## Exponential Distribution

• The time between events in Poisson process (i.e. inverse of Poisson) • Poisson: Events per single unit of time
• Exponential: Time per single events
• Pre-req’s:
• Events must occur at constant rate
• Events must be independent of each other
• Exponential is a continuous distribution as it measure time which can be fractioned or divided
• Since it is continuous distribution, we will have probability distribution function(pdf)
• In Poisson distribution
``````mean = λ
``````
• In exponential distribution
``````mean = μ = 1/λ
`````` • Example:
• Unique visitors arrive at the quality thought recpetion by a Poisson process at an average of 3 per hour.
• Find the probability that the next visitor arrives
• with in 10 minutes • after 30 minutes • in exactly 25 minutes => 0
• Excel Sheet
• EXPON.DIST(x,lambda,cumulative)

## CHI-Squared distribution -> 𝛘2 distibution

• IF Z1,Z2……Zk are the independent samples from standard Normal distribution • Chi-squared distribution is primarily used in hypothesis test for comparing Goodness of fit (theoreticial probabiliites vs Outcome Probabilities)
• Expected Value and variance • Visualization: • TEST Statistic • Example:
• In an engineering class of 75 students, 11 are left-handed. Does this class fit the prevailing theory that 12% of the people are left handed • CONCLUSION:
• There is not enough evidence to suggest the proportion of left handedness is other than 0.12
• A poker dealing machine is supposed to deal cards at random from an infinite deck
• In a test, you counted 1600 cards and observed the following 