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Data scientist certification

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ExcelR is the fastest growing company in providing Data scientist certification. In Data analytics course we are providing you key benefits, after compleating the Data scientist certification program successfully ExcelR is providing you certification from Malaysian University. ExcelR is based out and have multiple branches in India if you wish to learn in Data scientist certification your search ends at ExcelR. – PowerPoint PPT presentation

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Title: Data scientist certification


1
Types of Poisson Regression
2
Offset Regression
  • A variant of Poisson Regression
  • Count data often have an exposure variable, which
    indicates the number of times the event could
    have happened
  • This variable should be incorporated into a
    Poisson model with the use of the offset option

3
Offset Regression
  • If all the students have same exposure to math
    (program), the number of awards are comparable
  • But if there is variation in the exposure, it
    could affect the count
  • A count of 5 awards out of 5 years is much bigger
    than a count of 1 out of 3
  • Rate of awards is count/exposure
  • In a model for awards count, the exposure is
    moved to the right side
  • Then if the algorithm of count is logged also
    the exposure, the final model contains
    ln(exposure) as term that is added to the
    regression equation
  • This logged variable, ln(exposure) or a
    similarity constructed variable is called the
    offset variable

4
Offset Poisson Regression
  • A data frame with 63 observations on the
    following 4 variables. (lung.cancer)
  • years.smok a factor giving the number of years
    smoking
  • cigarettes a factor giving cigarette consumption
  • Time man-years at risk
  • y number of deaths

5
Negative Binomial Regression
  • One potential drawback of Poisson regression is
    that it may not accurately describe the
    variability of the counts
  • A Poisson distribution is parameterized by ?,
    which happens to be both its mean and variance.
    While convenient to remember, its not often
    realistic.
  • A distribution of counts will usually have a
    variance thats not equal to its mean. When we
    see this happen with data that we assume (or
    hope) is Poisson distributed, we say we have
    under- or over dispersion, depending on if the
    variance is smaller or larger than the mean.
  • Performing Poisson regression on count data that
    exhibits this behavior results in a model that
    doesnt fit well.

6
  • One approach that addresses this issue is
    Negative Binomial Regression.
  • We go for Negative Binomial Regression when
    Variance gt Mean (over dispersion)
  • The negative binomial distribution, like the
    Poisson distribution, describes the probabilities
    of the occurrence of whole numbers greater than
    or equal to 0.
  • The variance of a negative binomial distribution
    is a function of its mean and has an additional
    parameter, k, called the dispersion parameter.
  • The variance of a negative binomial distribution
    is a function of its mean and has an additional
    parameter, k, called the dispersion parameter.
  • var(Y)µµ2/k

7
Zero Inflated Regression
8
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