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School: UCF
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: 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: 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
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: 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, Ri
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: 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: 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
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: 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 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: 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 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: Statistics For Engineers
Assignment 8a Due Nov 19 at 11:59pm Points 100 Questions 10 Available Nov 15 at 9am - Nov 19 at 11:59pm 5 days Time Limit 180 Minutes Instructions You have 3 hours to complete this Assignment. Numerical answers must be given to 4 decimal places. Optional:
School: UCF
Course: Statistics For Engineers
Assignment 8b Due Nov 21 at 11:59pm Points 100 Questions 11 Available Nov 17 at 9am - Nov 21 at 11:59pm 5 days Time Limit 180 Minutes Instructions You have 3 hours to complete this Assignment. Numerical answers must be given to 4 decimal places. Optional:
School: UCF
Course: Statistics For Engineers
Assignment 7a Due Nov 7 at 11:59pm Points 100 Questions 10 Available Nov 1 at 9am - Nov 7 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: Yo
School: UCF
Course: Statistics For Engineers
Assignment 6 Due Oct 31 at 11:59pm Points 100 Questions 10 Available Oct 25 at 9am - Oct 31 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: Statistics For Engineers
10/11/13 Assignment 3b: STA3032_CMB-13Fall Assignment 3b Due Sep 26 at 11:59pm Points 100 Questions 10 Available Sep 20 at 9am - Sep 26 at 11:59pm 7 days Time Limit 180 Minutes Instructions You have 3 hours to complete this Assignment. Ensure you go over
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: 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: 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
School: UCF
Course: Data Preparation
Welcome to the TTE4004 Page TTE4004: TRANSPORTATION ENGINEERING (Spring 2006) DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING UNIVERSITY OF CENTRAL FLORIDA INSTRUCTOR: OFFICE HOURS: GOAL AND OBJECTIVES: Professor Haitham Al-Deek, Ph.D., P.E., Rm. 209, E
School: UCF
Course: Data Mining Methodology II
STAT 6704 Data Mining II Subject: Lecture: Instruction Period Holidays Instructor: Spring 2008 SYLLABUS Data Mining Methodology II (STA6704) M & W 6:00-7:15 pm, Room 220, CL1 01/07/2008 04/21/2008 1/21/2008, Monday, Martin Luther King Jr.s Day 3/10
School: UCF
Course: Computer Processing Of Statistical Data
STA 4102 Computer Processing of Statistical Data Fall 2008 Section 0001, TuTh 1:30-2:45 PM, CL1 205 Professor: Dr. Roberto Carta Office: Room 212F, Computer Center II Email: rcarta@mail.ucf.edu Webpage: http:/pegasus.cc.ucf.edu/~rcarta Office Hours
School: UCF
Course: Statistical Methods I
STAT 2023 Summer C, 2005 SYLLABUS Subject: Lecture: Dates: Instructor: Statistical Methods I TuTh 2:00-3:50 pm, Room 102, MOD9 05/16/2005 - 08/05/2005 Xiaogang Su Room 102, Computer Center II (407) 823-2940 [O] xiaosu@mail.ucf.edu TuTh 4:00-5:00 pm
School: UCF
Course: Statistical Methods II
STAT 4163 Stat Method II Summer, 2007 Term B SYLLABUS Subject: Lecture: Time Length: Instructor: Statistical Methods II MTuWTr 10:00 - 11:50 am, Room 212, BA 06/25/2006 Mon - 08/03/2007 Fri Holiday: Wednesday, July 4 Dr. Xiaogang Su Room 102 Comput
School: UCF
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: 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: 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
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: 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, Ri
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: Statistical Methods II
Solutions to Practice Problems Problem 10.3: (a) General Linear Models Procedure Dependent Variable: RESP Source Model Error Corrected Total DF 2 10 12 Sum of Squares 12.420308 18.332000 30.752308 Mean Square 6.210154 1.833200 F Value 3.39 Pr > F .07528 (
School: UCF
Course: Statistical Methods 1
Chapter 1 Homework Solutions 12. a) b) c) d) e) f) quantitative quantitative quantitative qualitative quantitative quantitative 16. a) b) c) d) e) f) all students enrolled in the course; gpa quantitative census sample 100% no; size of the class and how re
School: UCF
Course: Statistical Methods 1
Chapter 2 Homework Solutions 30. a) frequency b) 14 c) 49 66. a) b) c) d) e) f) skewed right skewed left skewed right symmetric skewed right (ages of cars) skewed left 86. a) 12.93; meters b) 16.77; meters2 c) 4.09; meters 98. a) between 27 and 51 b) appr
School: UCF
Course: Statistical Methods 1
Chapter 9 Homework Solutions 4. d 12. 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. b) The confidenc
School: UCF
Course: Statistical Methods 1
Chapter 3 Homework Solutions 14. a) (1,1), (1,2), (1,3), (1,4), (1,5), (1,6) (2,1), (2,2), (2,3), (2,4), (2,5), (2,6) (3,1), (3,2), (3,3), (3,4), (3,5), (3,6) (4,1), (4,2), (4,3), (4,4), (4,5), (4,6) (5,1), (5,2), (5,3), (5,4), (5,5), (5,6) (6,1), (6,2),
School: UCF
Course: Statistical Methods II
Solutions to Practice Problems Problem 10.1: (a) Cholesterol levels (b) There is only one qualitative factor, socioeconomic class. (c) Since there is only one factor, each level in this factor is a treatment. There are four treatmentspoverty, low income,
School: UCF
Course: Statistical Methods 1
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 1
Chapter 6 Homework Solutions 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) No, the answer to part b does not depend on the value of the mean. 42. a) b)
School: UCF
Course: Statistical Methods 1
Chapter 7 Homework Solutions 8. a) 95% b) 90% c) 99% d) 80% e) 67.78% 70. True 74. a) 225; b) 267 82. 1,692 124. X = 1.471, s= .064, (1.392, 1.55) 136. Use p = .094 and SE = .02. You would need to take n = 818 samples.
