Online study resources available anywhere, at any time
High-quality Study Documents, expert Tutors and Flashcards
Everything you need to learn more effectively and succeed
We are not endorsed by this school |
- Course Hero has verified this tag with the official school catalog
We are sorry, there are no listings for the current search parameters.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
We don't have any study resources available yet.
School: UConn
Course: Intro To Stat 1
Parag Santhosh Peoplesoft: 1576431 Section 25D Greg Matthews I prefer using a bar chart because it is easy to see the differences in categorical data, and it is easier to compare them to the other categories. When I used a pie chart, it was annoying
School: UConn
Course: Intro To Stat 1
Parag Santhosh Peoplesoft: 1576431 Section 25D Greg Matthews For Class 1, the data resembled a mound-shaped, atleast it was closer to the shape than class 2. The most prevalent score was 65, with over 20 students attaining that score. The mean of the
School: UConn
Course: Statistical Inference I
10.4 303 Analysis of Randomized Complete Block Designs yhi 10000 9000 8000 7000 Label is level of protocol (i) T .2. 1 .3. . . . . . .1. . . . . . . . . .2. . . . 3. . . . .3. . .2. . . . . . . . . .3 . . . . . . .1. . . . . . . . . . . . 1 . . . .2.3. .
School: UConn
Course: Statistical Inference I
10.6 Analysis of General Complete Block Designs 315 signicantly different from each other. At 50% capacity, the only difference that we nd is a difference between brands 1 and 2 in block 1, with brand 2 being superior. Putting together all this informatio
School: UConn
Course: Statistical Inference I
310 Chapter 10 Table 10.7 Complete Block Designs Analysis of variance for the general complete block design with negligible blocktreatment interaction and block size k vs Source of Variation Block Degrees of Freedom b1 bvs b v + 1 Treatment ssE v 1 Error
School: UConn
Course: Statistical Inference I
10.8 317 Factorial Experiments For the blocktreatment interaction model (10.6.6), the (hit)th residual is ehit yhit yhit yhit y hi. . The error assumptions are checked by residual plots, as summarized in Table 10.11 and described in Chapter 5. 10.8 Factor
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375 Review of Calculus and Some Basic Concepts 1 Dierentiation 1.1 Denitions Let f be a function which is dened on some interval (c, d) and let a be some number in this interval. The derivative of the function f at a is the value of the limit f (a)
School: UConn
Course: Introduction To Mathematical Statistics
Review List 1. The nal Exam will have 6 problems. There are no true or false questions. The only two proofs are from the list in the end. Another two problems are from the homework. 2. You are allowed to use TWO pages of formula sheet, double sided. 3. Ca
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375 Review 1 Dierentiation 1.1 Denitions Let f be a function which is dened on some interval (c, d) and let a be some number in this interval. The derivative of the function f at a is the value of the limit f (a) = lim xa f (x) f (a) . xa f is said
School: UConn
Course: Statistical Computing
Globalization Globalizationisanincreaseinconnectedness ofcountriestooneanothereconomically, politicallyandsocially Itisamovementofpeopleacrossborders Globalizationpromotesinterconnectednessand interdependenceofcountries Economicdimension: Integrationofec
School: UConn
Course: Statistical Computing
GenderedIdentitiesinNations andStates 1 WhatareessentialcategoriesofIdentity? Whyarethesecategoriesimportant? Whyistheanalysisofgenderimportant? GenderedIdentitiesinNationsand States 2 Essentialcategoriesofidentitiesinthe nationstate Name Address Gender B
School: UConn
Course: Statistical Computing
Intersectionality 1 The recognition of many strands that make up identity For example the ways in which sexism and racism are intertwined in the identities of women of color that make them have a different experience of gender inequality compared to white
School: UConn
Course: Statistical Computing
School Enrollment in 2000 NU 7.8% Category AG BU ED EG FA NU AG 9.9% FA 14.2% BU 27.8% EG 31.5% ED 8.8% YimingZhangSection015D School Enrollments in 1990 1200 1000 ENROLL 800 600 400 200 0 AG BU ED EG SCHOOL YimingzhangSection015D FA NU Enrollment for 199
School: UConn
Course: Statistical Computing
Yiming Zhang Section-015D 1. Reference: 2. What is the main purpose of the graph? he wealthier a students family is, the higher the SAT score. 3. What type of graph of graph is it - histogram, bar chart.? 4. Does the graph show information about more than
School: UConn
Course: Statistical Computing
Applicants Name: _ ShortAnswerQuestions&Essay Directions: You must format this document by double-spacing your typed response below each question. ShortAnswerQuestions(350 word maximum per question) 1. Why do you want to be an orientation leader? What do
School: UConn
Course: Statistical Computing
One-Sample T: Engineering Test of mu = 589.5 vs not = 589.5 Variable N Mean StDev SE Mean Engineering 28 589.5 95.4 95% CI T P 18.0 (552.5, 626.5) -0.00 0.998
School: UConn
Course: Statistical Inference I
11.2 Table 11.3 343 Design Issues A disconnected incomplete block design with b 8, k 3, v 8, r 3 Block I II III IV 1 2 3 4 3 4 5 6 5 6 7 8 3 5 b b rrd r b d r 7 1 b Figure 11.1 Connectivity graphs to check connectedness of designs Block V VI VII VIII 5 6
School: UConn
Course: Statistical Inference I
342 Chapter 11 Incomplete Block Designs The randomly ordered blocks are shown in the fourth column of Table 11.2. Step (ii): Now we randomly assign time slots within each day to the treatment labels. Again, using pairs of random digits either from a rando
School: UConn
Course: Statistical Inference I
11.2 Table 11.1 341 Design Issues An incomplete block design with b k 3, v 8, r 3 Block I II III IV 1 2 3 4 3 4 5 6 Block V VI VII VIII 8 1 2 3 5 6 7 8 8, 7 8 1 2 4 5 6 7 blocks, labeled I, II, . . ., VIII, each of size k 3, which can be used for an exper
School: UConn
Course: Statistical Inference I
340 Chapter 11 Incomplete Block Designs experiment that was designed as a cyclic group divisible design. Sample-size calculations are discussed in Section 11.8, and factorial experiments in incomplete block designs are considered in Section 11.9. Analysis
School: UConn
Course: Statistical Inference I
1 1 Incomplete Block Designs 11.1 Introduction 11.2 Design Issues 11.3 Analysis of General Incomplete Block Designs 11.4 Analysis of Balanced Incomplete Block Designs 11.5 Analysis of Group Divisible Designs 11.6 Analysis of Cyclic Designs 11.7 A Real Exp
School: UConn
Course: Statistical Inference I
336 Exercises Table 10.29 Data for the exam paper experiment AB 11 12 21 22 Block I (Teaching Assistant 1) Response 92 84 84 81 72 85 31 89 79 47 78 87 47 30 88 60 81 50 86 88 81 68 70 54 77 59 66 47 48 35 83 62 56 70 85 54 61 56 84 AB 11 12 21 22 Block I
School: UConn
Course: Introduction To Mathematical Statistics
l. 'Iiue or False For each statement below, use "a" if you think it is true; " x" if you think it is false. You do not need to justify your answer nor need to show werk. For the following True of False questions, dene X, Y to be continuous random variable
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375: Practice Final Exam 1. Consider the discrete random variable Y with probability function p(y) = 4 , for y = 1, 2, 3, 5y (a) Find P (Y 3 | Y 1). (b) Find P (Y 3 | Y 1). (c) Find the moment generating function of Y . (d) Find E(Y ). (e) Find V (
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375 Practice Midterm Exam I Solution Fall 2013 September 28, 2013 Student Name (Print): _ PeopleSoft ID: _ 1. True or False For each statement below, use " if you think it is true; " if you think it is false. You do not need to justify your answer n
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375: Practice Final Exam 1. Consider the discrete random variable Y with probability function p(y) = (a) (b) (c) (d) (e) 4 , for y = 1, 2, 3, 5y Find P (Y 3 | Y 1). Find P (Y 3 | Y 1). Find the moment generating function of Y . Find E(Y ). Find V (
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375 Practice Midterm Exam I Fall 2013 September 28, 2013 NAME: _ STUDENT ID: _ Please read all of the following information before starting the exam: You are allowed to use ONE page of formula sheet, double sided. Calculator is not needed nor allo
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375 Practice Midterm Exam I Solution Student Name (Print): _ PeopleSoft ID: _ 1. True or False For each statement below, use " if you think it is true; " if you think it is false. You do not need to justify your answer nor need to show work. (a) (b)
School: UConn
Course: Statistical Methods (Calculus Level I)
3.71) 3.75) 3.81) 3.85) 4.2) 4.6)
School: UConn
Course: Statistical Computing
Short Response Answers: 1. I want to be orientation leader because I want to represent how much this campus means to me and hopefully translate that into the incoming freshmen. I love meeting, working, and building relationships with people. Its one of th
School: UConn
Course: Statistical Computing
Local University GPA and SAT scores 4.00 3.75 GPA 3.50 3.25 3.00 2.75 2.50 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 S R-Sq R-Sq(adj) 0.397020 2.5% 0.0% Sat Score YimingZhangSe ction015D Regression Line for GPA AND SAT scores GPA = 2.570 + 0.00028
School: UConn
Course: Statistical Computing
Yiming Zhang Husky CT Assignment: The article by Shelia Grant Bunch discusses takes a look at the different experiences of 23 grandmothers who are providing full time child care to their grandchildren due to military deployment of their own child. The aut
School: UConn
Course: Statistical Computing
Yiming Zhang Group Presentation Response: Domestic Violence in the NFL Our group agreed on this topic because domestic violence resulted in some of the most viral and controversial gender inequality incidents that has occurred this year. The topic has aff
School: UConn
Final Exam Information Instructor: Steven Chiou General Information The nal examination for this course will be given at 10:30 a.m. on Tuesday, December 11 in our regular class room, CB 206. The exam will be roughly the same length of the second midterm.
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375Q: Introduction to Mathematical Statistics Kun Chen Assistant Professor Department of Statistics University of Connecticut Storrs, CT November 21, 2013 Outline I 1 Chapter 6 Function of Random Variables 6.3 The Method of Distribution Functions 6.
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
Statistics 3115Q-01/5315-01 Analysis of Experiments Fall 2014 Textbooks: 1. Applied Regression Analysis and Other Multivariable Methods, 4th Edition, 2008 by Kleinbaum, D. G., Kupper, L.L., Nizam, A. and Muller, K. E., Duxbury Press & Thomson Brooks/Cole.
School: UConn
Course: Mathematical Statistics II
STAT 5685 Spring 2013 STAT 5685: Mathematical Statistics II, Spring, 2013 January 22, 2013 Instructor: Jun Yan Department of Statistics CLAS 328 860/486-3416 jun.yan@uconn.edu Late homework will not be accepted for any reason. You are encouraged to discus
School: UConn
SYLLABUS Dishwasher Safe STAT 3345Q-01: PROBABILITY MODELS FOR ENGINEERS Class Hour and Class Room Class Hour: Tuesdays and Thursdays 11:00am - 12:30pm every week. (Saturday and Sunday: 4:30am-6:00am ) Class Room: CLAS 313. Website for Stat STAT 3345Q-01
School: UConn
SYLLABUS STAT 1100QC : ELEMENTARY CONCEPT OF STATISTICS Class Hour and Class Room Class Hour: Monday, Tuesday, Wednesday, and Thursday - 11:00am - 1:00pm every week from June 1st to July 10th. Lab Hour (with D. Bhattacharjee): Friday - 11:00am -
School: UConn
University of Connecticut STAT 1000, Introduction to Statistics Instructor Oce Phone Email Lectures Class Web Page May 2009 May Session Vladimir Pozdnyakov CLAS 336 (860) 486-6979 Vladimir.Pozdnyakov@uconn.edu Mon/Tue/Wed/Thu/Fri 9:00am-1pm, CLAS 1
School: UConn
Course: Intro To Stat 1
Parag Santhosh Peoplesoft: 1576431 Section 25D Greg Matthews I prefer using a bar chart because it is easy to see the differences in categorical data, and it is easier to compare them to the other categories. When I used a pie chart, it was annoying
School: UConn
Course: Intro To Stat 1
Parag Santhosh Peoplesoft: 1576431 Section 25D Greg Matthews For Class 1, the data resembled a mound-shaped, atleast it was closer to the shape than class 2. The most prevalent score was 65, with over 20 students attaining that score. The mean of the
School: UConn
Course: Statistical Inference I
10.4 303 Analysis of Randomized Complete Block Designs yhi 10000 9000 8000 7000 Label is level of protocol (i) T .2. 1 .3. . . . . . .1. . . . . . . . . .2. . . . 3. . . . .3. . .2. . . . . . . . . .3 . . . . . . .1. . . . . . . . . . . . 1 . . . .2.3. .
