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School: UCF
Course: Theory Of Interest
Stat 4183 Fall 2012 Exam One Name 1. The present value of two payments of $100 each to be made at the end of n years and 2n years is $80. If i = 0.10, nd n. 100(v n + v 2n ) = 80; 2. It is known that a8 a12 = a3 +sx ay + s z for any eective interest rate
School: UCF
Course: Theory Of Interest
Stat 4183 Fall 2012 Exam Two Name 1. A loan of $1000 is being repaid with quarterly payments at the end of each quarter for 5 years at 6% convertible quarterly. Find the outstanding loan balance at the end of the second year and nd the amount of principal
School: UCF
Course: STATISTICAL METHODS I
CHAPTER 8 Homework:1,3,7,17,23,26,31,33,39,41,47,49,55,61, 67,69,100,107,109ab Sec 8.1: Elements of a hypothesis Testing: (1) Set up hypotheses A hypothesis is simply a statement about a population parameter, e.g. the population mean. There are two types
School: UCF
Course: Statistical Methods
Chapter 4 Discrete Random Variables 1. Suppose a bookie will give you $6 for every $1 you risk if you pick the winner in 3 ballgames. For every $1 bet, you either lose $1 or gain $6. What is the bookies expected earnings per dollar wagered? 2. Given the f
School: UCF
Course: Statistical Methods
Chapter 4 Discrete Probability 4.1Discretevs.ContinuousRandomVariables Distributions 4.2DiscreteProbabilityDistributions 4.3ExpectedValueforDRVs 4.4BinomialDistribution formula STA2023_Inghram_Fall2013 1 4.1 Discrete vs. Continuous Random Variables Twotyp
School: UCF
Course: Principles Of Statistics
STA 2014C.006 1. Find these probabilities: a. P( Z > -0.74 ) b. P( Z < 0 ) c. P( -2.15 < Z 1.55 ) d. P( Z = 2.38) Chapter 5 Review 0.7704 0.5 0.9237 0 2. Assume the random variable X is normally distributed, with mean =74 and =8. Find the following probab
School: UCF
Course: Statistical Methods 1
A. ./\ AA.) 1-H(0.\3~5] xv 2x3 pix) 'M LeftOAaS) 4v t5)? £0.23an a. 2x39) : 3b.0-\_81/ v , I v Please show all work to receive full credit. With uestions concernin the Normal Distribution ou must grovide a gicture. L010 1. Based on past results, t
School: UCF
Course: Data Preparation
Data Visualization Section 1 Introduction Section 2 Numerical Measurements for One Variable Numerical Measures for Location Parameter Numerical Measures for Scale Parameter Section 3 Graphical Methods for One Variable Histogram Box Plot Density Plot
School: UCF
Course: Principles Of Statistics
Chapter Outline 1.1 An Overview of Statistics 1.2 Data Classification 1.3 Experimental Design 1 of 61 Section 1.1 An Overview of Statistics 2 of 61 Section 1.1 Objectives Define statistics Distinguish between a population and a sample Distinguish betwe
School: UCF
Course: Statistical Methods
STATISTICS AND THE TI-84 Lesson #11 Inferential Statistics: Two populations 1.Inference:comparing twopopulation means Exercise 1.There aretwopopulations.Asample ofsize120from oneofthepopulations gave a mean of15andastandard deviation of1.3.Asample ofsize8
School: UCF
Course: Statistical Methods
Chapter6 SamplingDistributions 6.1 ConceptofaSamplingDistribution 6.2 PropertiesofSamplingDistributions 6.3 TheSamplingDistributionoftheSampleMeanand theCentralLimitTheorem Motivation Inmostpracticalapplications,wewontknowhowarandomvariable behaves. Inthe
School: UCF
Course: Regression Analysis
Examples and Multivariate Testing Lecture XXV I. Examples. A. B. Example 9.6.1 (mean of a binomial distribution) Assume that we want to know whether a coin toss is biased based on a sample of ten tosses. Our null hypothesis is that the coin is fair ( H 0
School: UCF
Course: Regression Analysis
Composite Tests and The Likelihood Ratio Test Lecture XXIV I. Simple Tests Against a Composite A. Mathematically, we now can express the tests as testing between H 0 : 0 against H1 : 1 , where 1 is a subset of the parameter space. Given this specification
School: UCF
Course: Theory Of Interest
Stat 4183 Fall 2012 Exam One Name 1. The present value of two payments of $100 each to be made at the end of n years and 2n years is $80. If i = 0.10, nd n. 100(v n + v 2n ) = 80; 2. It is known that a8 a12 = a3 +sx ay + s z for any eective interest rate
School: UCF
Course: Theory Of Interest
Stat 4183 Fall 2012 Exam Two Name 1. A loan of $1000 is being repaid with quarterly payments at the end of each quarter for 5 years at 6% convertible quarterly. Find the outstanding loan balance at the end of the second year and nd the amount of principal
School: UCF
Course: Theory Of Interest
Stat 4183 Fall 2012 Exam Two 1. A loan of $1000 is being repaid with quarterly payments at the end of each quarter for 5 years at 6% convertible quarterly. Find the outstanding loan balance at the end of the second year and nd the amount of principal in t
School: UCF
Course: Probability Theory 1
Pens~ Name: P robability T heory. Q uiz 2. 1. (10 points). Mike is choosing 5 fruit from a basket with 7 oranges, 6 apples, 5 pears and 8 bananas. I f his choice is completely random (all fruit are equally likely), what is t he probability t hat a) Mike c
School: UCF
Course: Probability
P~Bkr Name: P robability. T est 1 . 1. (20 points). Ann bought three books, $30 each, for her three nieces in Russia. She knows the probability of a parcel being lost is about 1 /3. I f she sends all three in one parcel, i t will cost $20, if she sends th
School: UCF
Course: Probability
Pens~ Name: P robability T heory. Q uiz 2. 1. (10 points). Mike is choosing 5 fruit from a basket with 7 oranges, 6 apples, 5 pears and 8 bananas. I f his choice is completely random (all fruit are equally likely), what is t he probability t hat a) Mike c
School: UCF
Course: Statistical Methods
Chapter 4 Discrete Random Variables 1. Suppose a bookie will give you $6 for every $1 you risk if you pick the winner in 3 ballgames. For every $1 bet, you either lose $1 or gain $6. What is the bookies expected earnings per dollar wagered? 2. Given the f
School: UCF
Course: Statistics For Engineers
Assignment 7b Due Nov 11 at 11:59pm Points 100 Questions 11 Available Nov 5 at 9am - Nov 11 at 11:59pm 7 days Time Limit 120 Minutes Instructions You have 2 hours to complete this Assignment. Numerical answers must be given to 4 decimal places. Optional:
School: UCF
Course: Theory Of Interest
Stat 4183 Fall 2011 Home work # 7 Name Warning: only a sketch of solutions. please spend some time nishing up the numerical calculation, which is required in the exam! 1. An investor enters into an agreement to contribute $6000 immediately and $1000 at th
School: UCF
Course: Statistics For Engineers
W I {Nﬁﬁ n WhH-Tckk‘ HOWKit Z L), 1‘1 ' 3'4 SW33; AOH3éloq 1&7, 7954 \/\/ V\/ l 7. E W 3: (ELLKSWQ, r541, 3M2! \QZEIMEZ, LI 132, ‘1 (ZS zaHpM/l 37,1 4, 32/!ZwslzélEtﬂmﬂBl B) A in! L) Hem RB. {~\: %\%Z‘k,l3qz,(%1’6,l%z% AUV§ ék3‘121l32‘rﬂ4szlﬁzb’l ZSI
School: UCF
Course: Statistics For Engineers
56 CHAPTER Probability 2 Sometimes A and B have no outcomes in common, so that the intersection of A and B contains no outcomes. I Let 0 denote the null event (the event consisting of no outcomes whatsoever). When A n B = 0, A and B are said to be mutuall
