Lecture 22 - Fall 2009

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

This preview shows pages 1–3. Sign up to view the full content.

± 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.

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
± 2 ± Any Normal CRV
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

### Page1 / 4

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

This preview shows document pages 1 - 3. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online