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Udemy - Poisson Regression With SAS Stat
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Poisson Regression analysis model is used for predictive analysis
SAS Stat provides built-in functions to calculate and evaluate the Poisson regression model
One of the use cases of a Poisson regression model would be predicting the number of leads that will convert to customers within a particular time frame
Poisson regression is based on the concept of Poisson distribution
Requirements
Poisson regression is based on the concept of Poisson distribution
Description
Poisson regression is based on the concept of Poisson distribution. It is another category belonging to the set of regression techniques that combines the properties of both Linear as well as Logistic regressions. However, unlike Logistic regression which generates only binary output, it is used to predict a discrete variable.
Poisson Regression in SAS is a type of regression analysis model which is used for predictive analysis where there are multiple numbers of possible outcomes expected which are countable in numbers. SAS provides built-in functions to calculate and evaluate the Poisson regression model. Poisson regression is useful to predict the value of the response variable Y by using one or more explanatory variable X. This is a preferred probability distribution which is of discrete type. One of the use cases of a Poisson regression model would be predicting the number of leads that will convert to customers within a particular time frame in an organization.
In Probability and Statistics, there are three distributions based on continuous and discrete data – Normal, Binomial, and Poisson Distributions.
Normal Distribution
is often seen as a Bell Curve. Poisson distribution is often referred to as the Distribution of rare events. This is predominantly used to predict the probability of events based on how often the event has happened. It allows for a given number of events occurring in a set of periods. It is used in many real-life situations.
Who this course is for
Data Engineers, Analysts, Architects, Software Engineers, IT operations, Technical managers
Anyone who wants to learn about data and analytics
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FILE LIST
Filename
Size
~Get Your Files Here !/01 - Introduction/001 Course Introduction.mp4
63.2 MB
~Get Your Files Here !/01 - Introduction/002 Problem Statement.mp4
69.8 MB
~Get Your Files Here !/02 - Dataset/001 Dataset Explanation.mp4
38.2 MB
~Get Your Files Here !/03 - Exploring Dataset/001 Exploring Dataset.mp4
40.6 MB
~Get Your Files Here !/03 - Exploring Dataset/002 Exploring Dataset Continue.mp4
60.8 MB
~Get Your Files Here !/04 - Fitting Poisson Regression Model/001 Fitting Poisson Regression Model Part 1.mp4
61.2 MB
~Get Your Files Here !/04 - Fitting Poisson Regression Model/002 Fitting Poisson Regression Model Part 2.mp4
62.1 MB
~Get Your Files Here !/04 - Fitting Poisson Regression Model/003 Fitting Poisson Regression Model Part 3.mp4
59.7 MB
~Get Your Files Here !/04 - Fitting Poisson Regression Model/004 Fitting Poisson Regression Model Part 4.mp4
56.1 MB
~Get Your Files Here !/04 - Fitting Poisson Regression Model/005 Fitting Poisson Regression Model Part 5.mp4
87.2 MB
~Get Your Files Here !/04 - Fitting Poisson Regression Model/006 Fitting Poisson Regression Model Part 6.mp4
99.2 MB
~Get Your Files Here !/05 - Fitting Negative Binomial Model/001 Fitting Negative Binomial Model Part 1.mp4
86.2 MB
~Get Your Files Here !/05 - Fitting Negative Binomial Model/002 Fitting Negative Binomial Model Part 2.mp4
66.8 MB
~Get Your Files Here !/05 - Fitting Negative Binomial Model/003 Fitting Negative Binomial Model Part 3.mp4
63.7 MB
~Get Your Files Here !/05 - Fitting Negative Binomial Model/004 Fitting Negative Binomial Model Part 4.mp4
67.5 MB
~Get Your Files Here !/05 - Fitting Negative Binomial Model/005 Fitting Negative Binomial Model Part 5.mp4