School: UCF
Course: Statistics For Engineers
:N 0.13:9: O8 > 0:0, 03258 $0328 8088. 5: 0: 55:02 5: :0: :8 0:0:m: 885000 8 5:8 08: : 0:88 0:002. - :0: 8: 0:0: 0:. 5000 85:55:05. 5 0:58: : _. 20 2: 80 :02 50 0:85: 880:0: 0m : 8:030: 0m 9:000 8852500 2: 55: «8: 50 858: 58550:. I 98 0:0 :0858 $550
School: UCF
Course: Statistical Methods
Sampling Lab The purpose of this laboratory exercise is to familiarize yourself with the different sampling techniques. You need one page from a movie listing (like contained in TV-Guide). Note, if you actually use TV Guide, then you need to use two facin
School: UCF
Course: Data Preparation
Lab for Statistical Decision Theory Data Explanation: Same as the data used in Practicum 3 Problem 1 For the target TAR1, (a) Repeat Practicum 2 with exactly the same options used in the practicum. (b) Write down the profit equation for each decision wher
School: UCF
Course: Data Preparation
Lab for Statistical Decision Theory Data Explanation: Same as the data used in Practicum 3 Problem 1 For the target TAR1, (a) Repeat Practicum 2 with exactly the same options used in the practicum. (b) Write down the profit equation for each decision wher
School: UCF
Course: Principles Of Statistics
Q: Construct a Confidence Interval mean, average Is sample size (n) 30? START proportion, percentage p Whats the parameter of interest? (6.3) Confidence Interval for p Ye s No (6.1) Confidence Interval for Large Sample Size If is unknown, use s Yes Is kn
School: UCF
Course: Principles Of Statistics
"It is a characteristic of wisdom not to do desperate things." 1 - Henry David Thoreau How To Do Well In This Class (or Surviving Statistics for Dummies) 1. Pay attention during lectures. 2. Review your lecture notes after each class, it doesn't have to b
School: UCF
Course: Statistical Methods
APPLICATION:Descriptive:(#&graphical)patterns,sum.Info,identifypop/sample(collectionsofexperimentalunits),convenientformex)piechartw/#s Inferential:(sampledata)stereotype,estimate,makedecisions/predictions,generalizationsaboutlargesetofdataex)Mapw/colorst
School: UCF
Course: Statistical Methods
STA2023.0004Exam1PrepSheet Date:FridaySeptember13,2013@2:30pm Location:THEUSUALCLASSROOM Format:18multiplechoicequestionsworth10pts.each Time:50minutes Pleasebring: Pencil Eraser RaspberryScantron Calculator StudentID 5x8indexcard(maxsizechangedfrom
School: UCF
Course: STATISTICS
STA1001C Student Course Materials Course Materials Table of Contents Chapter 1 Course Launch, What is Statistics, and Mind Set Activity Supplement Community Contract . 3 Lesson 1.1: The Statistical Analysis Process. 5 Supplement 1.2: Mindset Activity .17
School: UCF
Course: STATISTICS
Tuesday and Thursday Schedule Week 1 Tuesday Sept 1 Scheduled Lesson 1.1 Thursday Sept 3 Scheduled Lesson 1.1 (Lesson 1.2- Talk) Lesson 2.2 Lesson 2.4 Week 2 Week 3 Sept 8 Sept 15 Lesson 2.1 Lesson 2.3 Sept 10 Sept 17 Week 4 Sept 22 Lesson 2.4 Lesson 2.5
School: UCF
Course: Statistics For Engineers
STA 3032Probability and Statistics for Engineers Fall 2015 Course Semester Schedule: Date 1 August 25, 2015 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 August 27, 2015 September 1, 2015 September 3, 2015 September 8, 2015 Septemb
School: UCF
Course: Statistics For Engineers
STA 3032Probability and Statistics for Engineers Fall 2015 Instructor Contact Instructor Office Office Hours Phone E-mail Basma Selim, Ph.D. Lake Nona COM 313 Tu and Th 5:30 - 6:00 pm and 7:15 7:45pm Other times by appointment 407.266.1411 basma.selim@ucf
School: UCF
Course: Statistical Methods
STA 2023 Statistical Methods I Instructor: Mrs. Susan Schott E-Mail: sschott@ucf.edu Note: I will only respond to knights e-mail Phone: Office: Spring 2013 823-2832 (best way to reach me is email) CCII 210C Webcourses: Access to the webcourse component ca
School: UCF
Course: Principles Of Statistics
PRINCIPLES OF STATISTICS SPRING 2015 CHINESE PROVERB: Tell me, Ill forget. Show me, I may remember. But involve me and Ill understand. COURSE: STA 2014 - Principles of Statistics INSTRUCTOR: Vu Nguyen (Tom) OFFICE: Technology Commons II, 212F E-MAIL: Vu.N
School: UCF
Course: Theory Of Interest
Stat 4183 Fall 2012 Exam One Name 1. The present value of two payments of $100 each to be made at the end of n years and 2n years is $80. If i = 0.10, nd n. 100(v n + v 2n ) = 80; 2. It is known that a8 a12 = a3 +sx ay + s z for any eective interest rate
School: UCF
Course: Theory Of Interest
Stat 4183 Fall 2012 Exam Two Name 1. A loan of $1000 is being repaid with quarterly payments at the end of each quarter for 5 years at 6% convertible quarterly. Find the outstanding loan balance at the end of the second year and nd the amount of principal
School: UCF
Course: STATISTICAL METHODS I
CHAPTER 8 Homework:1,3,7,17,23,26,31,33,39,41,47,49,55,61, 67,69,100,107,109ab Sec 8.1: Elements of a hypothesis Testing: (1) Set up hypotheses A hypothesis is simply a statement about a population parameter, e.g. the population mean. There are two types
School: UCF
Course: Statistical Methods
Chapter 4 Discrete Random Variables 1. Suppose a bookie will give you $6 for every $1 you risk if you pick the winner in 3 ballgames. For every $1 bet, you either lose $1 or gain $6. What is the bookies expected earnings per dollar wagered? 2. Given the f
School: UCF
Course: Statistical Methods
Chapter 4 Discrete Probability 4.1Discretevs.ContinuousRandomVariables Distributions 4.2DiscreteProbabilityDistributions 4.3ExpectedValueforDRVs 4.4BinomialDistribution formula STA2023_Inghram_Fall2013 1 4.1 Discrete vs. Continuous Random Variables Twotyp
School: UCF
Course: DERIVATIVE PRICING
Stat 5101 (Geyer) Fall 2011 Homework Assignment 1 Due Wednesday, September 14, 2011 Solve each problem. Explain your reasoning. No credit for answers with no explanation. 1-1. For each of the following functions h either determine a constant c such that c
School: UCF
Course: Principles Of Statistics
STA 2014C.006 1. Find these probabilities: a. P( Z > -0.74 ) b. P( Z < 0 ) c. P( -2.15 < Z 1.55 ) d. P( Z = 2.38) Chapter 5 Review 0.7704 0.5 0.9237 0 2. Assume the random variable X is normally distributed, with mean =74 and =8. Find the following probab
School: UCF
Course: Data Preparation
Data Visualization Section 1 Introduction Section 2 Numerical Measurements for One Variable Numerical Measures for Location Parameter Numerical Measures for Scale Parameter Section 3 Graphical Methods for One Variable Histogram Box Plot Density Plot
School: UCF
Course: Statistical Methods
Crystal Jenkins STA 2023 TR 10:00am Case Study Appalachian Bear Rescue (ABR) is a not-for-profit organization located near the Great Smoky Mountains National Park. ABR's programs include the rehabilitation of orphaned and injured black bears, as well as r
School: UCF
Course: Statistical Methods
Crystal Jenkins STA 2023 TR 10:00am Case study pg 451 In Exercises 14, perform a two-sample z-test to determine whether the mean weight losses of the two indicated groups are different. For each exercise, write your conclusion as a sentence. Use 0.05. 1.
