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 …

An intuitive understanding of Sampling distribution and central limit theorem using practical example Read More »

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 …

A brief introduction to Linear Transformation Read More »

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.

In this tutorial we will look at Poisson distribution characteristics, build Poisson distribution formula and look at some R functions to calculate the probability of occurrence using Poisson distribution.

In this tutorial, we will understand the assumptions of binomial distribution, take a business example of binomial distribution, build the binomial distribution formula and use R to solve the problem.

In this tutorial we will look at two measures of relationship between two numeric variables: the covariance and coefficient of correlation.

More commonly used dispersion measures in statistics are variance and standard deviation. These measures give summary statistics, hence does not tell much about the overall data. A five number summary is elaborate measure to check the overall data and its spread.

Descriptive statistics is used to describe the data. A first step in this process is to check the distribution of values of each numeric variable. In this tutorial, various tools for describing the variable such as distributions, tabulations and graphical representation are discussed.