Linearization and Gaussian

<p>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 &ldquo;x&rdquo; input, i&rsquo;ll give you &ldquo;y&rdquo; output. If a change in x changes y linearly it is linear model or function. If it doesn&rsquo;t, it&rsquo;s non linear.</p> <p>For example:</p> <p><strong>y= 3x+2</strong>,&nbsp;<strong>y= 3</strong>&nbsp;and&nbsp;<strong>y= 3/4 x+7</strong>&nbsp;are linear, whereas&nbsp;<strong>y=&nbsp;<em>x&sup2;</em></strong>,&nbsp;<strong>y= sqrt(x&sup2;+y&sup2;)</strong>,&nbsp;<strong>y=x⁵</strong>&nbsp;or&nbsp;<strong>y= log(x)</strong>&nbsp;are non linear.</p> <p>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&rsquo;ll get a linear plot like the one displayed above. Similarly If you pick a non linear function you&rsquo;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.</p> <p><a href="https://medium.com/@er.ujjwalsaxena/linearization-and-gaussian-700ab40fe900"><strong>Click Here</strong></a></p>