School: UCF
Course: Data Preparation
Using SAS Enterprise Miner 6.2 - SEMMA 1. Opening Enterprise Miner 1.1 Initiating Project 1.2 Starting Project and Diagram 1.3 Initiating Data Exploration 1.4 Setting Prior Probability and Profit Matrix 2. SEMMA Data Mining Process Used in Enterprise Mine
School: UCF
Course: Statistical Methods 1
A. ./\ AA.) 1-H(0.\3~5] xv 2x3 pix) 'M LeftOAaS) 4v t5)? £0.23an a. 2x39) : 3b.0-\_81/ v , I v Please show all work to receive full credit. With uestions concernin the Normal Distribution ou must grovide a gicture. L010 1. Based on past results, t
School: UCF
Course: Statistics For Engineers
STA 3032 - Probability and Statistics for Engineers Spring Semester, 2008 Instructor: Dr. Nabin Sapkota Office: ENGR2, Room 429 Office Hours: Monday 9:00-12:00. Thursday 3:00-6:00 pm Phone: 407-823-5644 Email: nsapkota@mail.ucf.edu Text: Johnson, Richard.
School: UCF
Course: Statistics For Engineers
Assignment 7b Due Nov 11 at 11:59pm Points 100 Questions 11 Available Nov 5 at 9am - Nov 11 at 11:59pm 7 days Time Limit 120 Minutes Instructions You have 2 hours to complete this Assignment. Numerical answers must be given to 4 decimal places. Optional:
School: UCF
Course: Probability
SOLUTIONS. CHAPTER 6 Section 6.1 Section 6.2 Section 6.3
School: UCF
Course: Probability
SOLUTIONS. CHAPTER 7 Section 7.1 Section 7.2 \ Section 7.3
School: UCF
Course: Statistical Methods II
Solution for Practice Problems: Problem 14.3 1. Let F ( x ) be the distribution for population A and G ( y ) the distribution for population B. Then the hypotheses should be stated as the following H0 : F ( x ) = G ( y ) Ha : F ( x ) > G ( y ) 2. Sample A
School: UCF
Course: Probability
SOLUTIONS. CHAPTER 8 Section 8.1 Section 8.2 Section 8.3 Section 8.4
School: UCF
Course: Probability Theory 1
SOLUTIONS. CHAPTER 10 Section 10.1 Section 10.2
School: UCF
Course: Probability Theory 1
SOLUTIONS. CHAPTER 9 Section 9.1 Section 9.2
School: UCF
Course: Probability Theory 1
SOLUTIONS. CHAPTER 8 Section 8.1 Section 8.2 Section 8.3 Section 8.4
School: UCF
Course: Probability
SOLUTIONS. CHAPTER 4 Section 4.2 Section 4.3 Section 4.4 Section 4.5
School: UCF
Course: Statistical Methods II
Solution for Practice Problems: Problem 14.5 1. T+ 43 2. T 152 3. T+ 371 Problem 14.6 H0 : The distributions for the schools, A and B are identical Ha : They are different Twin pair 1 2 3 4 5 6 A 65 72 86 50 60 81 B 69 72 74 52 47 72 D -4 0 12 -2 13 9 |D|
School: UCF
Course: Data Preparation
InClass Exercise: Getting Familiar with SAS Enterprise Miner (adapted from Applied Analytics using SAS Enterprise Miner, SAS Institute, Cary, NC. 2010) Creating a SAS Enterprise Miner Project A SAS Enterprise Miner project contains materials related to
School: UCF
Course: Statistics
Stat 516 1. Homework 1 Solutions (a) GAUUACACGUGCCUUGGA (b) asp tyr thr cys gly (c) The amino acid cys would change to the stop codon. Thus, we would end up with the sequence asp tyr thr. 2. See slide 9 of slide set number 3. 3. The notation calls for one
School: UCF
Course: Statistical Methods
Chapter 4 Discrete Probability 4.1Discretevs.ContinuousRandomVariables Distributions 4.2DiscreteProbabilityDistributions 4.3ExpectedValueforDRVs 4.4BinomialDistribution formula STA2023_Inghram_Fall2013 1 4.1 Discrete vs. Continuous Random Variables Twotyp
School: UCF
Course: Principles Of Statistics
STA 2014C.006 1. Find these probabilities: a. P( Z > -0.74 ) b. P( Z < 0 ) c. P( -2.15 < Z 1.55 ) d. P( Z = 2.38) Chapter 5 Review 0.7704 0.5 0.9237 0 2. Assume the random variable X is normally distributed, with mean =74 and =8. Find the following probab
School: UCF
Course: Statistical Methods 1
A. ./\ AA.) 1-H(0.\3~5] xv 2x3 pix) 'M LeftOAaS) 4v t5)? £0.23an a. 2x39) : 3b.0-\_81/ v , I v Please show all work to receive full credit. With uestions concernin the Normal Distribution ou must grovide a gicture. L010 1. Based on past results, t
School: UCF
Course: Theory Of Interest
‘ 7- n l M 1"} F; g f_ T”: «Trim ﬁght,“ i 5., @195? L a“ CA “W” N w a; aw” “i” w j. A : s' ~ 5 155 I: , s i H, L {a 2M3; L Wm} €52 ma a; m k 5g L, Q, “if” : ? «(k/“7L3 I »* V‘h‘V "ﬂag i‘ ’ji:~&fm*:"i O iiyﬁxagﬁw ‘v a :p ’ “In; F L (‘I id” “JUL g", r 4’ A
School: UCF
Course: STATISTICS
Chapter 7: Estimation Using Confidence Intervals Two Basic Types of Inferences 1) Estimation- guess the value of the parameter a. Point Estimate- one value Ex) guess =7 b. Interval estimate- collection of values Ex) guess that is between 6 and 8 2) Test
School: UCF
Course: STATISTICS
Chapter 5: Continuous Random Variables- random variables that can take on any value in an interval Section 5.1) Probability Distribution for a Continuous Random Variable The probability Distribution of a continuous x is a smooth curve located over the val
School: UCF
Course: Statistical Methods
Spg 13 Final Exam Review Problem This is a list of suggested review problems for the cumulative part of the final exam. Your studying should not be limited to doing only these problems. I would also suggest going over the class notes to prepare for the ex
School: UCF
Course: Principles Of Statistics
NAME: Calculate: 1. 4. 7. 10. 11. 12. 13. 9 + 12 3 = 2. 76 10 3 4 = 3. 72 = 8. 256 = 9. 8 = 35 29 = 5. Find the square root of 25 Find the square of 4 Consider the fraction a. 3 5 = 2 9 6. (6 3)(6 + 3) = 2 3 3 7 13 STA 2014C Spring 2015 Math Review = 27
School: UCF
Course: Principles Of Statistics
STA 2014C.006 Chapter 6 Review 1. In a random sample of 28 sports cars, the average annual fuel cost was $2218 and the standard deviation was $523. Assume fuel costs are normally distributed. We want to construct a 90% confidence interval for the populati
School: UCF
Course: Principles Of Statistics
Cumulative Review for Exam 1 1. What is the difference between a parameter and a statistic? 2. Match the following: a. Population variance b. Population standard deviation c. Population mean d. Sample variance e. Sample mean f. Sample standard deviation i
School: UCF
Course: Principles Of Statistics
STA 2014C.006 Chapter 4 Review 1. Differentiate between discrete and continuous random variables. Give 2 examples of each type. 2. The following distribution represents the probability of the number of people flying to Indianapolis on a given month, in th
School: UCF
Course: Statistical Methods