School: UConn
Course: Statistical Inference I
10.6 Analysis of General Complete Block Designs 315 signicantly different from each other. At 50% capacity, the only difference that we nd is a difference between brands 1 and 2 in block 1, with brand 2 being superior. Putting together all this informatio
School: UConn
Course: Statistical Inference I
310 Chapter 10 Table 10.7 Complete Block Designs Analysis of variance for the general complete block design with negligible blocktreatment interaction and block size k vs Source of Variation Block Degrees of Freedom b1 bvs b v + 1 Treatment ssE v 1 Error
School: UConn
Course: Statistical Inference I
10.8 317 Factorial Experiments For the blocktreatment interaction model (10.6.6), the (hit)th residual is ehit yhit yhit yhit y hi. . The error assumptions are checked by residual plots, as summarized in Table 10.11 and described in Chapter 5. 10.8 Factor
School: UConn
Course: Statistical Inference I
316 Chapter 10 Complete Block Designs We need to nd the minimum value of s that satises equation (10.6.10); that is, s 2v 2 2 b 2 (2)(5)(0.4)2 2 (3)(0.5)2 2.13 2 . The denominator (error) degrees of freedom for the blocktreatment interaction model is 2 b
School: UConn
Course: Statistical Inference I
318 Chapter 10 Example 10.8.1 Complete Block Designs Banana experiment The objectives section of the report of an experiment run in 1995 by K. Collins, D. Marriott, P. Kobrin, G. Kennedy, and S. Kini reads as follows: Recently a banana hanging device has
School: UConn
Course: Statistical Inference I
320 Chapter 10 Complete Block Designs sstot 9575.92. So, ssE sstot ss ssC ssD ss(CD) 8061.88 , and (bcds 1) (b 1) (c 1) (d 1) (c 1)(d 1) df 47 2 1 1 1 42 . These values are shown in the analysis of variance table, Table 10.13. We can see that the mean squ
School: UConn
Course: Statistical Inference I
10.8 Table 10.12 319 Factorial Experiments Percentage blackened banana skin Experimenter (Block) I Storage D 1 2 1 2 II 1 1 2 2 1 2 1 2 49 57 20 40 60 46 63 47 41 31 64 62 61 34 34 42 III Table 10.13 Light C 1 1 2 2 Percentage of blackened skin (yhijt ) 3
School: UConn
Course: Statistical Inference I
10.9 Table 10.14 Using SAS Software 321 A SAS program for analysis of the cotton-spinning experiment DATA COTTON; INPUT BLOCK TRTMT FLYER TWIST BREAK; LINES; 1 12 1 1.69 6.0 2 12 1 1.69 9.7 : : : : : 13 23 2 1.78 6.4 ; PROC PRINT; ; * block-treatment mode
School: UConn
Course: Statistical Inference I
314 Chapter 10 Complete Block Designs (ii) differences between brands for each capacity and each block separately. In terms of the parameters in the blocktreatment interaction model (10.6.6), the contrast that compares brand 1 with brand 2 averaged over c
School: UConn
Course: Statistical Inference I
10.6 313 Analysis of General Complete Block Designs model (6.2.3) with s observations per cell, formulae for multiple comparisons are similar to those given in (6.5.40), page 164, with a replaced by v and r replaced by s. Thus, a set of 100(1 )% simultane
School: UConn
Course: Statistical Inference I
304 Chapter 10 Table 10.4 Complete Block Designs Data for the resting metabolic rate experiment Subject 1 2 3 4 5 6 7 8 9 1 7131 8062 6921 7249 9551 7046 7715 9862 7812 Protocol 2 6846 8573 7287 7554 8866 7681 7535 10087 7708 3 7095 8685 7132 7471 8840 69
School: UConn
Course: Statistical Inference I
10.5 307 A Real ExperimentCotton-Spinning Experiment 10.5.2 Sample-Size Calculation Since the experimenters were interested in all pairwise comparisons of the effects of the treatment combinations, as well as some other special treatment contrasts, we wil
School: UConn
Course: Statistical Inference I
10.4 305 Analysis of Randomized Complete Block Designs 10.4.2 Multiple Comparisons The blocktreatment model (10.4.1) for the randomized complete block design is similar to the two-way main-effects model (6.2.3) for an experiment with two treatment factors
School: UConn
Course: Statistical Inference I
308 Chapter 10 Table 10.6 Complete Block Designs Analysis of variance for the cotton-spinning experiment Source of Variation Block Treatment Error Total Degrees of Freedom 12 5 60 77 Sum of Squares 177.155 231.034 306.446 714.635 Mean Square 14.763 46.207
School: UConn
Course: Statistical Inference I
306 Chapter 10 Complete Block Designs For each pairwise comparison i p , we have ci2 2, so using the Scheff method e of multiple comparisons and msE= 77217.7 from Table 10.5, the interval becomes i p y .i y .p 2.694 (77217.7)(2)/9 y .i y .p 352.89 . The t
School: UConn
Course: Statistical Inference I
10.6 309 Analysis of General Complete Block Designs y .i Label is type of yer T 11 1. . 10 . . . . 9 1. 8 . . . . . .2. . . 2. . . . 7 6 Figure 10.3 Mean number of breaks per 100 pounds for the cotton-spinning experiment . . . . . . . . . . . 1 2 5 E 1.63
School: UConn
Course: Statistical Inference I
312 Chapter 10 Complete Block Designs y hi. Label is block (h) T 600 1. . . . . . 500 . . . . . . . . . . . .1 1 400 . . 1 300 . 1. . . . .1. . . 200 . 2. .2. . . .2 . 2 2. . . . . .2 Figure 10.4 E Plot of treatment averages for the light bulb experiment
School: UConn
Course: Statistical Inference I
10.6 Table 10.9 311 Analysis of General Complete Block Designs Resistances for the light bulb experiment. Low resistance implies high illumination. (Order of observations is shown in parentheses.) Block I (60 watt) 1 314 (12) 300 (13) 310 (15) 290 (22) 2
School: UConn
Course: Statistical Inference I
322 Chapter 10 Table 10.15 Complete Block Designs SAS output for the blocktreatment model; cotton-spinning experiment The SAS System General Linear Models Procedure Dependent Variable: BREAK Source Model Error Corrected Total Source BLOCK TRTMT DF 12 5 Su
School: UConn
Course: Statistical Inference I
10.9 Table 10.16 323 Using SAS Software SAS output for the factorial main-effects model; cotton-spinning experiment The SAS System General Linear Models Procedure Source BLOCK FLYER TWIST DF 12 1 3 Type I SS 177.15538 130.78205 100.22410 Mean Square 14.76
School: UConn
Course: Statistical Inference I
324 Exercises Table 10.17 SAS program output for the reduced model for the cotton-spinning experiment The SAS System General Linear Models Procedure Dependent Variable: Y Source Model Error Corrected Total DF 14 63 77 Sum of Squares 394.78103 319.85436 71
School: UConn
Course: Statistical Inference I
337 Exercises Table 10.30 Data for the exercise experiment (in heartbeats per minute) listed with order of observation (Ord) AB 11 12 21 22 Block IInfrequent exercise yijkl Age Sex Ord yijkl Age Sex Ord 55 25 0 6 36 34 0 8 74 26 0 17 64 25 0 20 36 26 1 11
School: UConn
Course: Statistical Inference I
336 Exercises Table 10.29 Data for the exam paper experiment AB 11 12 21 22 Block I (Teaching Assistant 1) Response 92 84 84 81 72 85 31 89 79 47 78 87 47 30 88 60 81 50 86 88 81 68 70 54 77 59 66 47 48 35 83 62 56 70 85 54 61 56 84 AB 11 12 21 22 Block I
School: UConn
Course: Statistical Inference I
1 1 Incomplete Block Designs 11.1 Introduction 11.2 Design Issues 11.3 Analysis of General Incomplete Block Designs 11.4 Analysis of Balanced Incomplete Block Designs 11.5 Analysis of Group Divisible Designs 11.6 Analysis of Cyclic Designs 11.7 A Real Exp
School: UConn
Course: Statistical Inference I
340 Chapter 11 Incomplete Block Designs experiment that was designed as a cyclic group divisible design. Sample-size calculations are discussed in Section 11.8, and factorial experiments in incomplete block designs are considered in Section 11.9. Analysis
School: UConn
Course: Statistical Inference I
11.2 Table 11.1 341 Design Issues An incomplete block design with b k 3, v 8, r 3 Block I II III IV 1 2 3 4 3 4 5 6 Block V VI VII VIII 8 1 2 3 5 6 7 8 8, 7 8 1 2 4 5 6 7 blocks, labeled I, II, . . ., VIII, each of size k 3, which can be used for an exper
School: UConn
Course: Statistical Inference I
342 Chapter 11 Incomplete Block Designs The randomly ordered blocks are shown in the fourth column of Table 11.2. Step (ii): Now we randomly assign time slots within each day to the treatment labels. Again, using pairs of random digits either from a rando
School: UConn
Course: Statistical Inference I
11.2 Table 11.3 343 Design Issues A disconnected incomplete block design with b 8, k 3, v 8, r 3 Block I II III IV 1 2 3 4 3 4 5 6 5 6 7 8 3 5 b b rrd r b d r 7 1 b Figure 11.1 Connectivity graphs to check connectedness of designs Block V VI VII VIII 5 6
School: UConn
Foundations of Probability: Part I Cyr Emile MLAN, Ph.D. mlan@stat.uconn.edu Foundations of Probability: Part I p. 1/36 Introduction Text Reference: Introduction to Probability and Its Application, Chapter 2. Reading Assignment: Sections 2.1-2.3, Januar
School: UConn
Foundations of Probability: Part II Cyr Emile MLAN, Ph.D. mlan@stat.uconn.edu Foundations of Probability: Part II p. 1/25 Counting Rules Text Reference: Introduction to Probability and Its Application, Chapter 2. Reading Assignment: Sections 2.4-2.5, Ja
School: UConn
Course: Statistical Inference I
335 Exercises Table 10.28 Data for the insole experiment C 1 1 2 2 D 1 2 1 2 C 1 1 2 2 D 1 2 1 2 899.99 924.92 888.09 884.01 852.94 882.95 920.93 872.50 (3) (2) (4) (1) (22) (21) (26) (23) Block I (Right Leg) Response in Newtons (order) 910.81 (5) 927.79
School: UConn
Course: Statistical Inference I
334 Exercises Table 10.27 Data for the colorfastness experiment Block (Experimenter) 1 Number of Washes 1 2 3 4 5 yhit (Measurement on the Gray Scale) 3.8, 4.0, 4.0, 3.9, 3.8, 3.7, 3.9, 4.0, 4.0, 4.0, 3.9, 4.0 3.0, 3.7, 3.8, 3.0, 3.7, 4.0, 2.9, 3.5, 3.2,
School: UConn
Course: Statistical Inference I
325 Exercises Table 10.18 Respiratory exchange ratio data Subject 1 2 3 4 5 6 7 8 9 1 0.79 0.84 0.84 0.83 0.84 0.83 0.77 0.83 0.81 Protocol 2 0.80 0.84 0.93 0.85 0.78 0.75 0.76 0.85 0.77 3 0.83 0.81 0.88 0.79 0.88 0.86 0.71 0.78 0.72 Source: Bullough, R.
School: UConn
Course: Statistical Inference I
326 Exercises Table 10.19 Data (mpg) for the gasoline pilot experiment (order of observation within block is shown in parentheses) Car/Driver (Block) 1 2 3 87 33.48 (2) 33.23 (2) 32.95 (3) Octane 89 34.20 (3) 33.79 (3) 31.25 (1) 93 35.30 (1) 36.10 (1) 32.
School: UConn
Course: Statistical Inference I
327 Exercises Table 10.20 Data for the candle experiment (in seconds) Color Block Tom Derek Tsai Yang Red 989 1032 1077 1019 899 912 911 943 898 840 955 1005 993 957 1005 982 White 1044 979 987 1031 847 880 879 830 840 952 961 915 987 960 920 1001 Blue 10
School: UConn
Course: Statistical Inference I
330 Exercises 9 in2 (levels of factor D, coded 1, 2). The purpose of the experiment was not to see how close to the 5 cm that subjects could draw, but rather to compare the effects of the shape and area of the border on the length of the lines drawn. The
School: UConn
Course: Statistical Inference I
328 Exercises (b) Complete an analysis of variance table for the data and test for equality of treatment effects. (c) Evaluate whether blocking was worthwhile and whether the assumption of no treatmentblock interaction looks reasonable. (d) Compute sums o
School: UConn
Course: Statistical Inference I
329 Exercises Cue Stimulus at two levels auditory and visual (Factor A, coded 1, 2), and Cue Time at three levels 5, 10, and 15 seconds between cue and stimulus (Factor B, coded 1, 2, 3), giving a total of v 6 treatment combinations (coded 11, 12, 13, 21,
School: UConn
Course: Statistical Inference I
332 Exercises (a) State a suitable model for this experiment and check that the assumptions on your model hold for these data. (b) Use an appropriate multiple comparisons procedure to evaluate which treatment combination is best. (c) Evaluate whether bloc
School: UConn
Course: Statistical Inference I
331 Exercises The experiment was run as a randomized complete block design with four blocks, each block being dened by a different subject. The subjects were selected from the populations of male students in the 2030 year range in a statistics class. (a)
School: UConn
Course: Statistical Inference I
Exercises 333 (c) Calculate a condence interval to compare the execution times of the two algorithms for the largest population size and largest sampling fraction. (Assume that you will want to calculate a large number of condence intervals for various co
School: UConn
Conditional Probability and Independence Cyr Emile MLAN, Ph.D. mlan@stat.uconn.edu Conditional Probability and Independence p. 1/26 Introduction Text Reference: Introduction to Probability and Its Application, Chapter 2. Reading Assignment: Sections 3.1
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series Vector Time Series Copyright: Nalini Ravishanker, Univ. of Connecticut Vector Time Series Let cfw_Xt , t = 0, 1, 2, and cfw_Yt , t = 0, 1, 2, be two time series, with respective means x and y respectively. Joint Stati
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 28 Multifactor Experiments with Randomization Restrictions The Split-Plot Design Example: A paper manufacturer is interested to study the effect of three different methods of preparing the pulp and four different cooking temp
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 24 Miltifactorial Designs - Mixed Models Example: An experiment on diesel engines was conducted to determine the causes of variability in brake specific fuel consumption. Four different engines (E) of the same type and four d
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515Q Handout 26 A General Multi Stage Balanced Nested Design The two-stage nested design can be easily extended to a general m-stage nested design. The following example describes an experiment to be modeled by a three-stage nested desig
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 25 Nested Designs In certain multifactor experiments the levels of one factor (B) are similar but not identical for different levels of another factor (A). A design of that nature is called a nested design or a hierarchical d
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 23 Analysis of a Random Model Example: The factors that influence the braking strength of a synthetic fiber are being studied. Four production machines and three operators were chosen at random and a factorial experiment with
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Model: Handout 22 Yijk = + Ai + Bj + (AB)ij + k(ij), i =1,.,a, j=1,.,b, k=1,.,n. We have discussed this model for the case when both A and B are fixed factors. If both factors are random we will say that it is a random model and if o
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 19 A Single Replicate in a 2k Design In some experiments only one replicate of all the 2k treatments combinations are observed. In that case one cannot analyze all the effects in the model. To address this issue one can emplo
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 21 Fractional Replication for a 2k Factorial Design. Quite often a single replication of a factorial experiment is beyond the resources of an investigator, or there is no real need to execute the entire replicate of the exper
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series Forecasting ARMA Processes Copyright: Nalini Ravishanker, Univ. of Connecticut Forecasting ARMA Processes Consider a stationary and invertible ARMA(p,q) process (B)Xt = (B)wt with MA representation given by Xt = j=0 j w
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series Review of Regression Methods Copyright: Nalini Ravishanker, Univ. of Connecticut Review of Regression Methods A regression model is a mechanism that enables us to describe the eect of one or more explanatory variables (
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series Time Series Regression Copyright: Nalini Ravishanker, Univ. of Connecticut Deterministic Time Series Regression Methods We will study the following: Structural Decomposition Trend Fitting by Polynomial Trend Models Tren
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series State Space Models Copyright: Nalini Ravishanker, Univ. of Connecticut Examples of Time Series Let yt denote a possibly vector-valued time series. Goal: model patterns in yt and predict future values. In some examples,
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series ARIMA Processes Copyright: Nalini Ravishanker, Univ. of Connecticut ARIMA Processes We discuss AR, MA, ARMA and ARIMA models and their properties The process cfw_Xt is an AutoRegressive Moving Average process with AR o
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series Fitting ARIMA Models Copyright: Nalini Ravishanker, Univ. of Connecticut Fitting ARIMA Models There are three broad phases in tting ARIMA models: Model Identication Model Estimation Model Diagnostics - Model Adequacy an
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375 Review of Calculus and Some Basic Concepts 1 Dierentiation 1.1 Denitions Let f be a function which is dened on some interval (c, d) and let a be some number in this interval. The derivative of the function f at a is the value of the limit f (a)
School: UConn
Course: Introduction To Mathematical Statistics
Review List 1. The nal Exam will have 6 problems. There are no true or false questions. The only two proofs are from the list in the end. Another two problems are from the homework. 2. You are allowed to use TWO pages of formula sheet, double sided. 3. Ca
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375 Review 1 Dierentiation 1.1 Denitions Let f be a function which is dened on some interval (c, d) and let a be some number in this interval. The derivative of the function f at a is the value of the limit f (a) = lim xa f (x) f (a) . xa f is said
School: UConn
Course: Statistical Computing
Globalization Globalizationisanincreaseinconnectedness ofcountriestooneanothereconomically, politicallyandsocially Itisamovementofpeopleacrossborders Globalizationpromotesinterconnectednessand interdependenceofcountries Economicdimension: Integrationofec
School: UConn
Course: Statistical Computing
GenderedIdentitiesinNations andStates 1 WhatareessentialcategoriesofIdentity? Whyarethesecategoriesimportant? Whyistheanalysisofgenderimportant? GenderedIdentitiesinNationsand States 2 Essentialcategoriesofidentitiesinthe nationstate Name Address Gender B
School: UConn
Course: Statistical Computing
Reproductive Rights Warm up: What are the things that are included in reproductive health and rights? 1 Reproductive Rights Reproductive health and rights may include: right to birth control right to legal or safe abortion right to access quality repro
School: UConn
Course: Statistical Computing
SocialandHistorical Constructionof Gender Whatmakesmendifferentfrom women? Howdoeswesternsciencedefine thecategoriesofmaleand female? AND Howisgenderinterconnected withothercategoriessuchas race,class,abilityetc.? 1 Biologyvs.culture Feministsareintereste
School: UConn
Course: Statistical Computing
Final Exam Review Sheet Fall 2014 Chapter 1 Know the six characteristics of intimate relationships Understand the nature of sex/gender differences in relationships Understand the importance of intimate relationships for people (why do we need them, what
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
Notes 5: Multiple Regression: Hypothesis Testing 1 Testing Hypotheses in Multiple Regression Basic assumption The following linear regression model holds: Y = 0 + 1 X1 + + k Xk + E, E N (0, ). Overall test: is there any i = 0, i = 1, . . . , k? Test of
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
Notes 2: Simple Regression Example 1 How to predict sons height, given fathers height. Galtons approach Collect data: n pairs (Xi , Yi ), each for one father-son pair, Xi = fathers height, Yi = sons height. Find the curve (straight line, parabola, etc.)
