We provide effective and economically affordable online training courses for R and Python, click here for more details and course registration ! Normal distribution is a continuous random variable distribution with bell-shaped probability density curve. It is widely used in statistical data analysis, and the basis for many other distributions Read more…
The exponential distribution is modeling the probability distribution of the random time until next event occur in a Poisson event process. A Poisson event process has a constant occurring rate during an interval. Exponential distribution is a particular case of the gamma distribution when the shape parameter (alpha) equals 1.
Normal distribution is describing random variables with bell-shaped probability density functions. Normal distribution is widely used in data science because large sample random variates have a mean value which follows approximate normal distribution if variates are independently drawn from any distributions. The probability density function for normal distribution is determined by two parameters: mean(miu) and standard deviation(sigma).
A Student t-distributed random variable is modeling the ratio between a standard Normal random variate and square root of a Chi-squared random variable divided by its degrees of freedom.
Kernel density function is a nonparametric method to find the drawing density curve of random samples, and it is often used to draw a smoothed curve in data visualization. In R programming with ggplot2 package, a chaining of functions ggplot() and geom_density() is often used to draw different smoothed curves showing the distribution of continuous variables.
Weibull distribution, named after Swedish mathematician Waloddi Weibull, is a continuous distribution which is widely used to model the distribution of random time between events. Exponential distribution, which is used to model the random time until next event occurs and have so-called memoryless feature or constant failure rate. In order to relax this memoryless condition, analysts may use either Gamma distribution or Weibull distribution instead.
Lognormal distribution in probability and statistics is used to model the distribution of a positive random variable Y, if Y = ln(X) has a normal distribution with mean μ and standard deviation σ.
Beta distribution is a family of distributions which are used to model the probability of continuous random variables defined on [0, 1]. There are two parameters , α and β in Beta distribution. A continuous uniform distribution defined on [0, 1] is actually a special case of a beta distribution, when both α and β equal 1.