Click here for online course registration ! R is a popular programming language, It is free and open source. R has been widely used in the field of statistical data analysis, econometrics, machine learning , just name a few in the last almost three decades. R is regarded as the easy-to-use and easy-to-learn statistics software, as well as its abundance of packages in almost all the areas in data science. In this course, we provide Read more…
In R programming, function aggregate() provides an easy way to calculate summary statistics of variables by specific groups in a data frame.
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.
Dictionary is a data structure type in Python. One reason for why Python is so popular among programmers is that dictionary provides an useful and effective way to store key-value pairs information. When functions in Python carry out some tasks, it can return required information to a dictionary.
In R programming, the value ‘NA’ is used to represent a missing value. Say we try to read a csv file from working directory and generate a data frame. There several places in the csv file have value ‘999’, which means missing value due to various circumstances during data survey and collection.
In data analysis it is often needed to set new values to a variable based on one or several conditions, and these kinds of operations are called recode variables. The most frequently applied recoding variables in R may be the setting some values to missing values (NA), and recoding a continuous to the values of a categorical variable.
R has rich resources of functions dealing with date values in data analysis. In this post, we introduce and show how to use as.Date() function for working with data values in R. as.Date() accepts a string input with specific format and transform the value to a date object in R.
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.
It is not uncommon to generate random sequences in R programming. sample() function provides the feasibility of generating such random objects from given vectors, either with or without replacement. The following code shows an example that 32 numbers with replacement drawn from 50 integers from 1 to 50.
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.