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!!!