Variance and Standard Deviation are the most commonly used measures of variability and spread. Variability and spread are nothing but the process to know how much data is being varying from the mean point.

# Tag: Data Science

## Step by Step Approach to Principal Component Analysis using Python

Principal Component Analysis or PCA is used for dimensionality reduction of the large data set. Using PCA we can speed-up the ML algorithms by reducing the feature spaces.

## What is Logistic Regression?

Logistic regression is used for binary classification problem which has only two classes to predict. However with little extension and some human brain, it can easily be used for multi class classification problem. In this post I will be explaining about binary classification. I will also explain about the reason behind maximizing log likelihood function.

## Basic Statistics for Data Science – Part 1

Types of Statistics: Descriptive vs Inferential

Basic terminology like Population vs Sample

Types of Variables: Numerical vs Categorical

Measures of central tendencies: Mean, Median and Mode and their specific use cases

Measures of dispersion/spread: Variance, standard deviation etc.

## What is the Coefficient of Determination | R Square

The Coefficient of Determination is the measure of the variance in response variable ‘y’ that can be predicted using predictor variable ‘x’. It is the most common way to measure the strength of the model.

## What is Linear Regression? Part:1

Linear Regression is a field of study which emphasizes on the statistical relationship between two continuous variables known as Predictor and Response variables. Predictor variable is most often denoted as x and also known as Independent variable. Response variable is most often denoted as y and also known as Dependent variable.

## Covariance and Correlation

Covariance and Correlation are very helpful while understanding the relationship between two continuous variables. Covariance tells whether both variables vary in same direction (positive covariance) or in opposite direction (negative covariance). Whereas Correlation explains about the change in one variable leads how much proportion change in second variable.