However an important thing to learn about these distributions is that they represent a model. A model is nothing but a function which says if you give me “x” input, i’ll give you “y” output. If a change in x changes y linearly it is linear model or function. If it doesn’t, it’s non linear.
For example:
y= 3x+2, y= 3 and y= 3/4 x+7 are linear, whereas y= x², y= sqrt(x²+y²), y=x??? or y= log(x) are non linear.
Important: If you pick a linear function from above and generate a 1000 random numbers to replace x one by one for each number, you’ll get a linear plot like the one displayed above. Similarly If you pick a non linear function you’ll get something like below. Where x axis represents the random numbers you have generated to insert as input to the function, and y represents the output of the function.