Doing and discovering statistics using Python programming.
Python functions handling calculating the density, cumulative probability, quantile and random number generation for different statistical distributions: Normal distribution, t distribution, gamma distribution, chi-square distribution, F distribution, beta distribution, Hypothesis testing, etc.
Linear regression and Generalized linear models using Python programming.
ANOVA, factor analysis using Python programming.
Clustering model using Python programming.
We provide effective and economically affordable online training courses for R and Python, click here for more details and course registration ! Binomial distribution in statistics is a type of discrete probability distribution, modeling the probability of success times among a predetermined Bernoulli process. A Bernoulli process is a binary Read more…
We provide effective and economically affordable training courses for R and Python, click here for more details and course registration ! Mean, median and mode are three statistics that measure the central tendency of numerical data. Mean is the average of sample, and median the mid point of the sample 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).
Poisson distribution is a discrete distribution. It is frequently used to model the counts of event occurrence during a specified time interval, such as telephone calls coming in to a call center in a given day. There is one parameter in the Poisson probability function, λ, which denotes the constant occurring rate in a Poisson process.
In hypothesis testing, the possibility of the other side than the conclusion usually exists, and the analysis commits so-called Type I and Type II errors, with respect to the truth and the decision made upon the random sample and hypotheses. In particular, a Type I error measures the probability that a true Null hypothesis (H0) is incorrectly rejected, and a Type II error says the probability that a false H0 not being rejected, respectively.