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 4.T.2b
Textbook Question
In Exercises 1–7, consider a grocery store that can process a total of four customers at its checkout counters each minute.
Minitab was used to generate 20 random numbers with a Poisson distribution for . Let the random number represent the number of arrivals at the checkout counter each minute for 20 minutes. 3 3 3 3 5 5 6 7 3 6 3 5 6 3 4 6 2 2 4 1During each of the first four minutes, only three customers arrived. These customers could all be processed, so there were no customers waiting after four minutes.
b. Create a table that shows the number of customers waiting at the end of 1 through 20 minutes.

1
Step 1: Understand the problem. The grocery store can process up to 4 customers per minute. If more than 4 customers arrive in a given minute, the excess customers will have to wait. The goal is to create a table showing the number of customers waiting at the end of each minute for 20 minutes.
Step 2: Define the variables. Let the number of arrivals each minute be represented by the given Poisson-distributed random numbers. Let the number of customers processed per minute be fixed at 4. The number of customers waiting at the end of each minute can be calculated as the cumulative excess arrivals minus the cumulative processing capacity.
Step 3: Calculate the excess arrivals for each minute. For each minute, subtract the processing capacity (4 customers) from the number of arrivals. If the result is positive, it represents the number of customers who cannot be processed and will wait. If the result is negative or zero, it means no customers are waiting.
Step 4: Compute the cumulative waiting customers. For each minute, add the excess arrivals from the current minute to the cumulative waiting customers from the previous minute. This will give the total number of customers waiting at the end of each minute.
Step 5: Create the table. Organize the results into a table with two columns: one for the minute (1 through 20) and one for the number of customers waiting at the end of that minute. Use the calculations from Steps 3 and 4 to populate the table.

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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 commonly used in scenarios where events happen randomly, such as customer arrivals at a store.
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Queueing Theory
Queueing theory is the mathematical study of waiting lines or queues. It helps analyze the behavior of queues in various systems, including how many customers arrive, how long they wait, and how many can be served. In this context, it is essential for understanding customer flow and service efficiency at the grocery store's checkout counters.
Random Number Generation
Random number generation is the process of creating a sequence of numbers that lack any predictable pattern. In statistics, it is often used to simulate real-world processes, such as customer arrivals in this scenario. The generated random numbers can represent various outcomes, allowing for analysis and modeling of different situations.
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