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.R.10b
Textbook Question
Poisson: Deaths Currently, an average of 7 residents of the village of Westport (population 760) die each year (based on data from the U.S. National Center for Health Statistics).
b. Find the probability that on a given day, there are no deaths.

1
Step 1: Recognize that this is a Poisson probability problem. The Poisson distribution is used to model the number of events (e.g., deaths) occurring in a fixed interval of time or space, given a known average rate (λ). Here, the average rate is 7 deaths per year.
Step 2: Convert the average rate (λ) from yearly to daily. Since there are 365 days in a year, divide the yearly rate by 365 to find the daily rate: λ = 7 / 365.
Step 3: Use the Poisson probability formula to calculate the probability of 0 deaths on a given day. The formula is: P(X = k) = (λ^k * e^(-λ)) / k!, where k is the number of events (in this case, k = 0), λ is the average rate, and e is the base of the natural logarithm (approximately 2.718).
Step 4: Substitute k = 0 and the daily rate λ (calculated in Step 2) into the formula. Simplify the expression: P(X = 0) = (λ^0 * e^(-λ)) / 0!. Note that 0! = 1 and λ^0 = 1, so the formula simplifies to P(X = 0) = e^(-λ).
Step 5: Compute e^(-λ) using the daily rate λ. This will give you the probability of no deaths occurring on a given day.

This video solution was recommended by our tutors as helpful for the problem above
Video duration:
2mPlay a video:
Was this helpful?
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 a known average rate of occurrence. It is particularly useful for modeling rare events, such as deaths in a small population, where the events occur independently of each other.
Recommended video:
Guided course
Intro to Frequency Distributions
Rate Parameter (λ)
In the context of the Poisson distribution, the rate parameter (λ) represents the average number of occurrences in the specified interval. For the village of Westport, with an average of 7 deaths per year, λ would be 7. When calculating probabilities for shorter intervals, such as a day, λ must be adjusted accordingly (e.g., λ = 7/365 for daily calculations).
Recommended video:
Guided course
Parameters vs. Statistics
Probability of No Events
To find the probability of observing no events in a Poisson distribution, the formula P(X=0) = e^(-λ) is used, where e is the base of the natural logarithm. This formula indicates the likelihood of zero occurrences when the average rate is λ. In this case, it helps determine the probability of no deaths occurring on a given day in Westport.
Recommended video:
Probability of Multiple Independent Events
Watch next
Master Intro to Random Variables & Probability Distributions with a bite sized video explanation from Patrick
Start learningRelated Videos
Related Practice