School: UCF
Course: Statistical Methods 1
Chapter 8 Homework Solutions 30. a) H 0 : = 71 versus H a : > 71 b) Reject if z > 1.645 c) z = 3.953 d) Reject H0 76. You need to calculate the mean and standard deviation of the sample: 24, 20, 22 n = 3, X = 22, s = 2 Ho: =21 Ha: > 21 T.S. t = .87 RR: Re
School: UCF
Course: Statistical Methods 1
Chapter 4 Homework Solutions 46. -$.70 64. a) b) c) d) Satisifies the 5 characteristics of the binomial p(x) formula on page 199 with n=5 and p=.25 .264 .633 72. .049 128. a) .064; b) .936; c) 1,8, .849 130. a) .230; B) .143; c) .100 132. a) .467; b) .533
School: UCF
Course: Theortical Statistic
STA 6326 CHAPTER 1 - SOLUTIONS Problem 1.3 (c) Formally, A B x S : x A B c and Ac B c x S : x A and x B . Let x A B . Since x A B then x A and x B . Therefore, x Ac and c x B c . Hence, x Ac B c . Consequently, A B Ac B c . c Let x Ac B c . Since x A and
School: UCF
Course: Statistics For Engineers
STA 3032 Statistics for Engineers Spring 2012 Discrete Distributions Exercise Problems 1. A shipment of 120 alarms contains 5 that are defective. If 3 of these are randomly selected and shipped to a customer, find the probability that the customer will ge
School: UCF
Course: Statistics For Engineers
STA 3032 Statistics for Engineers Spring 2011 Probability - Exercise Problems 1. A special-purpose computer contains 3 switches, each of which can be set in 3 different ways. In how many ways can the computers bank of switches be set? Answer Each switch c
School: UCF
Course: Statistical Methods 1
Chapter 1 Homework Solutions 12. a) b) c) d) e) f) quantitative quantitative quantitative qualitative quantitative quantitative 16. a) b) c) d) e) f) all students enrolled in the course; gpa quantitative census sample 100% no; size of the class and how re
School: UCF
Course: Statistical Methods 1
Chapter 2 Homework Solutions 30. a) frequency b) 14 c) 49 66. a) b) c) d) e) f) skewed right skewed left skewed right symmetric skewed right (ages of cars) skewed left 86. a) 12.93; meters b) 16.77; meters2 c) 4.09; meters 98. a) between 27 and 51 b) appr
School: UCF
Course: Statistical Methods 1
Chapter 3 Homework Solutions 14. a) (1,1), (1,2), (1,3), (1,4), (1,5), (1,6) (2,1), (2,2), (2,3), (2,4), (2,5), (2,6) (3,1), (3,2), (3,3), (3,4), (3,5), (3,6) (4,1), (4,2), (4,3), (4,4), (4,5), (4,6) (5,1), (5,2), (5,3), (5,4), (5,5), (5,6) (6,1), (6,2),
School: UCF
Course: Statistical Methods 1
Chapter 4 Homework Solutions 46. -$.70 64. a) b) c) d) Satisifies the 5 characteristics of the binomial p(x) formula on page 199 with n=5 and p=.25 .264 .633 72. .049 128. a) .064; b) .936; c) 1,8, .849 130. a) .230; B) .143; c) .100 132. a) .467; b) .533
School: UCF
Course: Statistical Methods 1
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 1
Chapter 6 Homework Solutions 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) No, the answer to part b does not depend on the value of the mean. 42. a) b)
School: UCF
Course: Statistical Methods 1
Chapter 7 Homework Solutions 8. a) 95% b) 90% c) 99% d) 80% e) 67.78% 70. True 74. a) 225; b) 267 82. 1,692 124. X = 1.471, s= .064, (1.392, 1.55) 136. Use p = .094 and SE = .02. You would need to take n = 818 samples.
School: UCF
Course: Statistical Methods 1
Chapter 8 Homework Solutions 30. a) H 0 : = 71 versus H a : > 71 b) Reject if z > 1.645 c) z = 3.953 d) Reject H0 76. You need to calculate the mean and standard deviation of the sample: 24, 20, 22 n = 3, X = 22, s = 2 Ho: =21 Ha: > 21 T.S. t = .87 RR: Re
School: UCF
Course: Statistical Methods 1
Chapter 9 Homework Solutions 4. d 12. 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. b) The confidenc
School: UCF
Course: Statistical Methods II
Solutions to Practice Problems Problem 10.6: (a) Source Treat Block Error Total df 3 5 15 23 ANOVA SS 28.2 69.0 34.1 131.3 MS 9.4 13.8 2.27 F 4.14 6.08 p-value 0.067 <0.01 (b) H0: 1 =2 = 3 = 4 Ha: At least two means differ. Ftreat = 4.14 p-value = 0.067 >
School: UCF
Course: Statistical Methods II
Solutions to Practice Problems Problem 10.8: (a) General Linear Models Procedure Dependent Variable: RESP Source GLASS TEMP GLASS*TEMP Error Corrected Total DF 2 2 4 18 26 Sum of Squares 151966.7 1955410.1 289005.0 5926.0 2402307.9 Mean Square 75983.4 977
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
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: Statistics
Stat 516 Homework 2 Solutions 1. See handwritten notes at the end of these solutions. 2. Cut and paste the matrix of numbers to a text le. I saved that le as affypixel.txt. Open R and set the working directory to the directory containing the le. For examp
School: UCF
Course: Statistics
Stat 516 1. Homework 3 Solutions (a) (5 points) Expression data were simulated for an experiment involving g = 10, 000 genes and two treatment groups with ve independent experimental units in each treatment group. Gene2 2 specic variances 1 , . . . , g we
School: UCF
Course: Statistics
Stat 516 Homework 5 Solutions 1. Consider conducting m hypothesis tests. Let V denote the number of type I errors. Let R denote the number of rejected null hypotheses. Let Q = V /R if R > 0, and let Q = 0 if R = 0. Let (0, 1) be xed. By denition, a method
School: UCF
Course: Statistics
STAT 516 HW6 Solutions 1. Answers vary. 2. a) b) c) d) 1 3. Consider the following "data" to be clustered using a variety of methods described below. 10 20 40 80 85 121 160 168 195 For each part of the problem, assume that Euclidean distance will be used
School: UCF
Course: Probability
SOLUTIONS. CHAPTER 1 Section 1.2 Section 1.4 Section 1.7
School: UCF
Course: Probability
SOLUTIONS. CHAPTER 2 Section 2.2 Section 2.3 Section 2.4
School: UCF
Course: Probability
SOLUTIONS. CHAPTER 3 Section 3.1 Section 3.2 Section 3.3 Section 3.4 Section 3.5
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 14.3 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: Statistical Methods II
Solutions to Practice Problems Problem 14.1: Hypothesis: H0: = 104 Ha: 104 Test Statistic: S+ = 14 S = 6 S = max(S+ , S) = 14 Observed Significance Level: p-value = 2P(S 14) = 2(1 P(S 13) = 2(1 0.942) = 0.116 Conclusion: We can reject the null hypothesis
School: UCF
Course: Statistical Methods II
Solutions to Practice Problems Problem 13.3: (a) df = (r 1)(c 1) = (5)(3) = 15 2 15, 0.05 = 24.9958, i. e. , rejection region is 2 > 24.9958 (b) df = (r 1)(c 1) = (2)(4) = 8 2 8, 0.10 = 13.3616, i. e. , rejection region is 2 > 13.3616 (c) df = (r 1)(c 1)
School: UCF
Course: Statistical Methods II
Solutions to Practice Problems Problem 11.1: (a) There is a positive linear relationship between x and y. SS (b) 1 = xy = 0.918 SS xx 0 = y 1 x = 0.020 Problem 11.2: (a) 93 SS (b) 1 = xy = = 10.33 SS xx 9 0 = y 1 x = 64.43 (10.33)(1.5) = 48.93 1
School: UCF
Course: Statistical Methods II
Solutions to Practice Problems Problem 11.3: e xj = 140 c h= 43.429 26 2 SSxx = x 2 SSxy = xy n x 2 7 e je yj = 129 c h h 39.857 26 c 24 = n 7 e yj = 124 c h= 41.714 24 2 SS yy = y 2 n 2 7 39.857 SS 1 = xy = = 0.918 SS xx 43.429 SSE = SS SS = 41.714 (0.
