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Statistics usually includes:
statistical distribution: normal distribution, t distribution, gamma distribution, chi-square distribution, F distribution;
statistical modeling: Linear regression, Logistic regression, Generalized linear model, etc;
ANOVA, factor analysis, etc.
Focus on both correlation and causal relation analysis aomong variables and factors.
Using both R and Python to implement statistical programming.
Using ggolot2 in R and matplotlib in Python specially for plotting various statisticl figures.
We provide effective and economically affordable online training courses for R and Python, click here for more details and course registration ! In linear regression analysis, one may be very interested in relative importance of independent variables, that is, which variable contributes most in explaining the variation of response variable. Read more…
We provide effective and economically affordable online training courses for R and Python, click here for more details and course registration ! Exponential distribution is a continuous distribution that models random time between events (until next event occurs) during a Poisson process, which is a process with constant occurrence rate Read more…
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…
We provide effective and economically affordable online training courses for R and Python, click here for more details and course registration ! Continuous Uniform distribution is used to model a flat probability existence with a range (A,B). Its probability density can be depicted as The follow figure shows probability density Read more…
We provide effective and economically affordable online training courses for R and Python, click here for more details and course registration ! Poisson distribution is used to model the random number (x) of event occurrence during a Poisson process, which focus on the occurrence of same random events during a Read more…
We provide effective and economically affordable online training courses for R and Python, click here for more details and course registration ! Geometric distribution is to model the random trial number X until a first success, in Bernoulli trial experiments, with constant success rate p. The probability of x can Read more…
We provide effective and economically affordable online training courses for R and Python, click here for more details and course registration ! Negative binomial distribution is used to model the particular probability of k-th success occurring at x-th Bernoulli trial, which is equivalent to say that it has experienced x-1 Read more…
We provide effective and economically affordable online training courses for R and Python, click here for more details and course registration ! Hypergeometric distribution is connected with taking n items (no replacement) from N items, where k items labeled success and N-k items failure. The success number x of n Read more…
We provide effective and economically affordable online training courses for R and Python, click here for more details and course registration ! Binomial distribution is a discrete random distribution modeling the probability of event success number x in N Bernoulli trial. A Bernoulli trial is an experiment of binary (success/fail) Read more…
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…