ChapterSeven:StatisticalInference 16:06 StatisticalInferenceistheprocessofmakinguseofdatatodrawconclusions.Some commonformsofinferenceare Pointestimateaparticularvaluethatbestapproximatesaparameter(e.g.,mean, sigma,p) Confidenceintervalsandinterval(L,U)co
School: UCF
Course: Statistical Methods
ChapterFiveNotes 17:13 Acontinuousrandomvariablecanassumeanynumericalvaluewithinsomeintervalor intervals. Thegraphoftheprobabilitydistributionisasmoothcurveoftencalledaprobability densityfunctionwhichsatisfies f(x)greaterthanofequalto0forallxand Totalarea
School: UCF
Course: Statistical Methods
ChapterSixNotes 16:41 SamplingDistributionofSampleMean Takearandomsampleofnobservationsfromapopulationwithmeanandvarianceand calculatethemean.Yougetonevalueformean. Repeattheaboveprocessmantimesandgetmanyvaluesformean. Theprobabilitydistributionofthemeanv
School: UCF
Course: Statistical Methods
Chapter5&6:NormalandSamplingDistributions 16:24 TreadLifeofTires Thetreadlife(x)oftiresfollownormaldistributionwithmean=60,000ands.d.=6,200. Themanufacturerguaranteesthetreadlifeforthefirst52,560. Whatproportionoftireslastatleast55,000miles? p(Xgreatertha
School: UCF
Course: Statistical Methods
StatisticsChapter3Notes 17:06 ChapterThreeNotes Experiment Processthatprovidesresultsthroughobservation,(oftencalledanoutcomeorsample point)whichcannotbedeterminedwithcertaintyinadvanceoftheexperiment.Not 100%certainty. Examples Tossacoin(H,T) Throwadie(1
School: UCF
Course: Statistical Methods
ReviewQuestions 17:49 Ahumangenecarriesacertaindiseaseformthemothertothechildwithaprobability rateof60%.Supposeafemalecarrierofthegenehasthreechildren.Assumethatthe infectionsofthethreechildrenareindependentofoneanother. Findtheprobabilitythatnoneofthechi
School: UCF
Course: Statistical Methods
ChapterFourNotes 17:38 RandomVariablesandProbabilityDistribution DiscreterandomVariable(DRV) DRVisafunctionorrulethatassignsanumericalvaluetoeachsamplepointofthe samplespaceofanexperiment. DRVisoftendenotedbyX,Y,Zetc. Probabilitydistribution Isagraphorata
School: UCF
Course: Statistical Methods
STA2023 StageII:RandomVariablesandtheirProbabilityDistributions Chapter4andChapter5:Allprobabilitydistributions,allthetime. 12 10 8 Weig htsofAdultMale Greyhounds ProbabilityDistribution GhostlySightingsperWeekat theHauntedSt.Augustine Lighthouse =74lbs.=
School: UCF
Course: Statistical Methods
Chapter6 SamplingDistributions 6.1 ConceptofaSamplingDistribution 6.2 PropertiesofSamplingDistributions 6.3 TheSamplingDistributionoftheSampleMeanand theCentralLimitTheorem Motivation Inmostpracticalapplications,wewontknowhowarandomvariable behaves. Inthe
School: UCF
Course: Logistic Regression
Binary Outcome Variable Binary Data The observed value of a variable Y for each unit falls into one of two categories (success/failure; alive/dead; positive/negative) conveniently coded as 0 (=nonevent) and 1 (=event). Observed outcome y from each unit
School: UCF
Course: Logistic Regression
Logistic Regression: Sampling Methods and Analysis Logistic Simple Random Sample An observation on a binary outcome variable y and p independent variables x1, x2, xp are obtained from each of n subjects or units selected completely at random from a popu
School: UCF
Course: Statistics For Engineers
CHAPTER 8 INFERENCES CONCERNING VARIANCES SPRING 2008 STA 3032 A (1- ) 100% Confidence Interval for a population variance ( ) 2 Chi-Square distribution: 2 (n 1) s 2 2 / 2 (n 1)s 2 2 (n 1) s 2 2 (21 / 2) where 2 / 2 and (21 / 2) are
School: UCF
Course: Data Preparation
Data Visualization Section 1 Introduction Section 2 Numerical Measurements for One Variable Numerical Measures for Location Parameter Numerical Measures for Scale Parameter Section 3 Graphical Methods for One Variable Histogram Box Plot Density Plot
School: UCF
Course: Principles Of Statistics
Chapter Outline 1.1 An Overview of Statistics 1.2 Data Classification 1.3 Experimental Design 1 of 61 Section 1.1 An Overview of Statistics 2 of 61 Section 1.1 Objectives Define statistics Distinguish between a population and a sample Distinguish betwe
School: UCF
Course: Statistical Methods
STATISTICS AND THE TI-84 Lesson #11 Inferential Statistics: Two populations 1.Inference:comparing twopopulation means Exercise 1.There aretwopopulations.Asample ofsize120from oneofthepopulations gave a mean of15andastandard deviation of1.3.Asample ofsize8
School: UCF
Course: Statistical Methods
Chapter6 SamplingDistributions 6.1 ConceptofaSamplingDistribution 6.2 PropertiesofSamplingDistributions 6.3 TheSamplingDistributionoftheSampleMeanand theCentralLimitTheorem Motivation Inmostpracticalapplications,wewontknowhowarandomvariable behaves. Inthe
School: UCF
Course: Regression Analysis
Examples and Multivariate Testing Lecture XXV I. Examples. A. B. Example 9.6.1 (mean of a binomial distribution) Assume that we want to know whether a coin toss is biased based on a sample of ten tosses. Our null hypothesis is that the coin is fair ( H 0
School: UCF
Course: Regression Analysis
Composite Tests and The Likelihood Ratio Test Lecture XXIV I. Simple Tests Against a Composite A. Mathematically, we now can express the tests as testing between H 0 : 0 against H1 : 1 , where 1 is a subset of the parameter space. Given this specification
School: UCF
Course: Regression Analysis
Type I and Type II Errors and the Neyman -Pearson Lemma: Lecture XXIII I. Introduction A. B. C. D. In general there are two kinds of hypotheses: one type concerns the form of the probability distribution (i.e. is the random variable normally distributed)
School: UCF
Course: Regression Analysis
Bayesian Estimation and Confidence Intervals Lecture XXII I. Bayesian Estimation A. Implicitly in our previous discussions about estimation, we adopted a classical viewpoint. 1. We had some process generating random observations. 2. This random process wa
School: UCF
Course: Regression Analysis
Confidence Intervals Lecture XXI I. Interval Estimation A. B. As we discussed when we talked about continuous distribution functions, the probability of a specific number under a continuous distribution is zero. Thus, if we conceptualize any estimator, ei
School: UCF
Course: Regression Analysis
Concentrated Likelihood Functions, Normal Equations, and Properties of Maximum Likelihood: Lecture XX I. Concentrated Likelihood Functions A. In the last lecture I introduced the concept of maximum likelihood using a known variance normal distribution of
School: UCF
Course: Regression Analysis