School: UCF
Course: Statistical Methods II
Solutions to Practice Problems Problem 11.5: (a) 1 = SSxy SSxx = 0.1055 0 = y 1 x = 67.877 SSE = SS SS = 73.77 yy 1 xy s2 = SSE (n 2) = 12.29 s = s2 = 3.506 s = s SS xx = 0.022 1 (b) It might not have a straight-line relationship. However, the city size a
School: UCF
Course: Statistical Methods II
Solution to Practice Problem Problem 11.7: r = SS xy SS xx SS yy = 2670.04 r 2 = (SS yy SSE) SS yy = (25300)(355.50) = 0.89 355.50 73.77 = 0.792 355.50 City Size and Expenditure are highly positive correlated.
School: UCF
Course: Statistical Methods II
Solutions to Practice Problems Problem 11.8: (a) y = 0 + 1 xp = 17.388852 + (0.657752)(50) = 50.276 t0.005, 8 = 3.355 2 1 ( xp x ) 1 (50 51.1) 2 sy = s + = 9.9398 + = 3.147 n SS xx 10 5638.9 99% C. I. = y t0.005, 8 sy = 50.276 (3.355)(3.147) = [39.72, 60.
School: UCF
Course: Statistical Methods II
Solution to Practice Problem Problem 11.10: (a) 1894 SS 1 = xy = = 0.766 SS xx 2474 = y x = 76 (0.766)(46) = 40.764 0 1 y = 0 + 1 x = 40.764 + 0.766 x (b) Sure, it looks great. (c) SSE = SS yy 1SS xy = 2056 (0.766)(1894) = 605.196 s2 = MSE = SSE (n 2) =
School: UCF
Course: Statistical Methods II
Solutions to Practice Problems Problem 12.1: (a) y = 0 + 1x1 + 2x2 + (b) 0 = 0.666667 = 1.316667 1 2 = 8 (c) y = 0.667 + 1.317 x1 8 x2 (d) SSE = 1.345 MSE = 0.14944 s = Root MSE = 0.38658 (e) 1 1.316667 tc = = = 13.191 s 0.09981464 1 Reject the null h
School: UCF
Course: Statistical Methods II
Solutions to Practice Problems Problem 12.3: (a) Hypothesis: H0: 1 = 2 = 3 = 0 Ha: At least one i 0, i = 1, 2, 3 Test Statistic: Fc = R2 (1 R ) 2 k = [ n ( k + 1) ] 0.85 / 3 = 30.22 (1 0.85) /[ 20 (3 + 1)] Rejection Region: Fc > F0.05, 3, 16 = 3.24 Conclu
School: UCF
Course: Statistical Methods II
Solutions to Practice Problems Problem 12.7: (a) The residual plot looks fine. (b) All the residuals for humidity = 0.2 are below the 0 line and all the residuals for humidity = 0.3 are above the 0 line. The model equal variance assumption of the error te
School: UCF
Course: Statistical Methods II
Solutions to Practice Problems Problem 12.9: Quantitative: Average height of trees in transect, Total number of trees in transect, Height of hedgerow in transect, Width of hedgerow, Width of hedgerow, Width of transect verge, Depth of transect ditch, Widt
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
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: 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: Statistics For Engineers
CHAPTER 7 INFERENCES CONCERNING MEANS SPRING 2008 STA 3032 ^ A point estimation ( ) is a rule or formula that tell us how to calculate a numerical estimate based on the measurements contained in a sample. is a parameter ^ is a point estimator
School: UCF
Course: Statistics For Engineers
CHAPTER 6 SAMPLING DISTRIBUTIONS SPRING 2008 STA 3032 Random Sample: If n elements are selected from a population in such a way that every set of n elements in the population has an equal probability of being selected, the selection of such a set n e
School: UCF
Course: Statistics For Engineers
CHAPTER 9 INFERENCES CONCERNING PROPORTIONS SPRING 2008 STA 3032 Large Sample (1- ) 100% Confidence Interval for a population proportion (p) ^ ^ p Z / 2 p p Z / 2 ^ where ^ pq n ^ p is the sample proportion of observations with the characteri
School: UCF
Course: Statistical Methods
STA Class 4/8/2008 Example 3: Tires (fig 6.2-10) o The Goodyear Tire Company manufactures car tires that last distances that are normally distributed with a mean of 38,000 miles and a standard deviation of 4500 miles. A. If a tire is randomly sele
School: UCF
Course: Statistical Methods
STA Class 4/8/2008 Ch 6 Sampling Distributions o In solving most practical problems, the assumption that distributions and parameters are known is unrealistic o In inferential statistics, we use sample data to estimate distributions and parameters
School: UCF
Course: Statistical Methods
STA Class 4/8/2008 To find Poisson Probabilities o 1. Point probability formula o 2. Poisson tables (pg 889 893, cumulative) o Ex. A certain stretch of I-4 has an average of 3 accidents per day. What is the probability of: fig 4.5-3 1. Exactly 1
School: UCF
Course: Statistical Methods
STA Class 4/8/2008 To find Binomial Random Variable Probabilities o 1. Use methods from Ch 3 o 2. Use Binomial Point Probability Distribution Fig 4.3-3 n = number of trials p = prob. Of success q = prob. Of failure Three Children example o Find
School: UCF
Course: Statistical Methods
STA Class 4/8/2008 4.3 Expected Values of Discrete Random Variables o Mean (expected value): = multiplied by = E(x) = x p(x) o Variance: = (x-) p(x) = *x p(x)+ - o Ex. Sickle Cell Problem The expected number of children in 3 to get sickle
School: UCF
Course: Statistical Methods
STA Class 4/8/2008 Ch 4 Discrete Random Variables o Random variable-rule 4.1 two types of random variables (R.V.) o Discrete-countable EX: number of siblings, number of women hired o Continuous-can assume any number in an interval EX: height, we
School: UCF
Course: Statistical Methods
STA Class Exam 1 o o Ch 1 o o o o 4/8/2008 8 x 11 formula sheet allowed, both sides can be filled Do need to show work Understand data summary and presentation techniques Types of Data Quantitative and Qualitative. Definitions Descriptive & Infer
