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Merge pull request #74 from snowch/claude/binomial-distribution-calculator-QJG1z
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chapter_07.md

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![Bernoulli CDF](ch07_bernoulli_cdf_generic.svg)
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The CDF shows the cumulative probability: P(X ≤ 0) = 0.7 (just the negative test outcome) and P(X ≤ 1) = 1.0 (both outcomes).
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The CDF shows the step function: starts at 0 for x < 0, jumps to 0.7 at x=0 (the value when outcome is 0), stays flat at 0.7 until x=1, then jumps to 1.0 at x=1 (the value when including both outcomes 0 and 1).
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**Understanding PMF and CDF Charts**
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![Bernoulli CDF](ch07_bernoulli_cdf.svg)
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The CDF shows cumulative probabilities: P(X ≤ 0) = 0.9 and P(X ≤ 1) = 1.0.
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The CDF shows the step function: starts at 0 for x < 0, jumps to 0.9 at x=0, stays flat at 0.9 until x=1, then jumps to 1.0 at x=1.
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![Binomial CDF](ch07_binomial_cdf_generic.svg)
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The CDF shows P(X ≤ k), the cumulative probability of getting k or fewer heads.
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The CDF shows the step function increasing from 0 to 1, representing the cumulative probability of getting k or fewer heads.
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:::{admonition} Example: Sales Calls with n = 20, p = 0.15
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![Binomial CDF](ch07_binomial_cdf.svg)
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The CDF shows P(X ≤ k), the cumulative probability of getting k or fewer successful calls.
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The CDF shows the step function increasing from 0 to 1, representing the cumulative probability of getting k or fewer successful calls.
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![Geometric CDF](ch07_geometric_cdf_generic.svg)
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The CDF shows P(X ≤ k), approaching 1 as k increases (eventually you'll succeed).
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The CDF shows the step function increasing from 0 toward 1 as k increases (eventually you'll succeed).
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:::{admonition} Example: Certification Exam with p = 0.6
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![Geometric CDF](ch07_geometric_cdf.svg)
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The CDF shows P(X ≤ k), increasing toward 1 as the trial number increases.
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The CDF shows the step function increasing from 0 toward 1 as the trial number increases.
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![Negative Binomial CDF](ch07_negative_binomial_cdf_generic.svg)
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The CDF shows P(X ≤ k), the cumulative probability of achieving r successes within k trials.
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The CDF shows the step function increasing from 0 to 1, representing the cumulative probability of achieving r successes within k trials.
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:::{admonition} Example: Quality Control with r = 3, p = 0.05
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![Negative Binomial CDF](ch07_negative_binomial_cdf.svg)
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The CDF shows P(X ≤ k), the cumulative probability of finding 3 defective items within k tests.
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The CDF shows the step function increasing from 0 to 1, representing the cumulative probability of finding 3 defective items within k tests.
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![Poisson CDF](ch07_poisson_cdf_generic.svg)
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The CDF shows P(X ≤ k), useful for questions like "What's the probability of 6 or fewer calls?"
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The CDF shows the step function increasing from 0 to 1, useful for questions like "What's the probability of 6 or fewer calls?"
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:::{admonition} Example: Email Arrivals with λ = 5
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![Poisson CDF](ch07_poisson_cdf.svg)
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The CDF shows P(X ≤ k), useful for questions like "What's the probability of 6 or fewer emails?"
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The CDF shows the step function increasing from 0 to 1, useful for questions like "What's the probability of 6 or fewer emails?"
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![Hypergeometric CDF](ch07_hypergeometric_cdf_generic.svg)
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The CDF shows P(X ≤ k), useful for questions like "What's the probability of getting at most 1 Ace?"
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The CDF shows the step function increasing from 0 to 1, useful for questions like "What's the probability of getting at most 1 Ace?"
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:::{admonition} Example: Lottery Tickets with N=100, K=20, n=10
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![Hypergeometric CDF](ch07_hypergeometric_cdf.svg)
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The CDF shows P(X ≤ k), the cumulative probability of getting k or fewer winning tickets.
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The CDF shows the step function increasing from 0 to 1, representing the cumulative probability of getting k or fewer winning tickets.
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