In this post, you will learn the main concept of skewness, calculating the skewness in R and by hand, and its importance in the field of data analytics.

While studying, for example, the relationship between GDP and life expectancy, you might be interested to know whether there exists any relationship between the two indicators? is it a positive relationship or a negative relationship? and how strong the association is? These questions can be answered by computing the correlation coefficient between the two indicators. Depending on the type of data, different methods of correlation exist. In this post, you will learn the Pearson correlation coefficient and the Spearman correlation coefficient.

As the name suggests, the measures of dispersion show the extent of variability and the scattering of the data points. The main idea of the measures of dispersion is to get to know how the data are spread and how much the data points vary from the average value. There are mainly two types of measures of dispersion. 1) Absolute measures of dispersion 2) Relative measures of dispersion

The most common types of measure of central tendency are mean, median, and mode. Each of these measures shows the tendency of the data to clusture around a middle value using a different approach.

Box-plot is one of the effective ways to visually represent the distribution of data and it gives you an overall idea about how the data looks. And it is one of the best tools to identify the outliers to check if an association you find in your analysis can be explained by the presence of potential unusual observations. Through box plots we can find the minimum, lower quartile (25th percentile), median (50th percentile), upper quartile (75th percentile), and maximum of an continues variable.

In this post you will learn how to rename columns of a data frame in R.