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When we talk about regression, the first things that come to our mind are linear or logistic regression and somewhere in the distant back of the mind polynomial regression. Linear and logistic regression are 2 of the most popular types of regression methods. However, there are many different types of regression methods which can prove to be useful in different scenarios. Today we will be looking at Spline Regression using Step Functions.

Spline Regression is a non-parametric regression technique. This regression technique divides the datasets into bins at intervals or points called knots and each bin has its separate fit…

An attempt to explain the estimation process in statistics in the simplest form.

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Statistics, as we know, is the study of gathering data, summarizing & visualizing the data, identifying patterns, differences, limitations and inconsistencies and extrapolating information regarding the population from a sample.

This process of extrapolating information regarding the population from a sample is called Estimation. As it’s impossible to collect information from every single member of the population we instead gather information from a sample and work our way to estimate the information about the population. And in this process, we use something called an “Estimator” to generate the estimation.

When a value is calculated for the entire population it’s called…

An attempt to explain covariance and correlation in the simplest form.

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Analyzing and visualizing variables one at a time is not enough. To make various conclusions and analyses when performing exploratory data analysis, we need to understand how the variables in a dataset interact with respect to each other. There are numerous ways to analyze this relationship visually, one of the most common methods is the use of popular scatterplots. But scatterplots come with certain limitations which we will see in the later sections. Quantitatively, covariance and correlations are used to define the relationship between variables.


A scatterplot is one of the most common visual forms when it comes to comprehending…

An attempt to break down probability density functions to the most basic principles

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I just recently decided to try out Twitter for talking about data science topics. My aim is to start with statistics and move on to more complex topics in data science, as I learn along, and explain those topics succinctly. Following are some of my tweet threads on Probability distribution functions compiled here.

Probability Mass Functions or PMF:

  • Probability is the frequency expressed in fraction of the sample size ’n’. In order to derive probabilities from frequency, you divide the frequencies with ’n’. This process is called normalization.
  • Probability mass function (PMF) maps each value to its corresponding probability. PMF is plotted for discrete…

Wow, you have blatantly copied my article ( https://towardsdatascience.com/unraveling-spline-regression-in-r-937626bc3d96 ) and various other without even providing any citations! Way to go!

Trisha Chandra

Analytics & Data Science. Lifelong Learner. Love to learn new things and enjoy spreading that knowledge.

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