Thanks! Could you please specify the type of distribution used to generate the Poisson means? I mean, how did you generate the lambda values used to plot the Poisson density functions?
Here is a good explanation of the meaning of lambda: www.statstodo.com/Poisson_Exp.php The Poisson parameter Lambda (λ) is the total number of events (k) divided by the number of units (n) in the data (λ = k/n). The unit forms the basis or denominator for calculation of the average, and need not be individual cases or research subjects. For example, the number of asthma attacks may be based on the number of child months, or the number of pregnancies based on the number of women years in using a particular contraceptive.
The simplest way is to use np.mean to get the mean value and np.std to get the standard deviation if the data follows a normal distribution. If it is a pandas dataframe then I recommend that you use df.describe() to compute summary statistics. More content is available here: apmonitor.com/che263/index.php/Main/PythonDataAnalysis
Great - thank you very much for taking the time to share this.
Thanks! Could you please specify the type of distribution used to generate the Poisson means? I mean, how did you generate the lambda values used to plot the Poisson density functions?
Here is a good explanation of the meaning of lambda: www.statstodo.com/Poisson_Exp.php The Poisson parameter Lambda (λ) is the total number of events (k) divided by the number of units (n) in the data (λ = k/n). The unit forms the basis or denominator for calculation of the average, and need not be individual cases or research subjects. For example, the number of asthma attacks may be based on the number of child months, or the number of pregnancies based on the number of women years in using a particular contraceptive.
How to fit distribution over given dta?
The simplest way is to use np.mean to get the mean value and np.std to get the standard deviation if the data follows a normal distribution. If it is a pandas dataframe then I recommend that you use df.describe() to compute summary statistics. More content is available here: apmonitor.com/che263/index.php/Main/PythonDataAnalysis
@@apm Thank you
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