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Among many such debates, one that has heated up a lot in the past few years is R vs SAS. SAS used to be the king of the conversation .
R language is the world’s most widely used programming language for statistical analysis, predictive modeling and data science. It’s popularity is claimed in many recent surveys and studies. R programming language is getting powerful day by day as number of supported packages grows. Some of big IT companies such as Microsoft and IBM have also… Read More »100 Free Tutorials for learning R
Linear models are a very simple statistical techniques and is often (if not always) a useful start for more complex analysis. It is however not so straightforward to understand what the regression coefficient means even in the most simple case when there are no interactions in the model. If we are not only fishing for…
Understanding one of the most important types of data analysis.
Other Levels of Confidence Understanding the General Structure of Confidence Intervals CO-4: Distinguish among different measurement scales, choose the appropriate descriptive and inferential statistical methods based on these distinctions, and interpret […]
“What are the best books about Statistics?” We looked at 145 of the top Statistics books, aggregating and ranking them so we could answer that
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Extension of ggplot2, ggstatsplot creates graphics with details from statistical tests included in the plots themselves. It provides an easier syntax to generate information-rich plots for statistical analysis of continuous (violin plots, scatterplots, histograms, dot plots, dot-and-whisker plots) or categorical (pie and bar charts) data. Currently, it supports the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, meta-analysis, and regression analyses. References: Patil (2021) .
A boxplot. It can tell you about your outliers and what their values are. It can also tell you if your data is symmetrical, how tightly your data is grouped, and if and how your data is skewed.
Learn how Bayesian Statistics can help you solve business problems with data analysis. Enhance your skills in machine learning & data science
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Source: here In my previous article here, I discussed a simple way to solve complex optimization problems in machine learning. This was illustrated in the case of complex dynamical systems, involving non-linear equations in infinite dimensions, known as functional equations. These equations were solved using a fixed point algorithm, of which the Newton–Raphson method is a… Read More »Hurwitz-Riemann Zeta And Other Special Probability Distributions
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