An_Introduction_to_data_science_poisson_regression
                     Introduction to Poisson 
Regression
 It assumes that the data or output variable follows Poisson distribution
 Poisson distribution takes values from 0 to infinity
 We go for Poisson Regression when Variance = Mean = ƛ
 Output variable - Y is Count/Defect (Discrete)
 Input variable - X can take any value

Examples of Poisson Regression
Example 1. The number of persons killed by mule or horse kicks in the 
Prussian army per year. Ladislaus Bortkiewicz collected data from 20 
volumes of Preussischen Statistik. These data were collected on 10 
corps of the Prussian army in the late 1800s over the course of 20 years.
Example 2. The number of people in line in front of you at the grocery 
store. Predictors may include the number of items currently offered at a 
special discounted price and whether a special event (e.g., a holiday, a 
big sporting event) is three or fewer days away.
Example 3. The number of awards earned by students at one high 
school. Predictors of the number of awards earned include the type of 
program in which the student was enrolled (e.g., vocational, general or 
academic) and the score on their final exam in math.
Description of the data
In this example, num_awards is the outcome variable and indicates the
 number of awards earned by students at a high school in a year, math is a
 continuous predictor variable and represents students’ scores on their math
 final exam, and prog is a categorical predictor variable with three levels
 indicating the type of program in which the students were enrolled. It is
 coded as 1 = “General”, 2 = “Academic” and 3 = “Vocational”.
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