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
Notes 4: Multiple Regression 1 General Framework for Multiple Regression A multiple linear regression model has the form Y = 0 + 1 X 1 + . . . + k X k + E X1 , . . . , Xk : independent or predictor variables; Y : dependent or response variable; E: erro
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
Notes 3: Corr & ANOVA for SLR 1 Sample Correlation Coecient Recall, the population, or statistical , correlation coecient for X and Y is = Cov(X, Y ) X Y The sample correlation coecient for X and Y is r= SXY SX 1 . = SX SY SY r is dimensionless and 1 r
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
Notes 1: Basic Statistics 1 Descriptive Statistics Let X1 , . . . , Xn be n independent measurements on random variable X Sample mean to measure central tendency 1 X= n n Xi . i=1 Sample variance to measure variability 1 S = n1 n 2 i=1 (Xi X)2 Sample st
School: UConn
Course: Intro To Statistics II
Regression Equations Standardized regression equation with one predictor: zy = r zx Centered scores: cy = r(Sy/Sx)cx / cx = r(Sx/Sy)cy Raw Scores: y = r(Sy/Sx)x + My - bMx How much variance is not accounted for? : Correlation of non-determination = 1 r2 C
School: UConn
Course: Intro To Statistics II
Chapter 10: Linear Regression Linear regression o Regression line = where the line with the springs settles o Slope of the line tells you when you see changes across one variable (e.g., shoe size) see change in other variable (e.g., height) *Can quantify
School: UConn
Course: Intro To Statistics II
Chapter 9/Lecture 1: Linear Correlation Linear Correlation o Perfect Correlation what would it look like? E.g., An exact match on one measure with another E.g., Final grades that are all exactly 10 points lower than the midterm E.g., Shoe size and heig
School: UConn
Course: Intro To Statistics II
Chapter 17 Multiple Regression: An extension of linear regression in which there are multiple predictor variables predicting one criterion variable Two Uncorrelated Predictor Variables (two variables, each correlated with the criterion variable, but not w
School: UConn
Course: Intro To Statistics II
Lecture 3 Null Logic- The null hypothesis test is: o Weak the null hypothesis is always wrong o Cowardly it does not test what we think, only what we do not thing o Limited does not provide a model for everyday or applied inference Because we are removin
School: UConn
Chapter 2 - 1 STAT 2215 CHAPTER 2 T-TOOLS Chapter 2 - 2 Inference Using t-distributions This chapter focuses on t-tools These tools are often used in statistics, to make inferences about your data An inference is the act of using a sample data set to m
School: UConn
Chapter 7 - 1 STAT 2215 CHAPTER 7 SIMPLE LINEAR REGRESSION Chapter 7 - 2 Recap from Previous Chapters Chapters 1-5 covered methods for investigating the relationship between a categorical (group) variable and a numeric variable One-Sample t-tools Two-s
School: UConn
STAT 2215 CHAPTER 3 A CLOSER LOOK AT ASSUMPTIONS Chapter 3 - 2 Recap We previously covered some t-tools for looking at the mean of a single group, or comparing two groups. One-Sample t-test, One-Sample t-interval Matched Pairs t-test Two-Sample t-test
School: UConn
Chapter 4 - 1 STAT 2215 CHAPTER 4 ALTERNATIVES TO THE T-TOOLS Chapter 4 - 2 Chapter 4 Goals Introduce some alternatives to the t-Tools Four distribution-free methods Permutation test and Rank-sum test Two independent samples Sign Test and Signed-Rank
School: UConn
Chapter 1 - 1 STAT 2215 CHAPTER 1 DRAWING STATISTICAL CONCLUSIONS Chapter 1 - 2 The purpose of statistics is to examine data to answer questions of interest. Well focus on ways to make statistically valid conclusions from data sets. Statistical inferenc
School: UConn
STAT 2215 CHAPTER 5 COMPARISONS AMONG SEVERAL SAMPLES Chapter 5 - 2 OVERVIEW The previous tools weve seen are useful for making inference about a single mean, or comparing the means of two different groups. But what if we have more than two groups? Such a
School: UConn
STAT 2215 CHAPTER 6 LINEAR COMBINATIONS AND MULTIPLE COMPARISONS OF MEANS Chapter 6 - OVERVIEW In the last chapter we discussed how to test the equality of several group means. While these tests are quite useful, they do not inform us which groups have si
School: UConn
Outline 6.3 The Method of Distribution Functions 6.4 The Method of Transformations 6.5 The Method of Moment-Generatin STAT 3375Q 6.3 6.5 Steven Chiou University of Connecticut Steven Chiou STAT 3375Q 6.3 6.5 University of Connecticut Outline 6.3 The Metho
School: UConn
Outline 6.7 Order Statistics STAT 3375Q 6.7 Order Statistics Steven Chiou University of Connecticut Steven Chiou STAT 3375Q 6.7 Order Statistics University of Connecticut Outline 6.7 Order Statistics 1 6.7 Order Statistics Steven Chiou STAT 3375Q 6.7 Orde
School: UConn
Outline 5.2 Bivariate and Multivariate Probability Distributions STAT 3375Q 5.2 Steven Chiou University of Connecticut Steven Chiou STAT 3375Q 5.2 University of Connecticut Outline 5.2 Bivariate and Multivariate Probability Distributions 1 5.2 Bivariate a
School: UConn
Outline 5.5 The Expected Value of a Function of Random Variables 5.6 Special Theorems STAT 3375Q 5.5-5.6 Steven Chiou University of Connecticut Steven Chiou STAT 3375Q 5.5-5.6 University of Connecticut Outline 5.5 The Expected Value of a Function of Rando
School: UConn
Outline 5.8 The Expected Value and Variance of Linear Functions of Random Variables 5.11 Conditional Expectations STAT 3375Q 5.8 and 5.11 Steven Chiou University of Connecticut Steven Chiou STAT 3375Q 5.8 and 5.11 University of Connecticut Outline 5.8 The
School: UConn
