In this article we will have high-level overview of the process of data science. We will look at different stages of data science work. The process of solving a data science problem is summarized in the following figure. This is called as Data Science Road Map. The first step is always to frame the problem: …
Many statistical tests assume that the data is normally distributed. Hence if the underlying data is not normal, we need to transform a data to make it near normal before we apply these tests. The transformed data set removes skewness, mitigate biases and enhance robustness of statistical models. In this tutorial, we will use different …
How to remove skewness in the data? Learn 6 powerful data transformations. Read More »
The basic idea of inferential statistics is to use a statistic (mean,Standard Deviation etc.) calculated on a sample in order to estimate a parameter of a population (mean,Standard Deviation etc.). However, we have to accept an unavoidable fact that value we get for any statistic will vary from sample to sample, even when all samples …
In data analysis, we may obtain greater insight while expressing a variable in different form. For e.g. you could use different scale to better visualize the variables that have points close by. Many statistical tests assume that the data is normally distributed. Hence if the underlying data is not normal, we need to transform a …
There are range of techniques that you can use to check if your data sample deviates from a Gaussian distribution, called normality tests. In this tutorial, we will learn some techniques that can be used to check the normality of the data.
In this tutorial we will get comfortable with some of the commonly used terms from the field of Sampling theory The clarity on these terms is required to understand the inferential statistical techniques.
In this tutorial, we will have gentle introduction to normal distribution with real world example. We will generate normal distribution plot in R and learn some R functions to calculate the probabilities of normal distribution.
