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.

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