### Role of Generalised Linear Model in Non Life Pricing - Phase1

We will cover a series of topics relating to how Non Life Pricing is done through GLM.

But first let's see what is GLM

Let’s take the example of Weight (Y) and Height (X). The aim of linear models is to find the line of best fit through the data points.

Here is your X axis is Height and Y axis is your Weight. Y = B0 + B1x

Line of Best Fit is B0 + B1x where B0 is intercept on Y axis and B1 is the gradient.

Now the question is how that line comes?

Well, line is chosen in such a way to minimize the sum of squared error terms where error terms are distances from data points to straight line, error terms are normally distributed with mean 0 and variance σ2.

2.

We can extend our model to allow for other predictive variables. For example, we can decide that Weight can depend on height and calories consumed per day both. So here we cannot find the line…

But first let's see what is GLM

**Generalised Linear Model**Before Jumping on to what is GLM, let’s see what is**Linear models**1.**:***Linear Models*Let’s take the example of Weight (Y) and Height (X). The aim of linear models is to find the line of best fit through the data points.

Here is your X axis is Height and Y axis is your Weight. Y = B0 + B1x

Line of Best Fit is B0 + B1x where B0 is intercept on Y axis and B1 is the gradient.

Now the question is how that line comes?

Well, line is chosen in such a way to minimize the sum of squared error terms where error terms are distances from data points to straight line, error terms are normally distributed with mean 0 and variance σ2.

2.

**:***Multiple Linear Regression*We can extend our model to allow for other predictive variables. For example, we can decide that Weight can depend on height and calories consumed per day both. So here we cannot find the line…