probability_summary
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probability_summary

Course: STAT 219, Fall 2008

School: Stanford

Word Count: 8291

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MBA 604 Introduction Probaility and Statistics Lecture NotesMuhammad El-Taha Department of Mathematics and Statistics University of Southern Maine 96 Falmouth Street Portland, ME 04104-9300MBA 604, Spring 2003 MBA 604 Introduction to Probability and Sta
Georgia Tech - ISYE - 2028
Slight error. There should be another lambda in the denominator - thus 1/xln(a) is the answer
Georgia Tech - ISYE - 2028
ISYE2028 A and B Spring 2009Sample 1 January 8, 20091Take me out to the ballgame50 baseball fans were asked to report on the number of games they attended last year. Download the data baseball.csv from the data folder on the website. Use appropriate g
Georgia Tech - ISYE - 2028
ISYE 2028 A and B Notation and PrerequisitesDr. Kobi Abayomi January 8, 20091IntroductionJust a brief review of some notation you should know and some short questions. We often use roman letters cfw_X, Y, x, y. for things we hope to measure or model;
Georgia Tech - ISYE - 2028
ISYE 2028 Exam 1 EquationsDr. Kobi Abayomi March 25, 2009You must show all work to receive full credit. All regrades must be submitted the day the exam is returned.1Information You May Find Useful : Functions of One Random Variable Given a random vari
Georgia Tech - ISYE - 2028
ISYE 2028 A and B Spring 2009 Lecture 16Dr. Kobi Abayomi April 13, 20091Introduction - The simplest model - The ANOVA modelIn studying methods for the analysis of quantitative data, we rst focused on problems involving a single sample of numbers and t
Georgia Tech - ISYE - 2028
Lecture 15: Extensions of the Linear Model: Multiple Regression, Non-Linear Regression, Regression DiagnosticsDr. Kobi Abayomi April 13, 20091Introduction: Multiple Regression ModelingIn multiple regression, several predictors are used to model a sing
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ISYE2028 Spring 2009 Lecture 14 The Linear Model: OLS Regression Cont.Dr. Kobi Abayomi April 13, 20091Covariance and The Correlation CoecientRecall the form of the sample variance (of a variable x).n i=1 (xis2 = x)2 n1I can write it in this equiva
Georgia Tech - ISYE - 2028
ISYE2028 Spring 2009 The Linear Model: OLS RegressionDr. Kobi Abayomi April 10, 2009IntroductionRegression Analysis is one of the simplest ways we have in statistics to investigate the relationship between two or more variables related in a non-determi
Georgia Tech - ISYE - 2028
ISYE 2028 A and B Spring 2009 Practicum 3Dr. Kobi Abayomi March 23, 2009Please be able to show all of own your work and reasoning. Include computer printouts where you can. You wont need Good Luck. Due Monday April 14th - In class111.1RegressionRes
Georgia Tech - ISYE - 2028
Georgia Tech - ISYE - 2028
ISYE 2028 A and B Lecture 12 Condence Intervals and Hypothesis Testing cont.Dr. Kobi Abayomi March 25, 2009We have looked at hypothesis testing generally, but we have used only the specic example of a test for the population mean. For instance, if X , 2
Georgia Tech - ISYE - 2028
Georgia Tech - ISYE - 2028
ISYE 2028 A and B Lecture 11 Condence Intervals and Hypothesis TestingDr. Kobi Abayomi March 13, 20091A condence interval is an interval estimate for a population parameterFirst, an interval estimate is a range of possible values. If I say is between
Georgia Tech - ISYE - 2028
Georgia Tech - ISYE - 2028
Georgia Tech - ISYE - 2028
ISYE 2028 A and B Lecture 10 Sampling Distributions and Test StatisticsDr. Kobi Abayomi March 13, 20091Introduction: The context for Condence Intervals and Hypothesis Testing: Sampling Distributions for Test StatisticsHere is a (non-exhaustive) illust
Georgia Tech - ISYE - 2028
Georgia Tech - ISYE - 2028
ISYE 2028 A and B Lecture 9 Estimation and SamplingDr. Kobi Abayomi March 25, 20091SamplingWhen we sample we draw observations from a population with a distribution. Our samples are our observations. We often, almost always really, say that our sample
Georgia Tech - ISYE - 2028
Georgia Tech - ISYE - 2028
Georgia Tech - ISYE - 2028
Georgia Tech - ISYE - 2028
ISYE 2028 A and B Lecture 8Kobi Abayomi March 25, 20091Independent Random VariablesTwo random variables are independent if pX,Y (x, y ) = pX (x)pY (y ) or fX,Y (x, y ) = fX (x)fY (y ) (2) (1)This is directly analogous to the general probability rules
