This essay focuses on application areas of Poisson regression. we can therefore use Poisson regression to predict the number of incidents

application areas of Poisson regression

In their book Generalized Linear Models (New York: Chapman & Hall, 1983), the authors P. McCullagh and J.A. Nelder used the Poisson regression to study the ship dataset. Poisson regression is a special case of the generalised linear models in which the target variable, or dependent variable, is Poisson distributed. Since one of the main application areas of Poisson regression is to fit linear models on count data, we can therefore use Poisson regression to predict the number of incidents (which are also counts) given some input variables.

Mathematically, Poisson regression is a linear . It is model in which the expected value of the target variable Y is calculated. This is  by where β0 is the intercept, β1, β2, … , βk are the coefficients. It is  of the independent variables X1, X2, …, Xk. E(Y) is the predicted, or. This  expected value of Y, which will be transformed by the natural logarithm function. (a)

Find the corresponding scikit-learn module in the official website. It is  of scikit-learn and discuss the corresponding module, estimator. This  fit and predict functions, as well as their parameters in your own words. (10 marks) (b) Analyse the data by fitting a Poisson regression based on the DataFrames X and Y generated in Question 1. Follow the instruction in the official website and report the parameters of the estimated model. Create a Python program to fit a Poisson regression and generate a table or a DataFrame to present the coefficients with the corresponding labels.

Details;

Firstly, be see

secondly, integrity

thirdly, be cautious

further, be fast

further, report

lastly, passion

Lastly, base