School: UCF
Course: Statistical Methods
STA Class 4/8/2008 Questions o An advertising agency knows that 35% of all households subscribe to the weekday paper, 50% get the Sunday paper, and 20% get both. A) Represent this data in a Venn Diagram B) if a household subscribes to the weekday
School: UCF
Course: Statistical Methods
STA Class 4/8/2008 3.2 Unions and Intersections o These are compound events-they are formed by the composition of two or more other events o Union (A U B)-event that occurs if either A or B or both occur on a single performance of an experiment. Un
School: UCF
Course: Statistical Methods
STA Class 4/8/2008 Ch 3 Probability o Probability permits us to make the inferential jump from sample to population and then give a measure of reliability for the inferred o In this chapter we make the assumption that the population is known. 3.1 e
School: UCF
Course: Statistical Methods
STA Class 4/8/2008 2.7 Numerical Measures of Relative Standing o 1. p th percentile A number such that p% of observations fall below it and (100 p)% fall above it. o Ex: SAT math score is the 84th percentile Median-50th percentile o 2. Z-score *
School: UCF
Course: Statistical Methods
STA Class 4/8/2008 2.5 Numerical Measures of Variability o Describe the deviation of data about the mean (or other typical value) 1. Range difference between the largest and smallest observation 2. Variance (Figure 2.5-1 in notes) o Computationa
School: UCF
Course: Statistical Methods
STA Class Two Methods of Sets of Data: 4/8/2008 Ch. 2 Methods for Describing Sets of Data 1. Graphical o Oualitative00bar chart, pie chart o Quantitative00Stem and leaf, histogram 2. Numerical o Measures of central tendency, measures of variation
School: UCF
Course: Statistical Methods
STA Class Why Do You Need To Study Statistics? 4/8/2008 Ch. 1 1. To learn methods of Data collection 2. Data Analysis 3. Understanding of journal articles and news stories. Data The observation gathered through methods such as surveys, experiment
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
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: Regression Analysis
Exceptions to Ordinary Least Squares Lecture XXXI I. Heteroscedasticity A. Using the derivation of the variance of ordinary least squares estimator ^ XX 1 X V ^ V ^ XX XX 1 X X XX 1 1 1 X SX X X E :S IT T . E under the Gauss-Markov assumptions S B. 2 2 IT
School: UCF
Course: Regression Analysis
Restricted Least Squares and Hypothesis Testing: Lecture XXX I. Restricted Least Squares A. As a starting point, we consider the data in Table 1. Table 1. Regression Data y Observation 1 75.72173 2 45.11874 3 51.61298 4 92.53986 5 118.74310 6 80.78596 7 4
School: UCF
Course: Regression Analysis
Distribution of Estimates and Multivariate Regression Lecture XXVII I. Models and Distributional Assumptions A. Conditional Normal Model 1. The conditional normal model assumes that the observed random variables are distributed yi ~ N xi , 2 Thus, E yi xi
School: UCF
Course: Regression Analysis
Simple Linear Regression Lecture XXVIII I. Overview A. Most of the material for this lecture is from George Casella and Roger L. Berger Statistical Inference (Belmont, California: Duxbury Press, 1990) Chapter 12, pp. 554-577. The purpose of regression ana
School: UCF
Course: Regression Analysis
Vector Spaces and Eigenvalues Lecture XXVII I. Orthonormal Bases and Projections A. Suppose that a set of vectors x1 , xr for a basis for some space S in R m space such that r m . For mathematical simplicity, we may want to form an orthogonal basis for th
School: UCF
Course: Regression Analysis
Review of Matrix Algebra and Vector Spaces: Lecture XXVI I. Review of Elementary Matrix Algebra A. B. The material for this lecture is found in James R. Schott Matrix Analysis for Statistics (New York: John Wiley & Sons, Inc. 1997). Basic definitions 1. A
School: UCF
Course: Data Preparation
Outliers Morgan C. Wang Department of Statistics University of Central Florida 1 Morgan C. Wang 2/9/2011 Outline 2 Introduction Data Anomaly Univariate Outliers Detection Multivariate Outliers Detection Case Study Conclusions Morgan C. Wang 2/9/2011 Intro
School: UCF
Course: Statistical Methods III
Lecture & Examples Topic 3: The Wilcoxon Signed Rank Test Introduction: The test presented in this lecture is known as the Wilcoxon Signed Rank Test. This test, presented by Wilcoxon (1945), is designed to test whether a particular sample came from a popu
School: UCF
Course: Statistical Methods III
Lecture and Examples Topic 9: Testing Portions of a Model Suppose we use both Model I and Model II to fit the same data and want to know which model is better in fitting the data. Model I: y = 0 + 1 x1 + 2 x2 + g x g + Model II: y = 0 + 1 x1 + 2 x 2 + g
School: UCF
Course: Statistical Methods III
Lecture & Examples Topic 8: Models with Qualitative Independent Variable Model with One Qualitative Independent Variable with k Levels: Suppose we want to develop a model for the mean yield per acre, E(y), of four different varieties of snow peas (A, B, C
School: UCF
Course: Statistical Methods III
Lecture and Examples Topic 6: Models with a Single Quantitative Independent Variable As described in previous lectures, the first step in the construction of a regression model is to hypothesize the deterministic portion of the probabilistic model. So far
School: UCF
Course: Statistical Methods III
Lecture & Examples Topic 2: Completely Randomized Design The completely randomized design is the simplest form of experimental designs. In a completely randomized design, each treatment is applied to each experimental unit completely by chance. Although t
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 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: 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
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
Stats: Testing a Single Proportion You are testing p, you are not testing p hat. If you knew the value of p, then there would be nothing to test. All hypothesis testing is done under the assumption the null hypothesis is true! I can't emphasize this enoug
School: UCF
Course: Statistical Methods
Stats: Testing a Single Mean You are testing mu, you are not testing x bar. If you knew the value of mu, then there would be nothing to test. All hypothesis testing is done under the assumption the null hypothesis is true! I can't emphasize this enough. T
School: UCF
Course: Statistical Methods
Stats: Hypothesis Testing Steps Here are the steps to performing hypothesis testing 1. 2. 3. 4. 5. 6. Write the original claim and identify whether it is the null hypothesis or the alternative hypothesis. Write the null and alternative hypothesis. Use the
School: UCF
Course: Statistical Methods
Stats: Confidence Intervals as Tests Using the confidence interval to perform a hypothesis test only works with a two-tailed test. If the hypothesized value of the parameter lies within the confidence interval with a 1-alpha level of confidence, then the
School: UCF
Course: Statistical Methods 1
Question1(1point) StatisticsQuiz Usethefollowingsetofsamplevaluestoanswerthequestion. 31 23 28 27 19 18 22 19 30 17 13 21 37 10 12 20 13 33 24 26 Whatisthesamplemean?Roundanswertotenths. Question1options: 22.2 22.0 22.15 22.1 Save Question2(1point) Usethe
School: UCF
Course: Regression Analysis
Examination I October 1, 2006 I. Given f x, y such that f x, y 5 7 1 3 1 1 x x 2 y xy y 2 24 24 6 8 24 6 Defined over x, y 0, 2 . A. B. C. D. Is this a valid probability density function? (10 Points) Derive the marginal distribution of y . (10 Points) Der
School: UCF
Course: Regression Analysis
AEB 6933Mathematical Statistics for Food and Resource Economics Examination I, September 29, 2006 1. Given f x, y such that f x, y 3 10 x 2 352 y2 1 xy 4 defined over x, y 2, 2 . a. Is this a valid probability density function? (10 points) b. Derive the m
School: UCF
Course: Regression Analysis
AEB 6933Mathematical Statistics for Food and Resource Economics Examination I September 28, 2005 1. Given the cumulative distribution function F ( x, y ) = - 1 x 3 y + 1 x 2 y 2 - 1 xy 3 + 7 xy 3 2 3 6 defined over x, y [ 0,1] : a. b. c. d. Is this a vali
School: UCF
Course: Regression Analysis
AEB 6933 Statistics for Food and Resource Economics Examination I 1. Given that f(x) is distributed uniform x=[0,9], derive the distribution y such that y = (x ) = x . What is the expected value of 2 - y such that y 2 ? h( y ) = 0 otherwise Derive the mom
School: UCF
Course: Regression Analysis
AEB 6533Stastistics in Food and Resource Economics First TestOctober 6, 1999 1. Assume f(x)=1/10 and y=ln(x), find g(y), E[y], and V[y]. 2. Is f(x)=3x2 a valid distribution function? Find the moment generating function for this distribution. Given the sam
School: UCF
Course: Statistical Methods
Stats: Hypothesis Testing Introduction Be sure to read through the definitions for this section before trying to make sense out of the following. The first thing to do when given a claim is to write the claim mathematically (if possible), and decide wheth
School: UCF
Course: Data Preparation
STA 6714 Final Project Report Due Date: April 28, 2006 Final Presentation: April 26, 2006 Most data-mining projects always start from data preparation. However, the effectiveness of data preparation process can not be confirmed without adequate model buil
School: UCF
Course: Statistical Methods III
SAS example 1 data bloodp; input id sbp datalines; 1 144 39 2 220 47 3 138 45 4 145 47 5 162 65 6 142 46 7 170 67 8 124 42 9 158 67 10 154 56 11 162 64 12 150 56 13 140 59 14 110 34 15 128 42 16 130 48 17 135 45 18 114 17 19 116 20 20 124 19 21 136 36 22
School: UCF
Course: STATISTICAL METHODS I
STA2023 Class Test 3, Tuesday 23 November 1. Acid rain, caused by the reaction of certain air pollutants with rainwater, appears to be a growing problem in the northeastern section of the United States. Pure rainfall through clean air registers a pH readi
School: UCF
Course: STATISTICAL METHODS I
STA2023 Class Test 2, Tuesday 12 October 1. Suppose that 2% of the population of a rural county in Wyoming believe that OJ Simpson trial resulted in justice being served. Also, suppose I have randomly and independently sampled 200 of these residents. Let
School: UCF
Course: STATISTICAL METHODS I
STA2023 Class Test 1, Tuesday 21 September 1. A high school guidance counselor analyzed the data from a random sample of 500 community colleges collected from throughout the United States. One of his goals was to estimate the annual tuition costs of commu