Sufficient Statistics Lecture XIX I. Data Reduction A. References: Casella, G. and R.L. Berger Statistical Inference 2nd Edition, New York: Duxbury Press, Chapter 6 Principles of Data Reduction. Pp 271-309. Hogg, R.V., A. Craig, and J.W. McKean Introducti
School: UCF
Course: Regression Analysis
Mean Squared Error and Maximum Likelihood Lecture XVIII I. Mean Squared Error A. As stated in our discussion on closeness, one potential measure for the goodness of an estimator is 2 E ^ where ^ is the estimator and is the true value. In the preceding exa
School: UCF
Course: Regression Analysis
Definition of Estimator and Choosing among Estimators: Lecture XVII I. What is An Estimator? A. In the next several lectures we will be discussing statistical estimators and estimation. The book divides this discussion into the estimation of a single numb
School: UCF
Course: Regression Analysis
Empirical Examples of the Central Limit Theorem: Lecture XVI I. Back to Asymptotic Normality A. The characteristic function of a random variable X is defined as X t E eitX E cos tX i sin tX E cos tX iE sin tX Note that this definition parallels the def
School: UCF
Course: Regression Analysis
Limits and the Law of Large Numbers Lecture XV I. Almost Sure Convergence A. B. White, Halbert. Asymptotic Theory for Econometricians (New York: Academic Press, 1984). Chapter II. Let represent the entire random sequence Zt . As discussed last time, our i
School: UCF
Course: Regression Analysis
Large Sample Theory Lecture XIV I. Basic Sample Theory A. The problems set up is that we want to discuss sample theory. 1. First assume that we want to make an inference, either estimation or some test, based on a sample. 2. We are interested in how well
School: UCF
Course: Regression Analysis
Bivariate and Multivariate Normal Random Variables Lecture XIII I. Bivariate Normal Random Variables A. Definition 5.3.1. The bivariate normal density is defined by f x, y 2 exp X 1 Y 1 1 2 2 X X 2 Y Y x 2 y 2 1 2 x X X y Y Y B. Theorem 5.3.1. Let X , Y h
School: UCF
Course: Regression Analysis
Normal Random Variables Lecture XII I. Univariate Normal Distribution. A. Definition 5.2.1. The normal density is given by 1 x 2 1 f x exp x , 0 2 2 2 When X has the above density, we write symbolically X ~ N , 2 . B. Theorem 5.2.1. Let X be N , 2 . The
School: UCF
Course: Regression Analysis
Binomial Random and Normal Random Variables: Lecture XI I. Bernoulli Random Variables A. The Bernoulli distribution characterizes the coin toss. Specifically, there are two events X 0,1 with X 1 occurring with probability p . The probability distribution
School: UCF
Course: Regression Analysis
Moment Generating Functions Lecture X I. Moment Generating Functions A. Definition 2.3.3. Let X be a random variable with cumulative distribution function F X . The moment generating function (mgf) of X (or F X ), denoted M X t , is MX t E etX provided th
School: UCF
Course: Regression Analysis
Moments of More than One Random Variable Lecture IX I. Covariance and Correlation A. Definition 4.3.1: Cov X , Y E X E X XE Y Y E Y E X E Y E X E Y E XY E XY E XY E X Y E X E Y E X E Y E X E Y 1. Note that this is simply a generalization of the standard v
School: UCF
Course: Regression Analysis
Mean and Higher Moments Lecture VII I. Expected Value A. Definition 4.1.1. Let X be a discrete random variable taking the value xi with probability P xi , i 1, 2, . Then the expected value (expectation or mean) of X , denoted E X , is defined to be E X ab
School: UCF
Course: Regression Analysis
An Applied Sabbatical: Lecture VII I. Basic Crop Insurance A. Nelson, Carl H. "The Influence of Distributional Assumptions on the Calculation of Crop Insurance Premia." North Central Journal of Agricultural Economics 12(1)(Jan 1990): 718. 1. 2. 3. 4. In t
School: UCF
Course: Regression Analysis
Derivation of the Normal Distribution: Lecture VI I. Derivation of the Normal Distribution Function A. The order of proof of the normal distribution function is to start with the standard normal: 1 x2 2 f x e 2 1. First, we need to demonstrate that the di
School: UCF
Course: Regression Analysis
Distribution Functions for Random Variables: Lecture VI I. Bivariate Continuous Random Variables A. Definition 3.4.1. If there is a nonnegative function f x, y defined over the whole plane such that Px1 X x2 , y1 Y y 2 y2 y1 f x, y dx dy x2 x1 for any x
School: UCF
Course: Regression Analysis
Random Variables and Probability Distributions: Lecture IV I. Conditional Probability and Independence A. In order to define the concept of a conditional probability it is necessary to discuss joint probabilities and marginal probabilities. 1. A joint pro
School: UCF
Course: Regression Analysis
Probability Theory and Measure: Lecture III I. Uniform Probability Measure: A. I think that Bieren's discussion of the uniform probability measure provides a firm basis for the concept of probability measure. 1. First, we follow the conceptual discussion
School: UCF
Course: Regression Analysis
Basic Axioms of Probability: Lecture II I. Basics of Probability A. Using the example from Birenens Chapter 1: Assume we are interested in the game Texas lotto (similar to Florida lotto). 1. In this game, players choose a set of 6 numbers out of the first
School: UCF
Course: Regression Analysis
Introduction to Statistics, Probability and Econometrics: Lecture I I. The basic question to be answered on the first day is: What are we going to study over the next fifteen weeks and how does it fit into my graduate studies in Food and Resource Economic
School: UCF
Course: Regression Analysis
Generalized Method of Moments Estimator Lecture XXXII I. Basic Derivation of the Linear Estimator A. Starting with the basic linear model yt xt 0 ut 0 where yt is the dependent variable, xt is the vector of independent variables, is the parameter vector,
School: UCF
Course: Theory Of Interest
Stat 4183 Fall 2012 Exam One Name 1. The present value of two payments of $100 each to be made at the end of n years and 2n years is $80. If i = 0.10, nd n. 100(v n + v 2n ) = 80; 2. It is known that a8 a12 = a3 +sx ay + s z for any eective interest rate
School: UCF
Course: Theory Of Interest
Stat 4183 Fall 2012 Exam Two Name 1. A loan of $1000 is being repaid with quarterly payments at the end of each quarter for 5 years at 6% convertible quarterly. Find the outstanding loan balance at the end of the second year and nd the amount of principal
School: UCF
