Lecture 22 - Fall 2009

Lecture 22 - Fall 2009 - I. Introduction II. Descriptive...

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± 1 I. Introduction II. Descriptive Statistics III. Probability, Random Variables and Sampling Distributions. A. Probabilty (Chapter 5) B. Random Variables (Chapter 5) C. Normal Distribution (Chapter 6) D. Sampling Distribution (Chapter 7) Exam 2: Thursday, Nov. 5, 7:00 – 9:00 PM 1. CRVs: Describe and Define – What are they? Measured – fractions/decimals make sense. Any value in range possible – infinite possibilities ( uncountable ) Probabilities for exact values = 0 Calculate probabilities for intervals. C. Continuous Random Variables (CRVs) Examples: ² Fluid ounces in a “20oz. bottle of Coke.” What is: P( 19.90 < x < 20.10)? ² Weight of a “16 oz. bag of chips.” What is: P( 15.85 < x < 16.15)? ± Population Parameters μ x and σ x • Define the formula for normal distribution – a math model for the normal distribution: 2 1 2 1 () x x x f μ σ ⎛⎞ ⎜⎟ ⎝⎠ 2. Normally Distributed CRVs 2 x xe σπ =⋅ π = 3.14…. and e = 2.718… x is the variable, a normally distributed CRV.
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± 2 ± Any Normal CRV
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Lecture 22 - Fall 2009 - I. Introduction II. Descriptive...

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