School: UCF
Course: STATISTICAL METHODS I
Sta2023 Statistical Methods I Final Exam 4:00-6:50 Thursday 9 December 1 The weights of a group of male students are shown below using a stemand-leaf display Depth 2 4 7 14 19 (8) 21 14 8 4 3 1 stem leaves 120 3,8 130 0,5 140 0,2,5 150 1,5,5,5,6,6,6 160 0
School: UCF
Course: Statistical Methods I
STA2023 Class Test 1, Tuesday 21 September 1. A high school guidance counselor analyzed the data from a random sample of 500 community colleges collected from throughout the United States. One of his goals was to estimate the annual tuition costs of
School: UCF
Course: Statistical Methods I
Sta2023 Statistical Methods I Final Exam 4:00-6:50 Thursday 9 December 1 The weights of a group of male students are shown below using a stemand-leaf display Depth 2 4 7 14 19 (8) 21 14 8 4 3 1 stem leaves 120 3,8 130 0,5 140 0,2,5 150 1,5,5,5,6,6,6
School: UCF
Course: Statistics 2023
Sample Final Answers STA2023- Fall 2007 1. Weights: (20.2, 15.6, 12.9, 14.2, 16.7, 15.0) a) X 15.77 s= 2.52 b) Ho: = 15 oz. Ha: > 15 oz. T.S. t = 0.7485 compared to t of 1.476, do not reject Ho, conclude that you do not have enough evidence to say
School: UCF
Course: Statistics 2023
Sample Final STA2023- Fall 2007 Note: For each hypothesis test: Define Ho and Ha, R.R., Test Statistic, Decision and Conclusion. Decision will be Reject Ho or Do not reject Ho and Conclusion is in the words of the problem. 1. The following data (rand
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: Statistics For Engineers
Assignment 8a Due Nov 19 at 11:59pm Points 100 Questions 10 Available Nov 15 at 9am - Nov 19 at 11:59pm 5 days Time Limit 180 Minutes Instructions You have 3 hours to complete this Assignment. Numerical answers must be given to 4 decimal places. Optional:
School: UCF
Course: Statistics For Engineers
Assignment 8b Due Nov 21 at 11:59pm Points 100 Questions 11 Available Nov 17 at 9am - Nov 21 at 11:59pm 5 days Time Limit 180 Minutes Instructions You have 3 hours to complete this Assignment. Numerical answers must be given to 4 decimal places. Optional:
School: UCF
Course: Statistics For Engineers
Assignment 7a Due Nov 7 at 11:59pm Points 100 Questions 10 Available Nov 1 at 9am - Nov 7 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: Yo
School: UCF
Course: Statistics For Engineers
Assignment 6 Due Oct 31 at 11:59pm Points 100 Questions 10 Available Oct 25 at 9am - Oct 31 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: Statistics For Engineers
10/11/13 Assignment 3b: STA3032_CMB-13Fall Assignment 3b Due Sep 26 at 11:59pm Points 100 Questions 10 Available Sep 20 at 9am - Sep 26 at 11:59pm 7 days Time Limit 180 Minutes Instructions You have 3 hours to complete this Assignment. Ensure you go over
School: UCF
Course: Statistics For Engineers
Assignment 5a Due Oct 12 at 11:59pm Points 100 Questions 10 Available Oct 8 at 5pm - Oct 12 at 11:59pm 4 days Time Limit 180 Minutes Instructions You have 3 hours to complete this Assignment. Ensure you go over your work and click 'Submit Quiz' when you a
School: UCF
Course: Statistics For Engineers
10/11/13 Assignment 1: STA3032_CMB-13Fall Assignment 1 Due Sep 5 at 11:59pm Points 100 Questions 16 Available Aug 30 at 9am - Sep 5 at 11:59pm 7 days Time Limit 180 Minutes Instructions You have 3 hours to complete this Assignment. Ensure you go over your
School: UCF
Course: Statistics For Engineers
Assignment 5b Due Oct 23 at 11:59pm Points 100 Questions 10 Available Oct 18 at 9am - Oct 23 at 11:59pm 6 days Time Limit 180 Minutes Instructions You have 3 hours to complete this Assignment. Ensure you go over your work and click 'Submit Quiz' when you
School: UCF
Course: Statistics For Engineers
10/11/13 Assignment 4b: STA3032_CMB-13Fall Assignment 4b Due Oct 1 at 8:59am Points 100 Questions 10 Available Sep 27 at 9am - Oct 1 at 8:59am 4 days Time Limit 180 Minutes Instructions You have 3 hours to complete this Assignment. Ensure you go over your
School: UCF
Course: Statistics For Engineers
10/11/13 Assignment 2a: STA3032_CMB-13Fall Assignment 2a Due Sep 10 at 11:59pm Points 100 Questions 14 Available Sep 6 at 9am - Sep 10 at 11:59pm 5 days Time Limit 180 Minutes Instructions You have 3 hours to complete this Assignment. Ensure you go over y
School: UCF
Course: Statistics For Engineers
10/11/13 Assignment 4a: STA3032_CMB-13Fall Assignment 4a Due Oct 1 at 8:59am Points 100 Questions 10 Available Sep 27 at 9am - Oct 1 at 8:59am 4 days Time Limit 180 Minutes Instructions You have 3 hours to complete this Assignment. Ensure you go over your
School: UCF
Course: Statistics For Engineers
10/11/13 Assignment 3a: STA3032_CMB-13Fall Assignment 3a Due Sep 22 at 11:59pm Points 100 Questions 10 Available Sep 17 at 9am - Sep 22 at 11:59pm 6 days Time Limit 180 Minutes Instructions You have 3 hours to complete this Assignment. Ensure you go over
School: UCF
Course: Statistics For Engineers
10/11/13 Assignment 2b: STA3032_CMB-13Fall Assignment 2b Due Sep 15 at 11:59pm Points 100 Questions 11 Available Sep 10 at 9am - Sep 15 at 11:59pm 6 days Time Limit 180 Minutes Instructions You have 3 hours to complete this Assignment. Ensure you go over
School: UCF
Course: Theory Of Interest