Course: Theory Of Interest
Stat 4183 Fall 2012 Exam Two 1. A loan of $1000 is being repaid with quarterly payments at the end of each quarter for 5 years at 6% convertible quarterly. Find the outstanding loan balance at the end of the second year and nd the amount of principal in t
School: UCF
Course: Probability Theory 1
Pens~ Name: P robability T heory. Q uiz 2. 1. (10 points). Mike is choosing 5 fruit from a basket with 7 oranges, 6 apples, 5 pears and 8 bananas. I f his choice is completely random (all fruit are equally likely), what is t he probability t hat a) Mike c
School: UCF
Course: Probability
P~Bkr Name: P robability. T est 1 . 1. (20 points). Ann bought three books, $30 each, for her three nieces in Russia. She knows the probability of a parcel being lost is about 1 /3. I f she sends all three in one parcel, i t will cost $20, if she sends th
School: UCF
Course: Probability
Pens~ Name: P robability T heory. Q uiz 2. 1. (10 points). Mike is choosing 5 fruit from a basket with 7 oranges, 6 apples, 5 pears and 8 bananas. I f his choice is completely random (all fruit are equally likely), what is t he probability t hat a) Mike c
School: UCF
Course: Probability
P robability T heory. Q uiz 1 . 1. (10 points). Letters of the word " PEPPER" were scrambled together and the new word was formed. a) W hat is the probability t hat in the new word both E's are together? b) W hat is th~ probability t hat E 's are together
School: UCF
Course: Statistics
Name: _ Statistics 516 Exam 1 March 3, 2010 1. Suppose a test for differential expression is conducted for each of 100 genes. The following table provides information about the observed p-values. Range Number of p-values [0.0-0.1] 42 (0.1-0.2] 10 (0.2-0.3
School: UCF
Course: Theoretical Statistics II
STA 6327 STATISTICAL THEORY II EXAM I - SOLUTIONS PROBLEM 1 Note that Fn t P n X n t X n t P X n n t 1 P X n n t 1 P X n n t 1 FX n n n t 1 , n n t 1 1 , n if 0 t 0 t n . If we let 1 , then n n t t Fn X t 1 1 1 e , n n n if 0 t . T
School: UCF
Course: Theoretical Statistics II
STA 6327 STATISTICAL THEORY II EXAM I - SOLUTIONS PROBLEM 1 Note that Fn t P n X n t X n t P X n n t 1 P X n n t 1 P X n n t 1 FX n n n t 1 , n n t 1 1 , n if 0 t 0 t n . If we let 1 , then n n t t Fn X t 1 1 1 e , n n n if 0 t . T
School: UCF
Course: Theoretical Statistics
STA 6326 FINAL EXAM - SOLUTIONS PROBLEM 1 Let g x x 2 . Then d g x 2 x 0 , if 0 x . Hence g x is monotonic on the support of X. dx Therefore, g 1 y y1 2 and d 1 1 g y y 1 2 . dy 2 Furthermore, the support of Y is y : y x 2 , 0 x 0, and for y fY y f X g
School: UCF
Course: Theoretical Statistics
STA 6326 EXAM III - SOLUTIONS PROBLEM 1 Define X = # of admissions to the emergency ward in a day Consequently, we need to find the smallest b, such that P X b 0.50 where 2 x e 2 x! x 0 b P X b 1 P X b 1 Computing P X b for several values of b produced:
School: UCF
Course: Theoretical Statistics
STA 6326 EXAM II - SOLUTIONS PROBLEM 1 2 2 1 E X x 3 dx 2 2 2 dx 2 2 x 1 2 2 1 2 x x PROBLEM 2 The mgf of X is given by M X (t ) E (etX ) etx e x dx e e t 1 x dx e t 1 x e t 1 e t 1 e t 1 e t 1 t where the integral converges whenever t 1 0 t 1 . PROBLE
School: UCF
Course: Theoretical Statistics
STA 6326 EXAM I - SOLUTIONS PROBLEM 1 (a) Define S = cfw_all samples of size 2 of n white & m black balls drawn w/o replacement and A = cfw_all samples of size 2 drawn w/o replacement in which both are black or both are white The number of elements in S i
School: UCF
Course: Theoretical Statistics 1
STA 6326 EXAM II - SOLUTIONS PROBLEM 1 2 2 1 E X x 3 dx 2 2 2 dx 2 2 x 1 2 2 1 2 x x PROBLEM 2 The mgf of X is given by M X (t ) E (etX ) etx e x dx e e t 1 x dx e t 1 x e t 1 e t 1 e t 1 e t 1 t where the integral converges whenever t 1 0 t 1 . PROBLE
School: UCF
Course: Theoretical Statistics 1
STA 6326 EXAM I - SOLUTIONS PROBLEM 1 (a) Define S = cfw_all samples of size 2 of n white & m black balls drawn w/o replacement and A = cfw_all samples of size 2 drawn w/o replacement in which both are black or both are white The number of elements in S i
School: UCF
Course: Statistics For Engineers
Bin 2 4 6 8 10 12 14 16 18 More Frequency 7 12 30 67 84 92 55 36 17 0 1.5 3.5 5.5 7.5 9.5 11.5 13.5 15.5 17.5 0.0175 0.03 0.075 0.1675 0.21 0.23 0.1375 0.09 0.0425 1 Histogram 100 80 60 Frequency Column B 40 20 0 2 4 6 8 10 12 14 16 18 More Bin Relative H
School: UCF
Course: Theortical Statistics
STA 6327 STATISTICAL THEORY II EXAM III PROBLEM 1 Note that n n L x exp ln xi n exp ln xi i 1 xi xi i 1 n i 1 while : 0 and 0 : 1 . Hence, x sup L x o sup L x L 1 x Lx 1 n n exp 1 ln xi n xi i1 i 1 n n ln x i i 1 n exp n n n xi ln xi
School: UCF
Course: Theortical Statistics
STA 6327 STATISTICAL THEORY II EXAM II PROBLEM 1 Note that the likelihood function is given by n L | x 1 xi 1 n i 1 n x i 1 i Hence, the natural log of the likelihood is n ln L | x n ln 1 ln xi . i 1 Consequently, taking a derivative with respect to , s
School: UCF
Course: Theortical Statistics
STA 6326 EXAM II - SOLUTIONS PROBLEM 1 E Y E 2 X n 1 5 2 x 0 x 6 6 x n x n 2 5 x 0 x 6 6 x n 2 5 6 6 7 6 n n PROBLEM 2 The mgf of X is given by M X (t ) E (etX ) etx e x dx ln e 1t x dx ln 1 t 1 t 1 t e 1t x e1t ln 1t ln n x n x 2
School: UCF
Course: Theortical Statistics
STA 6327 STATISTICAL THEORY II EXAM I - SOLUTIONS PROBLEM 1 Note that Fn t P n 1 X n t 1 X n t P 1 X n n t 1 P 1 X n n t 1 P X n 1 n t 1 FX n 1 n n t 1 1 , n if 0 1 t 1 0 t n . If we let 1 , then n n t Fn 1 X t 1 1 1 e t , n n n if 0 t
School: UCF
Course: Theortical Statistics
STA 6326 EXAM I - SOLUTIONS PROBLEM 1 Let Ai i th question is answered correctly and assume that A1 , A2 , , A10 are mutually independent. Furthermore, if the student is guessing, then P Ai 0.20 . Define X = # of questions answered correctly Then 10 j 1
School: UCF
Course: Statistical Methods
Stats: Two Parameter Testing Definitions Dependent Samples Samples in which the subjects are paired or matched in some way. Dependent samples must have the same sample size, but it is possible to have the same sample size without being dependent. Independ
School: UCF
Course: Statistical Methods
Stats: Test for Independence In the test for independence, the claim is that the row and column variables are independent of each other. This is the null hypothesis. The multiplication rule said that if two events were independent, then the probability of
School: UCF
Course: Statistical Methods
