"No one is harder on a talented person than the person themselves" - Linda Wilkinson ; "Trust your guts and don't follow the herd" ; "Validate direction not destination" ;

March 13, 2016

R Day #3 - Tip for the Day

This is based on reading from notes from link 

Logistic Regression
  • Applied when response is binary
  • (0/1, yes/No etc..), Also known as dichotomous outcome variable 
Binomial probability model
  • consists of (i) n independent trials where 
  • (ii) each trial results in one of two possible outcomes (Yes/No, 1/0)
  • (iii) the probability p of a success stays the same for each trial
Maximum likelihood - Find the value of the parameter(s) (in this case p) which makes the observed data most likely to have occurred

Poisson Regression
Applied for below situations
  • The occurrences of the event of interest in non-overlapping “time” intervals are independent
  • The probability two or more events in a small time interval is small, and
  • The probability that an event occurs in a short interval of time is proportional to the length of the time interval
  • Heteroscedasticity - means unequal error variances
Negative Binomial Model
  • The Poisson model does not always provide a good fit to a count response. 
  • An alternative model is the negative binomial distribution
Happy Learning!!!
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