is your data lying to you? how to detect hidden non-linearity

hey data folks! are you sure your data is linear? think again…….

we often assume that relationships between variables are straightforward, but what if they aren’t ? what if hidden patterns are distorting your models, making predictions unreliable? non-linearity is everywhere: in finance, cybersecurity, ai, and even your daily analytics.

detecting non-linearity isn’t just a technical skill, it’s the key to unlocking better predictions, reducing errors, and gaining a serious competitive edge. in this guide, we’ll break down how to spot non-linearity and why it matters

linear relationship between variables:

a non-linear relationship between variables means that the model cannot accurately represent the relationship with a simple straight line.

a linear relationship can be expressed in the form of a straight-line equation in cartesian space:

examples of linear equations:

• y=ax+b

non-linear relationship between variables:

non-linear relationships model phenomena where the effect of one variable depends on its value or the value of other variables.