Stats: Scheffe' and Tukey Tests When the decision from the One-Way Analysis of Variance is to reject the null hypothesis, it means that at least one of the means isn't the same as the other means. What we need is a way to figure out where the differences
School: UCF
Course: Statistical Methods
Stats: Goodness-of-fit Test The idea behind the chi-square goodness-of-fit test is to see if the sample comes from the population with the claimed distribution. Another way of looking at that is to ask if the frequency distribution fits a specific pattern
School: UCF
Course: Statistical Methods
Stats: F-Test The F-distribution is formed by the ratio of two independent chi-square variables divided by their respective degrees of freedom. Since F is formed by chi-square, many of the chi-square properties carry over to the F distribution. The F-valu
School: UCF
Course: Statistical Methods
Stats: F-Test Definitions F-distribution The ratio of two independent chi-square variables divided by their respective degrees of freedom. If the population variances are equal, this simplifies to be the ratio of the sample variances. Analysis of Variance
School: UCF
Course: Statistical Methods
Stats: Type of Tests This document will explain how to determine if the test is a left tail, right tail, or two-tail test. The type of test is determined by the Alternative Hypothesis ( H1 ) Left Tailed Test H1: parameter < value Notice the inequality poi
School: UCF
Course: Statistical Methods
Chapter 4 Discrete Random Variables 1. Suppose a bookie will give you $6 for every $1 you risk if you pick the winner in 3 ballgames. For every $1 bet, you either lose $1 or gain $6. What is the bookies expected earnings per dollar wagered? 2. Given the f
School: UCF
Course: Statistics For Engineers
Assignment 7b Due Nov 11 at 11:59pm Points 100 Questions 11 Available Nov 5 at 9am - Nov 11 at 11:59pm 7 days Time Limit 120 Minutes Instructions You have 2 hours to complete this Assignment. Numerical answers must be given to 4 decimal places. Optional:
School: UCF
Course: Theory Of Interest
Stat 4183 Fall 2011 Home work # 7 Name Warning: only a sketch of solutions. please spend some time nishing up the numerical calculation, which is required in the exam! 1. An investor enters into an agreement to contribute $6000 immediately and $1000 at th
School: UCF
Course: Statistics For Engineers
W I {Nﬁﬁ n WhH-Tckk‘ HOWKit Z L), 1‘1 ' 3'4 SW33; AOH3éloq 1&7, 7954 \/\/ V\/ l 7. E W 3: (ELLKSWQ, r541, 3M2! \QZEIMEZ, LI 132, ‘1 (ZS zaHpM/l 37,1 4, 32/!ZwslzélEtﬂmﬂBl B) A in! L) Hem RB. {~\: %\%Z‘k,l3qz,(%1’6,l%z% AUV§ ék3‘121l32‘rﬂ4szlﬁzb’l ZSI
School: UCF
Course: Statistics For Engineers
56 CHAPTER Probability 2 Sometimes A and B have no outcomes in common, so that the intersection of A and B contains no outcomes. I Let 0 denote the null event (the event consisting of no outcomes whatsoever). When A n B = 0, A and B are said to be mutuall
School: UCF
Course: Statistics For Engineers
:N 0.13:9: O8 > 0:0, 03258 $0328 8088. 5: 0: 55:02 5: :0: :8 0:0:m: 885000 8 5:8 08: : 0:88 0:002. - :0: 8: 0:0: 0:. 5000 85:55:05. 5 0:58: : _. 20 2: 80 :02 50 0:85: 880:0: 0m : 8:030: 0m 9:000 8852500 2: 55: «8: 50 858: 58550:. I 98 0:0 :0858 $550
School: UCF
Course: Statistics For Engineers
Austin Martratt STA3032 Assignment #1 12. C Distribution of particle sizes seems to be positively skewed. 12.D The above graph shows data that is positively skewed, therefore, by comparison both graphs show data that is positively skewed.
School: UCF
Course: Statistical Methods
Chapter 9 - Two Sample Problems 1. Researchers wanted to test the effectiveness of vitamin C in reducing the number of colds a person contracts. The study had 208 students take vitamin C for a specified period of time and 155 students took a placebo durin
School: UCF
Course: Statistical Methods
Chapter 8 Homework Solutions 4. A type I error rejects Ho when it is true. A type II error accepts Ho when it is false. =P(type I error) =P(type II error) 30. a) Ho: =71 Ha: >71 b) z > 1.645 c) z=3.95 d) There is evidence to indicate the true mean exceeds
School: UCF
Course: Statistical Methods
Chapter 7 One Sample Estimation Problems 1. Suppose we want to estimate the average pulse rate of women. A sample of 40 women had a mean of 76.3 and a standard deviation of 12.5. Construct a 99% confidence interval for the mean pulse rate of all women. 2.
School: UCF
Course: Statistical Methods
STA 2023 Chapter 3 Problems 1. Toss a coin 3 times. Let A= at least one head and let B= at most one tail. Find P(A) and P(B). 2. Six students consist of 4 males and 2 females. If 2 students are randomly selected for a scholarship, what is the probability
School: UCF
Course: Statistical Methods
Chapter 2 Homework Solutions 30. a) frequency b) 14 c) 49 34. 13 14 15 16 17 18 19 29 00 7789 125 08 11 147 66. a) b) c) d) e) f) skewed right skewed left skewed right symmetric skewed right (ages of cars) skewed left 74. No; yes 98. a) 27 to 51 b) .025 c
School: UCF
Course: Statistical Methods
Chapter 6 Homework Solutions 26. No. It will be normal if n is large or if the population of X is normal. 32. a) smallest value is 100 3(1/3) or 99 and the largest is 100 + 3(1/3) or 101 b) 3 standard deviations above or below the mean ( i.e., + or 1) c)
School: UCF
Course: Statistical Methods
Chapter 7 Homework Solutions 8. a) 95% b) 90% 68. SE=1/2 Width 70. True 74. a) 225 b) 267 82. 1,692 124. 1.392 to 1.55 136. 818 c) 99% d) 80% e) 67.78%
School: UCF
Course: Statistical Methods
Chapter 1 Homework Solutions 12. a,b,c,e and f are quantitative d is qualitative 16. a) gpa b) quantitative c) census d) sample e) 100% f) No. Reliability would depend on size of the class and how representative the sample is of the popu;lation. 20. a) sa
School: UCF
Course: Statistical Methods
Chapter 9 Homework Solutions 4. d 12. 14. 38. 116. 130. a) The test statistic is t = -1.646. No is given in the problem, so use .05. RR: Reject Ho if t > 2.052 or if t < - 2.052. Since the test statistic is not in the RR, we cannot show the 2 means differ
School: UCF
Course: Statistical Methods
Chapter 5 Homework Solutions 16. a) 10 b) .05 c) probably not true 26. a) b) c) d) e) f) g) h) .5 - .4279 = .0721 .5 - .4406 = .0594 .4920 - .2486 = .2434 .4750 - .1293 = .3457 .5 .4901 + .4332 = .9233 .9901 .9901 a) b) c) d) e) f) g) h) -1.75 1.96 1.645
School: UCF
Course: Statistical Methods
