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:
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:
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