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ISYE 2028 A and B Lecture 7 Conditional Expectation and PredictionDr. Kobi Abayomi February 10, 20091Conditional Expectation (again)We should recall the denition of the conditional expectation: xP(x|Y = y ), discrete xfX |Y =y dx, continuousxE (X |Y
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ISYE 2028 A and B Lecture 6 Canonical Continuous Random Variables and some brief resultsDr. Kobi Abayomi February 10, 20091Introduction - The Normal Distribution, Normal DataRemember that continuous data is data that can assume an uncountable number o
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ISYE 2028 A and B Lecture 5Dr. Kobi Abayomi January 29, 20091Joint DistributionsTwo given random variables X and Y have a general distribution a joint distribution that is an extension of the single variable denition and notation we generate from rst
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ISYE 2028 A and B Lecture 4Dr. Kobi Abayomi January 20, 20091Introduction - Continuous Random VariablesWe call a random variable continuous if it has an uncountable number of values; if it can take all values in an interval of values. Examples of cont
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ISYE2028 A and B: Probability Lecture 3Dr. Kobi Abayomi January 15, 20091Introduction: We work with Random VariablesRemember that we use probability to describe outcomes of experiments1 which we call events2 If probability is the frame of statistics t
Georgia Tech - ISYE - 2028
Georgia Tech - ISYE - 2028
ISYE2028A and B: Probability Lecture 2Dr. Kobi Abayomi February 10, 20091The basic principles of countingIt is tting that a probability class begins with counting, for a probability of an event may be seen as an enumeration of the ratio: number of pos
Georgia Tech - ISYE - 2028
ISYE 2028 A and B Spring 2009 Lecture 1Dr. Kobi Abayomi January 8, 20091Introduction: What is DataIn statistics we worry about what there is to observe (what we expect to see) and what we have actually observed (what we do in fact see). Data are the q
Georgia Tech - ISYE - 2028
Georgia Tech - ISYE - 2028
Georgia Tech - ISYE - 2028
Georgia Tech - ISYE - 2028
Georgia Tech - ISYE - 2028
1ISyE 2028 Homework 6Due : July 17 2009 All questions are taken from textbook, Walpoles Probability & Statistics 8th edition 1. Following is temperature and discomfort index. Temperature Discomfort indext 71 55 68 66 67 70 71 54 53 53 54 55 70 54 73 57
Georgia Tech - ISYE - 2028
1ISyE 2028 Homework 5Due : July 06 2009 All questions are taken from textbook, Walpoles Probability & Statistics 8th edition 1. 11.16 2. 11.20 3. 11.25 4. Following is temperature and discomfort index. Temperature Discomfort indext 71 55 68 66 67 70 71
Georgia Tech - ISYE - 2028
1ISyE 2028 Homework 4Due : June 26 2009 All questions are taken from textbook, Walpoles Probability & Statistics 8th edition 1. 10.56 2. 10.62 3. 10.67 4. 10.77 5. 10.82 6. 10.90 7. 11.4 8. 11.14
Georgia Tech - ISYE - 2028
1ISyE 2028 Homework 3Due : June 15 2009 All questions are taken from textbook, Walpoles Probability & Statistics 8th edition 1. 10.2 2. 10.8 3. 10.22 4. 10.29 5. 10.35 6. 10.47
Georgia Tech - ISYE - 2028
1ISyE 2028 Homework 2Due : May 29 2009 All questions are taken from textbook, Walpoles Probability & Statistics 8th edition. 1. 8.44 2. 8.55 3. 9.2 4. 9.8 5. 9.13 6. 9.27 7. 9.41
Georgia Tech - ISYE - 2028
Miguel Pajares ISYE 2028# Generate 30 sample sets, 100 exponentially distributed for any #lambda, in this case, i chose lambda to be r = 1 sample1 = rexp(100, rate = r) sample2 = rexp(100, rate = r) sample3 = rexp(100, rate = r) sample4 = rexp(100, rate
Georgia Tech - ISYE - 2028
1ISyE 2028 Homework 1Due : May 20 2009 All questions except 4 are taken from textbook, Walpoles Probability & Statistics 8th edition. 1. 5.58 2. 6.5 3. 6.45 4. Generate 30 sample sets from common Exponential distribution(any ), each sample sets consists
Georgia Tech - MATH - 2602
Georgia Tech - MATH - 2602
Georgia Tech - MATH - 2602
MATH2602 A&B, 2009 Homework Problems 5.1 5.2 5.3 5.4 3, 1, 1, 1, 4, 3, 2, 2, 5, 5, 3, 3, 6a-e, 9a-c, 12, 15, 19, 27. 6, 8, 9, 11, 13, 15, 18, 19, 22, 26, 29, 35, 45, 47, 49, 53. 5, 7, 10, 12, 13, 15, 17, 22, 23. 4, 7, 8, 12.8.2 - 2, 5, 7a,c, 8, 18, 19, 2
Georgia Tech - MATH - 2602
Georgia Tech - MATH - 2602
Georgia Tech - MATH - 2602
Georgia Tech - MATH - 2602
Georgia Tech - MATH - 2602
Georgia Tech - MATH - 2602