Outline 5.7 The Covariance of Two Random Variables STAT 3375Q 5.7 Steven Chiou University of Connecticut Steven Chiou STAT 3375Q 5.7 University of Connecticut Outline 5.7 The Covariance of Two Random Variables 1 5.7 The Covariance of Two Random Variables
School: UConn
Outline 4.3 Expected Values for Continuous Random Variables 4.4 The Uniform Probability Distribution 4.5 The Norm Probab STAT 3375Q 4.3-4.5 Steven Chiou University of Connecticut Steven Chiou STAT 3375Q 4.3-4.5 University of Connecticut Outline 4.3 Expect
School: UConn
Outline 4.6 The Gamma Probability Distribution STAT 3375Q 4.6 Steven Chiou University of Connecticut Steven Chiou STAT 3375Q 4.6 University of Connecticut Outline 4.6 The Gamma Probability Distribution 1 4.6 The Gamma Probability Distribution Steven Chiou
School: UConn
Outline 4.2 The Probability Distribution for a Continuous Random Variable STAT 3375Q 4.2 Steven Chiou University of Connecticut Steven Chiou STAT 3375Q 4.2 University of Connecticut Outline 4.2 The Probability Distribution for a Continuous Random Variable
School: UConn
Outline 4.7 The Beta Probability Distribution 4.9 Other Expected Values STAT 3375Q 4.7-4.9 Steven Chiou University of Connecticut Steven Chiou STAT 3375Q 4.7-4.9 University of Connecticut Outline 4.7 The Beta Probability Distribution 4.9 Other Expected Va
School: UConn
Outline Section 3.7 - 3.9 Discrete Distributions Summary STAT 3375Q Chapter 3.7-3.9 Steven Chiou University of Connecticut 09/20/2012 Steven Chiou STAT 3375Q Chapter 3.7-3.9 University of Connecticut Outline Section 3.7 - 3.9 Discrete Distributions Summar
School: UConn
Outline Chapter 3 STAT 3375Q Chapter 3.1 - 3.3 Steven Chiou University of Connecticut 09/11/2012 Steven Chiou STAT 3375Q Chapter 3.1 - 3.3 University of Connecticut Outline Chapter 3 1 Chapter 3 3.1 Basic Denition 3.2 The Probability Distribution for a Di
School: UConn
Outline Section 3.5 - 3.6 STAT 3375Q Chapter 3.5-3.7 Steven Chiou University of Connecticut 09/18/2012 Steven Chiou STAT 3375Q Chapter 3.5-3.7 University of Connecticut Outline Section 3.5 - 3.6 1 Section 3.5 - 3.6 3.5 The Geometric Probability Distributi
School: UConn
Outline 3.4 The Binomial Probability Distribution STAT 3375Q Chapter 3, part 2 Steven Chiou University of Connecticut Steven Chiou STAT 3375Q Chapter 3, part 2 University of Connecticut Outline 3.4 The Binomial Probability Distribution 1 3.4 The Binomial
School: UConn
Outline Chapter 2 STAT 3375Q Chapter 2.7 2.9 Steven Chiou University of Connecticut 09/04/2012 Steven Chiou STAT 3375Q Chapter 2.7 2.9 University of Connecticut Outline Chapter 2 1 Chapter 2 2.7 Conditional Probability and the Independence of Events 2.8:
School: UConn
Outline Chapter 2 STAT 3375Q Chapter 2.6 Steven Chiou Department of Statistics University of Connecticut 08/30/2012 Steven Chiou STAT 3375Q Chapter 2.6 Department of Statistics University of Connecticut Outline Chapter 2 1 Chapter 2 2.6 Tools for Counting
School: UConn
Outline Chapter 2 STAT 3375Q Chapter 2 Steven Chiou Department of Statistics University of Connecticut 08/28/2012 Steven Chiou STAT 3375Q Chapter 2 Department of Statistics University of Connecticut Outline Chapter 2 1 Chapter 2 2.3 A Review of Set Notati
School: UConn
Outline Chapter 2 STAT 3375Q Chapter 2.4-2.5 Steven Chiou Department of Statistics University of Connecticut 08/30/2012 Steven Chiou STAT 3375Q Chapter 2.4-2.5 Department of Statistics University of Connecticut Outline Chapter 2 1 Chapter 2 Some extra not
School: UConn
Outline Chapter 1, section 3 STAT 3375Q Chapter 1 Steven Chiou University of Connecticut 08/28/2012 Steven Chiou STAT 3375Q Chapter 1 University of Connecticut Outline Chapter 1, section 3 1 Chapter 1, section 3 Steven Chiou STAT 3375Q Chapter 1 Universit
School: UConn
Course: Introduction To Statistics I
The objective of the sampling is to determine whether there is sufficient evidence to indicate that the fraction defective, p, is less than 0.05. Consequently, we will test the null hypothesis that p=0.05 against the alternative hypothesis that p<0.05. B
School: UConn
Course: Introduction To Statistics I
The least squared lines is one that has the following two properties: o The sum of the errors equals 0 o The sum of squared errors is smaller than for any other straight-line model Errors of prediction- the vertical distances between observed and predic
School: UConn
Course: Introduction To Statistics I
Conditions required a valid large-sample confidence interval for p o A random sample is selected from the target population o The sample size is large Conditions required for Valid Small-Sample Inferences about (mu1-mu2) o The two samples are randomly s
School: UConn
Course: Introduction To Statistics I
If a random sample of n observations is selected from a population with a normal distribution, the sampling distribution of x bar will be a normal distribution Consider a random sample of n observations selected from a population with mean and standard
School: UConn
Course: Introduction To Statistics I
The trials are independent The binomial random variable x is the number of Ss in n trials The standard normal distribution is a normal distribution with mu=0 and sigma=1. A random variable with a standard normal distribution, denoted by the symbol x, i
School: UConn
Course: Introduction To Statistics I
Events A and B are independent events if the occurrence of B does not alter the probability that A has occurred If events A and B are independent, the probability of the intersection of A and B equals the product of the probabilities of A and B If n el
School: UConn
Course: Introduction To Statistics I
The measurement comes from a different population The measurement is correct but represents a rare event Lower quartile- the 25th percentile of a data set. The middle quartile is the median. the upper quartile is the 75th percentile Interquartile range
School: UConn
