Lecture 8: Random Variables and Their Distributions | Statistics 110 | Summary and Q&A

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April 29, 2013
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Harvard University
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Lecture 8: Random Variables and Their Distributions | Statistics 110

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Summary

In this video, the instructor discusses the binomial distribution and random variables. The binomial distribution is written as bin of NP, where N and P are parameters. The distribution represents the number of successes in N independent trials, where each trial results in success or failure. The instructor explains that there are three important ways to think of the binomial distribution: counting the number of successes, using indicator random variables, and calculating the probability mass function. The instructor also explains the law of total probability and how to compute the probability of the sum of two binomials. Additionally, the instructor introduces the hypergeometric distribution and its connection to the binomial distribution.

Questions & Answers

Q: What is the binomial distribution and why is it important?

The binomial distribution represents the number of successes in N independent trials, where each trial results in success or failure. It is important because it is one of the most famous and useful distributions in statistics.

Q: Can you define the parameters of the binomial distribution?

The parameters of the binomial distribution are N and P. N represents the number of trials and P represents the probability of success in each trial.

Q: Is there only one binomial distribution?

Strictly speaking, there is not just one binomial distribution. There is a whole family of binomial distributions because N can be any positive integer and P can be any real number between 0 and 1.

Q: How can we interpret the binomial distribution in terms of the number of successes?

The binomial distribution can be interpreted as the number of successes in N independent Bernoulli P trials. Each trial results in success or failure, and the probability of success is represented by P.

Q: Can you explain the concept of indicator random variables?

Indicator random variables are used to indicate whether a specific event, such as a trial being a success or failure, has occurred. In the case of the binomial distribution, we can think of X as the sum of indicator random variables where XJ is 1 if the Jth trial is a success and 0 otherwise.

Q: What does it mean for random variables to be independent and identically distributed (iid)?

Independent and identically distributed (iid) means that the random variables are independent of each other and have the same distribution. In the case of the binomial distribution, all XJ's are iid Bernoulli P random variables.

Q: What is the probability mass function (PMF) and how does it relate to the binomial distribution?

The probability mass function (PMF) is a function that describes the probabilities of a random variable taking on specific values. For the binomial distribution, the PMF is given by the binomial coefficient multiplied by P to the power of the number of successes and Q to the power of the number of failures.

Q: How can we compute the sum of two binomials in terms of their PMFs?

The sum of two binomials can be computed by conditioning on one of the random variables and using the law of total probability. By summing the probabilities of the event X + Y = K given X = J multiplied by the probability of X = J, we can calculate the PMF of the sum.

Q: How does the hypergeometric distribution relate to the binomial distribution?

The hypergeometric distribution is similar to the binomial distribution, but it represents sampling without replacement. The number of successes in the hypergeometric distribution is the number of white marbles in a sample picked from a jar of black and white marbles.

Q: What is the probability mass function of the hypergeometric distribution?

The probability mass function of the hypergeometric distribution is given by the binomial coefficient multiplied by the product of the probabilities of choosing a specific number of white marbles and non-white marbles.

Q: Can the hypergeometric distribution be approximated using the binomial distribution?

Under certain conditions, such as when the sample size is small compared to the population size, the hypergeometric distribution can be approximated using the binomial distribution. This is because sampling with replacement and without replacement behave similarly in these cases.

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