Table of contents
- 1. Intro to Stats and Collecting Data55m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically1h 45m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables2h 33m
- 6. Normal Distribution and Continuous Random Variables1h 38m
- 7. Sampling Distributions & Confidence Intervals: Mean1h 3m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 12m
- 9. Hypothesis Testing for One Sample1h 1m
- 10. Hypothesis Testing for Two Samples2h 8m
- 11. Correlation48m
- 12. Regression1h 4m
- 13. Chi-Square Tests & Goodness of Fit1h 20m
- 14. ANOVA1h 0m
5. Binomial Distribution & Discrete Random Variables
Discrete Random Variables
Problem 5.3.9a
Textbook Question
In Exercises 9–16, use the Poisson distribution to find the indicated probabilities.
Births In a recent year (365 days), NYU-Langone Medical Center had 5942 births.
a. Find the mean number of births per day.

1
Step 1: Understand the Poisson distribution. The Poisson distribution is used to model the number of events (e.g., births) occurring in a fixed interval of time or space, given the events occur independently and at a constant average rate.
Step 2: Identify the total number of events and the time interval. In this problem, there are 5942 births over 365 days.
Step 3: Calculate the mean number of births per day. The mean (λ) for a Poisson distribution is the total number of events divided by the total time interval. Use the formula: .
Step 4: Simplify the fraction to find the mean number of births per day. This value represents the average rate of births per day at the medical center.
Step 5: Use this mean (λ) for further calculations if needed, such as finding probabilities of specific numbers of births per day using the Poisson probability formula.

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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Poisson Distribution
The Poisson distribution is a probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time or space, given that these events occur with a known constant mean rate and independently of the time since the last event. It is particularly useful for modeling rare events, such as the number of births in a day.
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Mean of a Poisson Distribution
In a Poisson distribution, the mean (λ) represents the average number of occurrences of the event in the specified interval. For the problem at hand, to find the mean number of births per day, you would divide the total number of births by the number of days in the year, providing a clear understanding of the expected daily occurrences.
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Expected Value
The expected value is a key concept in probability that provides a measure of the center of a probability distribution. In the context of the Poisson distribution, the expected value is equal to the mean (λ), which indicates the average outcome one can anticipate over a specified period. This concept helps in making predictions about future occurrences based on historical data.
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