Chapter 3 Homework Solutions 14. a) There are 36 sample points b) probability of each point is 1/36 c) P(A)= 1/36 P(B)=1/2 P(C)=1/6 P(D)=11/36 P(E)=1/6 34. a) .25 b) .5 c) 0 42. a) .3 b) .6 c) .8 68. a) .12 b) .3 78. a) .789 b) .450 c) No 126. a) 1,000,00
School: UCF
Course: Statistical Methods
Chapter 4 Homework Solutions 16. a) -4, 0, 1, 3 b) 1 c) .7 d) 0 46. -$.70 58. a) .121 b) .034 c) .081 72. .049 128. a) .064 b ).936 c) 1.8, .8485 130. Let x = # of times the vehicle is used in a day. a) .230 b) .143 c) .100
School: UCF
Course: Principles Of Statistics
5.3 EXERCISE 50 LUTIDNS 1. z=D.81 2. z=D.l 3. 2:139 4. 220.84 5. 2211545 IS. 221.01- T. z=1.555 Ii. z=2.DS 9. z=1.4 1|}. 224152 11. 321.1?5 12. 3:13.44 13. 22416? 14. z:D.25 15. z=.5T 16. 220.84 1?. 321138 13. 3:13.25 19. 22058 20. 321.99 21. z=:|:1.64-5
School: UCF
Course: Principles Of Statistics
3. 11}. ll 12 13 14 15 16 11' 13 19. 5.1 EXERCISE SDLUTIONE 1. Answers 1will vary. Q Neither. In a normal distribution, the mean and median are equal. 3.1 4. Points at which the tune changes from mining upward to tuning dounurard; tr o'andp-+ 0' 5. Answer
School: UCF
Course: Principles Of Statistics
11. 4.2 EXERCISE SOLUTIO NS 1. 40 .u : np : (50)(0.4) : 20 03 : Mpg : (503(0.4)(0.5) : 12 a = ti.» = .t(50)(0.4)(0.6) $3.5 .u :np:(124)(0.26):32.2 10. 03 2 mpg = (124)(0.26)(0.?4) x 23.9 0': Each trial is independent of the other trials when the outcome o
School: UCF
Course: Principles Of Statistics
LII 10. 11. 12. 14. 15. 16. 17. 18. 4.1 EXERCISE SOLUTIONS 1. A random variable represents a numerical value associated with each outcome of a probability experiment. Examples: Answers will vary. 2. A discrete probability distribution hsts each possible v
School: UCF
Course: Principles Of Statistics
3.4 EXERCISE SOLUTIONS 1. The number of ordered arrangements of 11 objects taken 1' at a time. Sampfe answer: An example of a permutation is the number of seating arrangements of you and three friends. 2. The number of ways to select I" of the H objects w
School: UCF
Course: Principles Of Statistics
2.4 EXERCISE SOLUTIONS I9 I U! The lange is the difference between the maximmrl and minimum values of a data set. The advantage of the range is that it is easy to calculate. The disadvantage is that it uses only two entries from the data set. A deviation
School: UCF
Course: Statistical Methods
Sampling Lab The purpose of this laboratory exercise is to familiarize yourself with the different sampling techniques. You need one page from a movie listing (like contained in TV-Guide). Note, if you actually use TV Guide, then you need to use two facin
School: UCF
Course: Data Preparation
Lab for Statistical Decision Theory Data Explanation: Same as the data used in Practicum 3 Problem 1 For the target TAR1, (a) Repeat Practicum 2 with exactly the same options used in the practicum. (b) Write down the profit equation for each decision wher
School: UCF
Course: Data Preparation
Lab for Statistical Decision Theory Data Explanation: Same as the data used in Practicum 3 Problem 1 For the target TAR1, (a) Repeat Practicum 2 with exactly the same options used in the practicum. (b) Write down the profit equation for each decision wher
School: UCF
Course: Principles Of Statistics
Q: Construct a Confidence Interval mean, average Is sample size (n) 30? START proportion, percentage p Whats the parameter of interest? (6.3) Confidence Interval for p Ye s No (6.1) Confidence Interval for Large Sample Size If is unknown, use s Yes Is kn
School: UCF
Course: Principles Of Statistics
"It is a characteristic of wisdom not to do desperate things." 1 - Henry David Thoreau How To Do Well In This Class (or Surviving Statistics for Dummies) 1. Pay attention during lectures. 2. Review your lecture notes after each class, it doesn't have to b
School: UCF
Course: Statistical Methods
APPLICATION:Descriptive:(#&graphical)patterns,sum.Info,identifypop/sample(collectionsofexperimentalunits),convenientformex)piechartw/#s Inferential:(sampledata)stereotype,estimate,makedecisions/predictions,generalizationsaboutlargesetofdataex)Mapw/colorst
School: UCF
Course: Statistical Methods
STA2023.0004Exam1PrepSheet Date:FridaySeptember13,2013@2:30pm Location:THEUSUALCLASSROOM Format:18multiplechoicequestionsworth10pts.each Time:50minutes Pleasebring: Pencil Eraser RaspberryScantron Calculator StudentID 5x8indexcard(maxsizechangedfrom
School: UCF
Course: STATISTICS
STA1001C Student Course Materials Course Materials Table of Contents Chapter 1 Course Launch, What is Statistics, and Mind Set Activity Supplement Community Contract . 3 Lesson 1.1: The Statistical Analysis Process. 5 Supplement 1.2: Mindset Activity .17
School: UCF
Course: STATISTICS
Tuesday and Thursday Schedule Week 1 Tuesday Sept 1 Scheduled Lesson 1.1 Thursday Sept 3 Scheduled Lesson 1.1 (Lesson 1.2- Talk) Lesson 2.2 Lesson 2.4 Week 2 Week 3 Sept 8 Sept 15 Lesson 2.1 Lesson 2.3 Sept 10 Sept 17 Week 4 Sept 22 Lesson 2.4 Lesson 2.5
School: UCF
Course: Statistics For Engineers
STA 3032Probability and Statistics for Engineers Fall 2015 Course Semester Schedule: Date 1 August 25, 2015 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 August 27, 2015 September 1, 2015 September 3, 2015 September 8, 2015 Septemb
School: UCF
Course: Statistics For Engineers
STA 3032Probability and Statistics for Engineers Fall 2015 Instructor Contact Instructor Office Office Hours Phone E-mail Basma Selim, Ph.D. Lake Nona COM 313 Tu and Th 5:30 - 6:00 pm and 7:15 7:45pm Other times by appointment 407.266.1411 basma.selim@ucf
School: UCF
Course: Statistical Methods
STA 2023 Statistical Methods I Instructor: Mrs. Susan Schott E-Mail: sschott@ucf.edu Note: I will only respond to knights e-mail Phone: Office: Spring 2013 823-2832 (best way to reach me is email) CCII 210C Webcourses: Access to the webcourse component ca
School: UCF
Course: Principles Of Statistics
PRINCIPLES OF STATISTICS SPRING 2015 CHINESE PROVERB: Tell me, Ill forget. Show me, I may remember. But involve me and Ill understand. COURSE: STA 2014 - Principles of Statistics INSTRUCTOR: Vu Nguyen (Tom) OFFICE: Technology Commons II, 212F E-MAIL: Vu.N
School: UCF
Course: Linear Models
STA 6246 Linear Models Fall 2011 Instructor James Hobert, 221 Grifn-Floyd Hall; Ofce Hours: Monday & Wednesday 4th period (10:4011:30); Phone: 273-2990; Email: jhobert@stat.ufl.edu Course Web Page http:/www.stat.ufl.edu/jhobert/sta6246 Course Description