Empowering Computer and Statistics
In most cases, there are complex and specific algorithms for abnormal or anomaly detection that have been implemented through various software in statistical or data analysis. However, in this article from the Arman Computer magazine, we aim to discuss the application of sorting and identifying abnormal observations, which, in addition to being simple, is also […]
The correlation coefficient or more precisely, the Pearson correlation coefficient, is a bivariate statistical descriptive statistics that examines the linear dependency between two vectors of observations. Given the range [1,-1] that the value of the Pearson correlation coefficient can have, it is very close to the value of trigonometric functions, especially the cosine function. Therefore, […]
In other articles from Arman Computer Magazine, we have examined how to calculate the average and weighted mean. But in this text, we want to discover the average or mean formula and learn how to derive its formula. I try to mention the formula syntax less, but sometimes it is necessary to use the syntax […]
“Coefficient of Variation” (CV) is suitable for comparing the dispersion (Variation) of two groups of data collected with different measurement units. In this statistical index, the ratio of dispersion to a central criterion is measured. In fact, this index shows how much the standard deviation of the values changes per unit of change in the […]
Regression methods model the relationship between independent and dependent variables. Linear regression models are widely used; however, they may not be suitable when the relationship between variables isn’t linear. Polynomial regression is an alternative that models the relationship of the dependent variable as a polynomial function of the independent variable. This post (from Armancomputer blog) […]
Descriptive statistics indices, such as Central Tendency indices or dispersion measures, are used to identify the statistical population. But using each one alone may not have a good result. Simultaneous attention to the criteria of concentration and dispersion helps the statistical population to be better known and its behavior can be compared and analyzed. they […]
In the previous article entitled Statistical Distribution and Its Density Plot Shiny Application – Part 1, we learned how to draw the graph of the density function of a distribution by generating random numbers from that distribution. In this article, we will solve the disadvantages and problems that existed in the previous program and provide […]
In the previous post entitled Random Numbers, we mentioned how to generate random numbers from the normal distribution in the R programming language showing statistical distribution and its density plot. In this post, with the help of the Shiny library in R programming language, we will get the desired distribution type and parameter from the […]
The statistical approach to random numbers is very particular. In gambling and games of chance, random numbers have the main role. So, in a sequence of posts in Armancomputer magazine, I will discuss probability distribution and introduce some commands in R language, to generate random numbers from the main statistical distributions such as Normal, Uniform, […]
Random numbers in computer data simulation and statistical distributions play an important role. Although we know that the numbers generated by computer algorithms are pseudo-random (No real Random), but in most cases we use these techniques to create random numbers in the range [a,b] (from a to b). In this article of Arman Computer magazine, we […]
