We provide effective and economically affordable training courses for R, Python and Statistics, click here for more details and course registration ! When a multiple linear regression model is estimated, the next step is usually checking the significance of each coefficient. As the arbitrary inclusion of an unnecessary variable into Read more…
In R programming, function aggregate() provides an easy way to calculate summary statistics of variables by specific groups in a data frame.
n this post, we show how to generate random numbers into vector and matrix in R programming, from various statistical distributions. Specifically, we focus several basic and widely used statistical distributions here, namely, normal distribution, continuous uniform distribution, binomial distribution and Poisson distribution.
Linear regression is widely used to model the relationship between response or dependent variable and explanatory or independent variables. The parameter in the model has linear form. When there is only one explanatory assumed in the model, it is called simple linear regression.
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.
When we do data analysis, random variables in the dataset are usually mutually correlated. Sometimes, we may want to measure the pure relationship between two variables, and the influence from other variables being controlled. A partial correlation calculation could fulfill this purpose.
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).
When a correlation, usually Person type correlation, is calculated, two variables have to be continuous. But this requirement does not excludes the situation when one of the two variables is a dichotomous (binary) distributed. Say if we want to measure the correlations between height and gender for a group of people, the variable gender has clear dichotomous values. This kind of Pearson correlation is called point-biserial correlation, because the value for gender variable is strictly 0 or 1.
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.
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.