# Statistics

## Making Predictions with Linear Regression

In this post you will learn how to build a linear regression, interpret the result, test its assumptions, and use the regression equation for predictions.

## Methods for Transforming Data to Normal Distribution

In this post, I try to cover the most common methods of transforming a skewed distribution into a normal distribution, and the foundational step that you must consider prior deciding which method to apply.

## Skewness | Definition and its Importance in Data Science

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.

## Analysis of the Relationship Between Two Quantitative Variables | Correlation

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.

## Measures of Dispersion

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

## What is Central Tendency?

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.