STA4183 Fall 2011 Homework for Chapter 7 Name 1. An investor enters into an agreement to contribute $6000 immediately and $1000 at the end of two years in exchange for the receipt of $4000 at the end of one year and $4000 at the end of three years. a Find
School: UCF
Course: Theory Of Interest
Stat 4183 Fall 2011 Home work # 6 Name Warning: only a sketch of solutions. please spend some time nishing up the numerical calculation, which is required in the exam! 1. Find the price which should be paid for a zero coupon bond that matures for $1000 in
School: UCF
Course: Theory Of Interest
Stat 4183 Fall 2012 Home work # 5 Name Warning: only a sketch of solutions. please spend some time nishing up the numerical calculation, which is required in the exam! 1. A loan of $1000 is being repaid with quarterly payments at the end of each quarter f
School: UCF
Course: Theory Of Interest
Stat 4183 Fall 2011 Home work # 4 Name P rof essor N utcracker 1. Find the accumulated value 20 years after the rst payment is made of an annuity on which there are 8 payments of $1000 each made at two-year intervals. The nominal rate of interest converti
School: UCF
Course: Probability Theory 1
SOLUTIONS. CHAPTER 1 Section 1.2 Section 1.4 Section 1.7
School: UCF
Course: Probability Theory 1
SOLUTIONS. CHAPTER 2 Section 2.2 Section 2.3 Section 2.4
School: UCF
Course: Probability Theory 1
SOLUTIONS. CHAPTER 4 Section 4.2 Section 4.3 Section 4.4 Section 4.5
School: UCF
Course: Probability Theory 1
SOLUTIONS. CHAPTER 5 Section 5.1 Section 5.2
School: UCF
Course: Probability Theory 1
SOLUTIONS. CHAPTER 6 Section 6.1 Section 6.2 Section 6.3
School: UCF
Course: Probability Theory 1
SOLUTIONS. CHAPTER 7 Section 7.1 Section 7.2 \ Section 7.3
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 Theory 1
SOLUTIONS. CHAPTER 9 Section 9.1 Section 9.2
School: UCF
Course: Probability Theory 1
SOLUTIONS. CHAPTER 10 Section 10.1 Section 10.2
School: UCF
Course: Probability
SOLUTIONS. CHAPTER 8 Section 8.1 Section 8.2 Section 8.3 Section 8.4
School: UCF
Course: Probability
SOLUTIONS. CHAPTER 7 Section 7.1 Section 7.2 \ Section 7.3
School: UCF
Course: Probability
SOLUTIONS. CHAPTER 6 Section 6.1 Section 6.2 Section 6.3
School: UCF
Course: Probability
SOLUTIONS. CHAPTER 4 Section 4.2 Section 4.3 Section 4.4 Section 4.5
School: UCF
Course: Probability
SOLUTIONS. CHAPTER 3 Section 3.1 Section 3.2 Section 3.3 Section 3.4 Section 3.5
School: UCF
Course: Probability
SOLUTIONS. CHAPTER 2 Section 2.2 Section 2.3 Section 2.4
School: UCF
Course: Probability
SOLUTIONS. CHAPTER 1 Section 1.2 Section 1.4 Section 1.7
School: UCF
Course: Statistics
STAT 516 HW6 Solutions 1. Answers vary. 2. a) b) c) d) 1 3. Consider the following "data" to be clustered using a variety of methods described below. 10 20 40 80 85 121 160 168 195 For each part of the problem, assume that Euclidean distance will be used
School: UCF
Course: Statistics
Stat 516 Homework 5 Solutions 1. Consider conducting m hypothesis tests. Let V denote the number of type I errors. Let R denote the number of rejected null hypotheses. Let Q = V /R if R > 0, and let Q = 0 if R = 0. Let (0, 1) be xed. By denition, a method
School: UCF
Course: Statistics
Stat 516 1. Homework 3 Solutions (a) (5 points) Expression data were simulated for an experiment involving g = 10, 000 genes and two treatment groups with ve independent experimental units in each treatment group. Gene2 2 specic variances 1 , . . . , g we
School: UCF
Course: Statistics
Stat 516 Homework 2 Solutions 1. See handwritten notes at the end of these solutions. 2. Cut and paste the matrix of numbers to a text le. I saved that le as affypixel.txt. Open R and set the working directory to the directory containing the le. For examp
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
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: 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: 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: Statistical Methods II
Solutions to Practice Problems Problem 14.1: Hypothesis: H0: = 104 Ha: 104 Test Statistic: S+ = 14 S = 6 S = max(S+ , S) = 14 Observed Significance Level: p-value = 2P(S 14) = 2(1 P(S 13) = 2(1 0.942) = 0.116 Conclusion: We can reject the null hypothesis
School: UCF
Course: Statistical Methods II
Solutions to Practice Problems Problem 13.3: (a) df = (r 1)(c 1) = (5)(3) = 15 2 15, 0.05 = 24.9958, i. e. , rejection region is 2 > 24.9958 (b) df = (r 1)(c 1) = (2)(4) = 8 2 8, 0.10 = 13.3616, i. e. , rejection region is 2 > 13.3616 (c) df = (r 1)(c 1)
School: UCF
Course: Statistical Methods II
Solutions to Practice Problems Problem 13.1: (a) 2.10,12 = 18.5494 0 (b) 2.05,14 = 23.6848 0 (c) 2.01,16 = 31.9999 0 (d) 2.005,20 = 39.9968 0 Problem 13.2: (a) Yes. Reasons are as follows: (1) There are 60 trials, and 5 + 8 + 18 + 29 = 60. (2) Each studen
School: UCF
Course: Statistical Methods II
Solutions to Practice Problems Problem 12.13: (a) 1 = 0 + 1 = 8 + 3 = 11 = + = 8 + 5 = 13 2 0 2 3 = 0 + 3 = 8 + 7 = 15 = =8 4 (b) 0 H 0 : 1 = 2 = 3 = 0 H a : at least one i 0 Problem 12.14: (a) 0 = 5 = 1.25 1 = 1 5 = 38.25 (1.25) = 39.5 = = 28.50 (1.2
School: UCF
Course: Statistical Methods II
