Hits: 86

## (R Tutorials for Citizen Data Scientist)

## Statistics with R for Business Analysts – Poisson Regression

Poisson Regression involves regression models in which the response variable is in the form of counts and not fractional numbers. For example, the count of number of births or number of wins in a football match series. Also the values of the response variables follow a Poisson distribution.

The general mathematical equation for Poisson regression is −

log(y) = a + b1x1 + b2x2 + bnxn.....

Following is the description of the parameters used −

**y**is the response variable.**a**and**b**are the numeric coefficients.**x**is the predictor variable.

The function used to create the Poisson regression model is the **glm()** function.

### Syntax

The basic syntax for **glm()** function in Poisson regression is −

glm(formula,data,family)

Following is the description of the parameters used in above functions −

**formula**is the symbol presenting the relationship between the variables.**data**is the data set giving the values of these variables.**family**is R object to specify the details of the model. It’s value is ‘Poisson’ for Logistic Regression.

### Example

We have the in-built data set “warpbreaks” which describes the effect of wool type (A or B) and tension (low, medium or high) on the number of warp breaks per loom. Let’s consider “breaks” as the response variable which is a count of number of breaks. The wool “type” and “tension” are taken as predictor variables.

**Input Data**

input <- warpbreaks print(head(input))

When we execute the above code, it produces the following result −

breaks wool tension 1 26 A L 2 30 A L 3 54 A L 4 25 A L 5 70 A L 6 52 A L

## Create Regression Model

output <-glm(formula = breaks ~ wool+tension, data = warpbreaks, family = poisson) print(summary(output))

When we execute the above code, it produces the following result −

Call: glm(formula = breaks ~ wool + tension, family = poisson, data = warpbreaks) Deviance Residuals: Min 1Q Median 3Q Max -3.6871 -1.6503 -0.4269 1.1902 4.2616 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 3.69196 0.04541 81.302 < 2e-16 *** woolB -0.20599 0.05157 -3.994 6.49e-05 *** tensionM -0.32132 0.06027 -5.332 9.73e-08 *** tensionH -0.51849 0.06396 -8.107 5.21e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 297.37 on 53 degrees of freedom Residual deviance: 210.39 on 50 degrees of freedom AIC: 493.06 Number of Fisher Scoring iterations: 4

In the summary we look for the p-value in the last column to be less than 0.05 to consider an impact of the predictor variable on the response variable. As seen the wooltype B having tension type M and H have impact on the count of breaks.

Applied Data Science Coding with Python: Linear Regression Algorithm

## Statistics with R for Business Analysts – Poisson Regression

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause.The information presented here could also be found in public knowledge domains.

# Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners

Latest end-to-end Learn by Coding Projects (Jupyter Notebooks) in Python and R:

**All Notebooks in One Bundle: Data Science Recipes and Examples in Python & R****. **

**End-to-End Python Machine Learning Recipes & Examples.**

**End-to-End R Machine Learning Recipes & Examples.**

**Applied Statistics with R for Beginners and Business Professionals**

**Data Science and Machine Learning Projects in Python: Tabular Data Analytics**

**Data Science and Machine Learning Projects in R: Tabular Data Analytics**

**Python Machine Learning & Data Science Recipes: Learn by Coding**

**R Machine Learning & Data Science Recipes: Learn by Coding**

**Comparing Different Machine Learning Algorithms in Python for Classification (FREE)**

There are 2000+ End-to-End Python & R Notebooks are available to build **Professional Portfolio as a Data Scientist and/or Machine Learning Specialist**. All Notebooks are only $29.95. We would like to request you to have a look at the website for FREE the end-to-end notebooks, and then decide whether you would like to purchase or not.