Statistics and Probability

In my last article we learned how to estimate population mean by using sample mean when population standard deviation is known. In most instances, the population standard deviation will be unknown and thus business analyst won’t able to estimate the population mean. When the population standard deviation is unknown, the sample standard deviation must be …

What is t-distribution? – An intuitive understanding using business example Read More »

What is point estimation and what is its fundamental drawback? The use of single sample value such as X_bar (sample mean value) to estimate the population value is known as point estimation, because single value X_bar represent one point on the real number line. For e.g. if we are trying to calculate mean height of …

What is the difference between Point estimation and interval estimation? Read More »

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 …

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.