In this post, I want to talk about **Heteroscedasticity**. I can understand, some of you might have a hard time pronouncing it (at-least I did). Anyway, we are more interested in its meaning and it’s application rather than its pronunciation.

So, **Heteroscedasticity** means the variability of a random disturbance is different across a vector. Now, this is a complex definition. Let me explain in terms of regression, it refers to a situation where **the variance of error is not uniform** across the measured values.

Let’s understand it with the help of an example. Let’s take the case of **income versus expenditure on meals**. As one’s income increases, the variability of food consumption will increase. A poorer person will spend a rather constant amount by always eating inexpensive food; a wealthier person may occasionally buy inexpensive food and at other times eat expensive meals. Those with higher incomes display a greater variability of food consumption. The below attached image visualises the concept of **Hetroscedasticity**.

Many statistical techniques like **Regression**, **Analysis of Variance** (**ANOVA**) etc. assume a **homoscedastic** behaviour of **residuals**. Violation of such assumptions can lead to inaccuracies in the statistical inference. One such regression technique is called **Linear Regression** which also assume a linear relationship between dependent and independent variable. To know more about Linear Regression, please visit **here**.