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 4.3.15
Textbook Question
Using a Distribution to Find Probabilities In Exercises 11–26, find the indicated probabilities using the geometric distribution, the Poisson distribution, or the binomial distribution. Then determine whether the events are unusual. If convenient, use a table or technology to find the probabilities.
Pass Completions NFL player Aaron Rodgers completes a pass 65.1% of the time. Find the probability that (a) the first pass he completes is the second pass, (b) the first pass he completes is the first or second pass, and (c) he does not complete his first two passes. (Source: National Football League)

1
Step 1: Identify the type of distribution to use. Since the problem involves finding the probability of the first success (pass completion) occurring on a specific trial, this is a geometric distribution. The probability of success (p) is given as 65.1% or 0.651, and the probability of failure (q) is 1 - p = 0.349.
Step 2: For part (a), use the formula for the geometric distribution: P(X = k) = q^(k-1) * p, where k is the trial number of the first success. Substitute k = 2, p = 0.651, and q = 0.349 into the formula to calculate the probability that the first pass completion occurs on the second pass.
Step 3: For part (b), calculate the probability that the first pass completion occurs on the first or second pass. This is the cumulative probability P(X ≤ 2), which is the sum of the probabilities for k = 1 and k = 2. Use the geometric distribution formula for each value of k and add the results: P(X ≤ 2) = P(X = 1) + P(X = 2).
Step 4: For part (c), calculate the probability that Aaron Rodgers does not complete his first two passes. This is equivalent to the probability of two consecutive failures, which can be expressed as q^2. Substitute q = 0.349 into the formula to find this probability.
Step 5: Determine whether the events are unusual. An event is typically considered unusual if its probability is less than 0.05. Compare the probabilities calculated in parts (a), (b), and (c) to this threshold to assess whether the events are unusual.

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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Geometric Distribution
The geometric distribution models the number of trials needed to achieve the first success in a series of independent Bernoulli trials. In this context, it is used to find the probability that the first successful pass completion occurs on a specific attempt, such as the second pass. The probability of success remains constant across trials, making it suitable for scenarios like pass completions.
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Binomial Distribution
The binomial distribution describes the number of successes in a fixed number of independent Bernoulli trials, each with the same probability of success. In the given question, it can be applied to find the probability of completing a certain number of passes out of a specified total attempts. This distribution is useful for determining the likelihood of multiple successes in a defined scenario.
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Unusual Events
An event is considered unusual if its probability is significantly low, typically less than 5%. In the context of the question, determining whether the outcomes of Aaron Rodgers' pass completions are unusual involves calculating the probabilities and assessing their significance. This concept helps in understanding the likelihood of specific outcomes in sports statistics and decision-making.
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