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Unformatted text preview: BIT 2405 Quantitative Methods I Chapter 5b Discrete Probability Distributions This Week Lectures Hawkes Today Next Week Is TEST WEEK! Lectures Hawkes REQUIRED Quiz 1 and the Optional Homework CLOSE @ 9am on Friday, 2/25. Lectures Hawkes Schedule Next Week Feb 21st. Day Content Time/PAM 2030 Monday 2/21 Hawkes 5.1 &amp; 5.2 DUE @ 11:59 pm Help Sessions 9:05, 11:15 &amp; 12:20 Tuesday 2/22 Wednesday 2/23 Help Sessions 9:05 &amp; 11:15 Early Test 1 12:20 pm Thursday 2/24 Friday 2/25 Test 1 9:05, 11:15 &amp; 12:20 Day Content Time/PAM 2030 Monday 2/21 Hawkes 5.1 &amp; 5.2 DUE @ 11:59 pm Help Sessions 9:05, 11:15 &amp; 12:20 Tuesday 2/22 Wednesday 2/23 Help Sessions 9:05 &amp; 11:15 Early Test 1 12:20 pm Thursday 2/24 Friday 2/25 Test 1 9:05, 11:15 &amp; 12:20 Schedule Next Week Feb 21st. Your Scheduled Test Time IS on Friday in the Time for which you are ENROLLED. You MUST EMAIL ME by MONDAY @ 9AM and get permission from me to change your test from your Scheduled Time. NOW IS THE TIME to figure out WHAT YOU KNOW &amp; WHAT YOU DONT KNOW . Today Key HELP SESSIONS Last Minute Help @ 12:30pm Questions? Chapter 5 Discrete Probability Distributions Today Last Time KEY Lecture # Chapter TEXT Quantitative Methods I, Anderson, Sweeney &amp; Williams Hawkes Learning Systems: Statistics HLS Sectio n Topic Sectio n Topic 08 Ch05a.ppt 09 Ch05b.ppt 5.1 Random Variables 5.1 Discrete Random Variables 5.2 Discrete Probability Distributions 5.2 The Binomial Distribution 5.3 Expected Value and Variance 5.4 Binomial Probability Distribution Chapter 5 Discrete Probability Distributions .10 .20 .30 .40 0 1 2 3 4 Random Variables Discrete Probability Distributions Expected Value and Variance Binomial Probability Distribution A random variable is a numerical description of the outcome of an experiment. Random Variables A discrete random variable may assume either a finite number of values or an infinite sequence of values. A continuous random variable may assume any numerical value in an interval or collection of intervals. We typically label the random variable as x a sequence too large to specify each value 1 2 3 4 1 2 3 4 Discrete and Continuous Variables. Whether Bound Whether Bound or Unbound 1 2 3 4 DISCRETE and Continuous Variables. DISCRETE: You are COUNTING WHOLE OBJECTS. You could devise a scale whereby the tick marks represent all possible values of the outcomes. No possible outcome in reality could fall between the values of your scale....
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This note was uploaded on 12/20/2011 for the course BIT 2405 taught by Professor Plkitchin during the Spring '08 term at Virginia Tech.
 Spring '08
 PLKitchin

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