Doing and discovering statistics using R programming.
R 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, etc.
Linear regression and Generalized linear models using R programming.
Discrete choice modeling using R programming.
ANOVA, factor analysis using R programming.
Clustering model using R programming.
We provide effective and economically affordable training courses for R and Python, click here for more details and course registration ! R provides rich availability of using probability functions. Probability functions in R takes the form [dpqr][prob], Where [dpqr] represent which kind of variates the function works on d – Read more…
rdatacode.com provides online training course for R and Python, click here for more info ! In simulation and Bayesian statistics, it is often needed to draw data coming from multivariate variables. R provides a function draw.d.variate.dist() from package ‘MultiRNG’ for such purpose. For example, draw.d.variate.normal() are used to draw multivariate Read more…
The normality assumption in linear regression is necessary to ensure the estimates of parameters are unbiased and the hypothesis testing is correct. It states that for the fixed or given values of explanatory variables, the dependent variables are normally distributed around the mean 0. It is equivalent to say that the residuals after model estimation follow a normal distribution with the mean 0.
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.
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.
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.
In hypothesis testing, the analyst has chance to commit both Type I and Type II errors. The Type I error (α) refers to the probability of wrongly rejecting a true Null hypothesis – H0, while the Type II error (ß) represents the probability that failing to reject a false H0. The value of 1- ß is called the Power of Test in hypothesis testing. Its value says the ability of correctly rejecting a false H0, under the specified Null hypothesis – H0 and Alternative hypothesis – H1.
In statistical hypothesis testing, there are usually two types of errors that the process will encounter, namely Type I and type II errors. Type I error (α) refers to the probability of rejection of a Null Hypothesis (H0) when actually it is true, and if a false Null hypothesis is missed to reject when an Alternative Hypothesis (H1) is true, then a type II error (ß) occurs.