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If you’ve tried sourcing for a top-quality, affordable, and legitimate R programming assignment help online, you definitely know how hard it is to land one, right? Getting an R programming expert who understands their craft and is willing to help genuinely may help you
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R programming is a purely statistical programming language designed to handle statistical computing and graphics. The open-source programming language is developed and managed by R Foundation for Statistical Computing.
Its environment is developed by Lucent Technologies, also known as Bell Laboratories, and is part of the GNU project that resembles S programming. Actually, the R programming language is considered the execution of the S programming language.
Over the recent past, R programming has gained a lot of popularity, especially in areas involving data mining, data analysis, and visualization, studying large databases, conducting online polls and surveys, etcetera.
R is a great programming language to start with, especially if you are a programming newbie—thanks to its simple syntax and the Tidyverse ecosystem that makes everything just seamless.
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As we have already seen, R programming has a wide range of applications. And that results from the many sub-topics that students must get conversant with to be proficient R programmers. At GradeWriters, our R programming assignment help experts understand all these concepts and can offer help across all R subsets. But what are these R programming subtopics?
Generally, regression in R programming and statistics involves predicting an unknown variable’s values from the known variable. In the R language, we obtain an equation relating the two variables—which is usually an equation of a straight line.
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Multiple Linear regression is considered an extension of Simple Linear Regression in R programming. Unlike SLR, which involves only one independent variable, MLR involves two or more independent variables and a single dependent variable—which is the variable we want to predict.
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Logistic regression is a predictive regression model used when we have a binary/dichotomous dependent variable in R programming. Logistic regression aims at explaining the relationship between the dependent binary variable with the other nominal independent variables.
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Bayesian statistics is a probabilistic data analysis method used to explain epistemological uncertainties—based on the Bayes theorem. In Bayes theorem, both the observed and unobserved elements have a joint probability distribution.
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Zero-truncated Poisson is also referred to as positive Poisson distribution, discrete probability distribution, or conditional Poisson distribution. It’s a regression model used to count data that doesn’t involve zero. Also, it doesn’t involve concepts such as model diagnostics, data cleaning, and verification, as well as potential follow-up analyses.
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Like every other programming language, R studio is where we write our code—the Integrated Development Environment (IDE) for the R language. It’s where statistical computing and graphics visualization happens. R studio runs on all operating systems and is connected to most modern browsers.
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