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0.7.5 (15 February 2019 ... Stata: StataCorp LLC: 18.5 (20 April 2024 ... "A Short Preview of Free Statistical Software Packages for Teaching ...
In statistics, standardized (regression) coefficients, also called beta coefficients or beta weights, are the estimates resulting from a regression analysis where the underlying data have been standardized so that the variances of dependent and independent variables are equal to 1. [1]
There are a few reviews of free statistical software. There were two reviews in journals (but not peer reviewed), one by Zhu and Kuljaca [26] and another article by Grant that included mainly a brief review of R. [27] Zhu and Kuljaca outlined some useful characteristics of software, such as ease of use, having a number of statistical procedures and ability to develop new procedures.
Weighted least squares (WLS), also known as weighted linear regression, [1] [2] is a generalization of ordinary least squares and linear regression in which knowledge of the unequal variance of observations (heteroscedasticity) is incorporated into the regression.
Stata utilizes integer storage types which occupy only one or two bytes rather than four, and single-precision (4 bytes) rather than double-precision (8 bytes) is the default for floating-point numbers. Stata's proprietary output language is known as SMCL, which stands for Stata Markup and Control Language and is pronounced "smickle". [10]
The weighted arithmetic mean is similar to an ordinary arithmetic mean (the most common type of average), except that instead of each of the data points contributing equally to the final average, some data points contribute more than others.
PSPP – A free software alternative to IBM SPSS Statistics; R – free implementation of the S (programming language) Programming with Big Data in R (pbdR) – a series of R packages enhanced by SPMD parallelism for big data analysis; R Commander – GUI interface for R; Rattle GUI – GUI interface for R
Kernel density estimation of 100 normally distributed random numbers using different smoothing bandwidths.. In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights.