/Papers/EDASage.pdf

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#### To fulfill our tutoring mission of online education, our college homework help and online tutoring centers are standing by 24/7, ready to assist college students who need homework help with all aspects of Statistics-R programming. Our mathematics tutors can help with all your projects, large or small, and we challenge you to find better online Statistics-R programming tutoring anywhere.

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