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M119L15

# M119L15 - 5.11 PART C PASCAL’S TRIANGLE(Lesson 15...

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(Lesson 15: Binomial Distributions; 5-3) 5.05 LESSON 15: BINOMIAL DISTRIBUTIONS (SECTION 5-3) PART A: WHAT IS A BINOMIAL EXPERIMENT? Similar examples: • 1/6 of all Americans have cooties. Five Americans are randomly sampled. Let X = the number in the sample with cooties. • A multiple-choice test has five questions, each with six options (A-F). A student guesses randomly on all five questions. Let X = the number of correct answers.

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(Lesson 15: Binomial Distributions; 5-3) 5.06 Note: P 5 ( ) 0.000129
(Lesson 15: Binomial Distributions; 5-3) 5.07 PART B: HOW DO WE FIND THE PROBABILITIES? 1) Software / TI calculators 2) Table A-1 (pp.609-611 in the 3 rd edition of Triola) Provided on exams. Use it when you can. Up to n = 15 , some values of p . 3) Binomial probability formula

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(Lesson 15: Binomial Distributions; 5-3) 5.08
(Lesson 15: Binomial Distributions; 5-3) 5.09

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(Lesson 15: Binomial Distributions; 5-3) 5.10 Note: P 5 ( ) 0.000129
(Lesson 15: Binomial Distributions; 5-3)

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Unformatted text preview: 5.11 PART C: PASCAL’S TRIANGLE (Lesson 15: Binomial Distributions; 5-3) 5.12 PART D: SAMPLING RULE (See Lesson 11.) PART E: USING TABLE A-1 (pp.609-611 in the 3 rd edition of Triola) Note: “At least 4” can be rephrased as “4 or more” or “no fewer than 4.” (Lesson 15: Binomial Distributions; 5-3) 5.13 Think About It: Why are the probabilities skewed towards the high end? We want: (Lesson 15: Binomial Distributions; 5-3) 5.14 (Lesson 15: Binomial Distributions; 5-3) 5.15 PART F: WHEN IS AN EVENT “UNUSUAL”? Rare Event Rule for Inferential Stats (modified from Triola) (Lesson 15: Binomial Distributions; 5-3) 5.16 Note: P 49 ( ) ≈ 7.80% (Lesson 15: Binomial Distributions; 5-3) 5.17 Analysis #2 (one-tailed) (use in HW) Analysis #3 (two-tailed; we use symmetry) Analysis #4 ( z scores) See the next Lesson!...
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