Solutions to Practice Problems Problem 12.11: (a) E ( y ) = 23.81 + 31.30 x1 + 3.84 x 2 0.275 x1 x 2 (b) Hypothesis: H 0 : 1 = 2 = 3 = 0 H a : At least one i 0 for i = 1,2,3 Test Statistic: Fc = 1695.286 Rejection Region: F > F3,11,0.05 = 3.59 Thus, we re
School: UCF
Course: Statistical Methods II
Solutions to Practice Problems Problem 12.9: Quantitative: Average height of trees in transect, Total number of trees in transect, Height of hedgerow in transect, Width of hedgerow, Width of hedgerow, Width of transect verge, Depth of transect ditch, Widt
School: UCF
Course: Statistical Methods II
Solutions to Practice Problems Problem 12.7: (a) The residual plot looks fine. (b) All the residuals for humidity = 0.2 are below the 0 line and all the residuals for humidity = 0.3 are above the 0 line. The model equal variance assumption of the error te
School: UCF
Course: Statistical Methods II
Solutions to Practice Problems Problem 12.3: (a) Hypothesis: H0: 1 = 2 = 3 = 0 Ha: At least one i 0, i = 1, 2, 3 Test Statistic: Fc = R2 (1 R ) 2 k = [ n ( k + 1) ] 0.85 / 3 = 30.22 (1 0.85) /[ 20 (3 + 1)] Rejection Region: Fc > F0.05, 3, 16 = 3.24 Conclu
School: UCF
Course: Statistical Methods II
Solutions to Practice Problems Problem 12.1: (a) y = 0 + 1x1 + 2x2 + (b) 0 = 0.666667 = 1.316667 1 2 = 8 (c) y = 0.667 + 1.317 x1 8 x2 (d) SSE = 1.345 MSE = 0.14944 s = Root MSE = 0.38658 (e) 1 1.316667 tc = = = 13.191 s 0.09981464 1 Reject the null h
School: UCF
Course: Statistical Methods II
Solution to Practice Problem Problem 11.10: (a) 1894 SS 1 = xy = = 0.766 SS xx 2474 = y x = 76 (0.766)(46) = 40.764 0 1 y = 0 + 1 x = 40.764 + 0.766 x (b) Sure, it looks great. (c) SSE = SS yy 1SS xy = 2056 (0.766)(1894) = 605.196 s2 = MSE = SSE (n 2) =
School: UCF
Course: Statistical Methods II
Solutions to Practice Problems Problem 11.8: (a) y = 0 + 1 xp = 17.388852 + (0.657752)(50) = 50.276 t0.005, 8 = 3.355 2 1 ( xp x ) 1 (50 51.1) 2 sy = s + = 9.9398 + = 3.147 n SS xx 10 5638.9 99% C. I. = y t0.005, 8 sy = 50.276 (3.355)(3.147) = [39.72, 60.
School: UCF
Course: Statistical Methods II
Solution to Practice Problem Problem 11.7: r = SS xy SS xx SS yy = 2670.04 r 2 = (SS yy SSE) SS yy = (25300)(355.50) = 0.89 355.50 73.77 = 0.792 355.50 City Size and Expenditure are highly positive correlated.
School: UCF
Course: Statistical Methods II
Solutions to Practice Problems Problem 11.5: (a) 1 = SSxy SSxx = 0.1055 0 = y 1 x = 67.877 SSE = SS SS = 73.77 yy 1 xy s2 = SSE (n 2) = 12.29 s = s2 = 3.506 s = s SS xx = 0.022 1 (b) It might not have a straight-line relationship. However, the city size a
School: UCF
Course: Statistical Methods II
Solutions to Practice Problems Problem 11.3: e xj = 140 c h= 43.429 26 2 SSxx = x 2 SSxy = xy n x 2 7 e je yj = 129 c h h 39.857 26 c 24 = n 7 e yj = 124 c h= 41.714 24 2 SS yy = y 2 n 2 7 39.857 SS 1 = xy = = 0.918 SS xx 43.429 SSE = SS SS = 41.714 (0.
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: 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: 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
School: UCF
Course: Data Preparation
Welcome to the TTE4004 Page TTE4004: TRANSPORTATION ENGINEERING (Spring 2006) DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING UNIVERSITY OF CENTRAL FLORIDA INSTRUCTOR: OFFICE HOURS: GOAL AND OBJECTIVES: Professor Haitham Al-Deek, Ph.D., P.E., Rm. 209, E
School: UCF
Course: Data Mining Methodology II
STAT 6704 Data Mining II Subject: Lecture: Instruction Period Holidays Instructor: Spring 2008 SYLLABUS Data Mining Methodology II (STA6704) M & W 6:00-7:15 pm, Room 220, CL1 01/07/2008 04/21/2008 1/21/2008, Monday, Martin Luther King Jr.s Day 3/10
School: UCF
Course: Computer Processing Of Statistical Data
STA 4102 Computer Processing of Statistical Data Fall 2008 Section 0001, TuTh 1:30-2:45 PM, CL1 205 Professor: Dr. Roberto Carta Office: Room 212F, Computer Center II Email: rcarta@mail.ucf.edu Webpage: http:/pegasus.cc.ucf.edu/~rcarta Office Hours
School: UCF
Course: Statistical Methods I
STAT 2023 Summer C, 2005 SYLLABUS Subject: Lecture: Dates: Instructor: Statistical Methods I TuTh 2:00-3:50 pm, Room 102, MOD9 05/16/2005 - 08/05/2005 Xiaogang Su Room 102, Computer Center II (407) 823-2940 [O] xiaosu@mail.ucf.edu TuTh 4:00-5:00 pm
School: UCF
Course: Statistical Methods II
STAT 4163 Stat Method II Summer, 2007 Term B SYLLABUS Subject: Lecture: Time Length: Instructor: Statistical Methods II MTuWTr 10:00 - 11:50 am, Room 212, BA 06/25/2006 Mon - 08/03/2007 Fri Holiday: Wednesday, July 4 Dr. Xiaogang Su Room 102 Comput
School: UCF
Course: Statistical Methods III
STAT 4164 Statistical Method III SYLLABUS Subject: Lecture: Instruction Period: Holidays: Fall 2008 Instructor: Office Hours: Text Book: Statistical Methods III (STA4164) MWF 1:30 am - 2:20 pm, Room 147, BA Monday, 08/25/2008 - 12/06/2008 Labor D
School: UCF
Course: Statistical Analysis
STAT 5206 Fall 2008 SYLLABUS Subject: Lecture: Instruction Period: Holidays: Statistical Analysis (STA 5206) MW 6:00-7:15 pm, Room 212, BA Monday, 08/25/2008 - 12/06/2008 Labor Day (Monday, 09/01/2008) Veterans Day (Tuesday, 11/11/2008) Thanksgivin
School: UCF
STA 2014 PRINCIPLES OF STATISTICS SPRING 2006 INSTRUCTOR: OFFICE: OFFICE HOURS: Dr. Xiaogang Su Room 102, Computer Center II (phone: 407-823-2940) Wednesday 2:00 to 4:00 pm Thursday 1:00 to 2:00 pm xiaosu@mail.ucf.edu http:/pegasus.cc.ucf.edu/~xsu/