Statistics with R Programming Part 3 | Poisson Distribution Tutorial | Data Science Tutorial

Statistics with R Programming Part 3 | Poisson Distribution Tutorial | Data Science Tutorial
Hello and welcome back to another session of statistics tutorial with R Programming Powered by Acadgild. In the previous video, you have learned Random Variables in R which includes the types of random variables i.e. Discrete Random Variables and Continuous Random Variables in-depth. In this Statistics tutorial with R, You will be able to learn one of the discrete random variables in detail that is Poisson Distribution, before that, if you have missed our previous tutorials of this series kindly click the link and watch the video for better continuation.
Let’s go through the concept in-depth,
Poisson Distribution:
• It expresses the probability of a given number of events (k) occurring in a fixed interval of time space
• These events occur with a known constant rate and independently of the time since the last event
• The rate of occurrence does not change with time and from event to event within an interval
• It can also be used for events in other specified intervals of distance, area volume
• 2 events cannot occur at the same instant; instead at each very small subinterval exactly one event either occurs or does not occur
• The probability of an event is proportion to the length of the time
• Its probability distribution can be expressed by a binomial distribution where the number of trials is greater than the number of successes
• If the time rate is given for the events to occur, then λ=rt where r has units of 1/time
• If an event averages around once per interval (λ=1), then P(0 events in next interval) = 0.37
• Also, P(exactly one event in next interval) = 0.37
Mean and variance
• Mean, E(x) = λ
• Variation, Var(x) = λ
• Because it is a consequence of the functional form of the Poisson distribution
• In Poisson distribution, the p value is small as compared to no. of trials n. We can relate this to Bernoulli. Thus, Var(x) = np(1-p) = np = E(x)
Kindly, go through the complete video to learn the implementation. Please like, share and subscribe to the channel for more tutorials.
#poisson, #Statistics, #R, #datascience

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