This tag is for articles related to research and development
TUTORIAL: PYTHON for fitting Gaussian distribution on data
In this post, we will present a step-by-step tutorial on how to fit a Gaussian distribution curve on data by using Python programming language. This tutorial can be extended to fit other statistical distributions on data.
TUTORIAL: Visual Basic for Application (VBA) macro in Excel for Monte-Carlo Simulation
In this post, a hands-on tutorial of Visual Basic for Application (VBA) macro in Microsoft Excel for Monte-Carlo (MC) simulation is presented. MC simulation is a powerful tool to analyse and solve various scientific and engineering applications.
Common problems in numerical computation: from data overflow, rounding error, poor conditioning to memory leak
In many modern science and engineering fields, numerical computation has an essential role to perform meaningful analyses. In numerical computation, many unexpected results occur due to errors related to digital computation instead of errors due to logic or model or formula errors.
Standard deviation and standard error: The fundamental and important differences
In statistical data analysis, the spread of data is mostly quantified with standard deviation. However, for scientific analysis, standard error (SE) (commonly also called, standard deviation of the mean) is more appropriate to use than standard deviation.
Demystifying p-value in analysis of variance (ANOVA)
In analysis of variance (ANOVA), p-value is very often used to determine whether an initial hypothesis is accepted or rejected. However, many people still do not know what p-value is. In fact, there is a better parameter to use to accept to reject an initial hypothesis.
Continuous and discrete statistical distributions: Probability density/mass function, cumulative distribution function and the central limit theorem
In real world, all variables are random and the randomness is modelled by statistical distributions. In this post, various type of statistical distributions for both continuous and discrete random variables are explained.