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 4.3.3
Textbook Question
In Exercises 1–4, find the indicated probability using the geometric distribution.
Find P(5) when p = 0.09

1
Step 1: Recall the formula for the probability mass function (PMF) of a geometric distribution: P(X = k) = (1 - p)^(k - 1) * p, where k is the trial number, p is the probability of success, and (1 - p) is the probability of failure.
Step 2: Identify the given values in the problem. Here, k = 5 (the trial number) and p = 0.09 (the probability of success).
Step 3: Substitute the given values into the formula. This becomes P(5) = (1 - 0.09)^(5 - 1) * 0.09.
Step 4: Simplify the expression. First, calculate (1 - 0.09) to find the probability of failure, then raise it to the power of (5 - 1), and finally multiply by 0.09.
Step 5: After simplifying the expression, you will have the probability P(5). Ensure all calculations are performed accurately to find the final result.

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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. It is characterized by a constant probability of success, denoted as 'p'. The probability mass function is given by P(X = k) = (1 - p)^(k-1) * p, where k is the trial number on which the first success occurs.
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Probability Mass Function (PMF)
The probability mass function (PMF) provides the probabilities of discrete random variables. For the geometric distribution, the PMF calculates the likelihood of achieving the first success on the k-th trial. Understanding the PMF is essential for determining specific probabilities, such as P(5) in this case, which represents the probability that the first success occurs on the fifth trial.
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Calculating Geometric Probability
To calculate the probability of the first success occurring on the k-th trial using the geometric distribution, substitute the values of p and k into the PMF formula. For example, with p = 0.09 and k = 5, the calculation involves finding (1 - 0.09)^(5-1) * 0.09. This process illustrates how to apply the geometric distribution to find specific probabilities.
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