Course: Introduction To Statistics I
Mean- the sum of the measurements divided by the number of measurements contained in the data sest Median- the middle number when the measurements are arranged in ascending order Skewed- a data set is said to be skewed if one tail of the distribution h
School: UConn
Course: Introduction To Statistics I
Representative sample- exhibits characteristics typical f those possesd by a population of interest Random sample- a sample selected from the population in such a way that every sample of size n has an equal chance of selection Statistical thinking- in
School: UConn
Course: Introduction To Statistics I
Statistics- the science of data. It involves collecting, classifying, summarizing, organizing, analyzing, and interpreting numerical information Descriptive Statistics- utilizes numerical and graphical methods to look for patterns in a data set, to summ
School: UConn
Course: Introduction To Statistics
10/2/08 Statistics Reviewing Test Check grade for statistics test Do minitabs 3 and 4 tonight Email professor if score is not posted Check the extra credit and try Start looking through textbook Discrete countable only in whole numbers Continuous any
School: UConn
Course: Introduction To Statistics
9/23/08 Statistics Exam coming soon Look at syllabus Last time: 1. The compliment of A, AC a. P(A) + P(Ac) =1 b. P(Ac) = 1 P(A) c. P(A)= 1 P(Ac) 2. P(A or B)=P(A) + P(B) P(A and B) 3. If events are mutually exclusive P(A and B)=0 4. If events are mutually
School: UConn
Course: Introduction To Statistics
9/18/08 Statistics Box plot Graphical display using 5-number summary Combinations Factorials o 5! =5*4*3*2*1=120 o 3!=3*2*1=6 o 1!=1 o 0!=1 by definition Combination formula o (n things take (r) amount of time) = nCr= n!/r!(n-r)! o Example: 4 items (A
School: UConn
Course: Statistical Computing
Intersectionality 1 The recognition of many strands that make up identity For example the ways in which sexism and racism are intertwined in the identities of women of color that make them have a different experience of gender inequality compared to white
School: UConn
Course: Statistical Computing
School Enrollment in 2000 NU 7.8% Category AG BU ED EG FA NU AG 9.9% FA 14.2% BU 27.8% EG 31.5% ED 8.8% YimingZhangSection015D School Enrollments in 1990 1200 1000 ENROLL 800 600 400 200 0 AG BU ED EG SCHOOL YimingzhangSection015D FA NU Enrollment for 199
School: UConn
Course: Statistical Computing
Yiming Zhang Section-015D 1. Reference: 2. What is the main purpose of the graph? he wealthier a students family is, the higher the SAT score. 3. What type of graph of graph is it - histogram, bar chart.? 4. Does the graph show information about more than
School: UConn
Course: Statistical Computing
Applicants Name: _ ShortAnswerQuestions&Essay Directions: You must format this document by double-spacing your typed response below each question. ShortAnswerQuestions(350 word maximum per question) 1. Why do you want to be an orientation leader? What do
School: UConn
Course: Statistical Computing
One-Sample T: Engineering Test of mu = 589.5 vs not = 589.5 Variable N Mean StDev SE Mean Engineering 28 589.5 95.4 95% CI T P 18.0 (552.5, 626.5) -0.00 0.998
School: UConn
Course: Statistical Inference I
11.2 Table 11.3 343 Design Issues A disconnected incomplete block design with b 8, k 3, v 8, r 3 Block I II III IV 1 2 3 4 3 4 5 6 5 6 7 8 3 5 b b rrd r b d r 7 1 b Figure 11.1 Connectivity graphs to check connectedness of designs Block V VI VII VIII 5 6
School: UConn
Course: Statistical Inference I
342 Chapter 11 Incomplete Block Designs The randomly ordered blocks are shown in the fourth column of Table 11.2. Step (ii): Now we randomly assign time slots within each day to the treatment labels. Again, using pairs of random digits either from a rando
School: UConn
Course: Statistical Inference I
11.2 Table 11.1 341 Design Issues An incomplete block design with b k 3, v 8, r 3 Block I II III IV 1 2 3 4 3 4 5 6 Block V VI VII VIII 8 1 2 3 5 6 7 8 8, 7 8 1 2 4 5 6 7 blocks, labeled I, II, . . ., VIII, each of size k 3, which can be used for an exper
School: UConn
Course: Statistical Inference I
340 Chapter 11 Incomplete Block Designs experiment that was designed as a cyclic group divisible design. Sample-size calculations are discussed in Section 11.8, and factorial experiments in incomplete block designs are considered in Section 11.9. Analysis
School: UConn
Course: Statistical Inference I
1 1 Incomplete Block Designs 11.1 Introduction 11.2 Design Issues 11.3 Analysis of General Incomplete Block Designs 11.4 Analysis of Balanced Incomplete Block Designs 11.5 Analysis of Group Divisible Designs 11.6 Analysis of Cyclic Designs 11.7 A Real Exp
School: UConn
Course: Statistical Inference I
336 Exercises Table 10.29 Data for the exam paper experiment AB 11 12 21 22 Block I (Teaching Assistant 1) Response 92 84 84 81 72 85 31 89 79 47 78 87 47 30 88 60 81 50 86 88 81 68 70 54 77 59 66 47 48 35 83 62 56 70 85 54 61 56 84 AB 11 12 21 22 Block I
School: UConn
Course: Statistical Inference I
337 Exercises Table 10.30 Data for the exercise experiment (in heartbeats per minute) listed with order of observation (Ord) AB 11 12 21 22 Block IInfrequent exercise yijkl Age Sex Ord yijkl Age Sex Ord 55 25 0 6 36 34 0 8 74 26 0 17 64 25 0 20 36 26 1 11
School: UConn
Course: Statistical Inference I
335 Exercises Table 10.28 Data for the insole experiment C 1 1 2 2 D 1 2 1 2 C 1 1 2 2 D 1 2 1 2 899.99 924.92 888.09 884.01 852.94 882.95 920.93 872.50 (3) (2) (4) (1) (22) (21) (26) (23) Block I (Right Leg) Response in Newtons (order) 910.81 (5) 927.79
School: UConn
Course: Statistical Inference I
334 Exercises Table 10.27 Data for the colorfastness experiment Block (Experimenter) 1 Number of Washes 1 2 3 4 5 yhit (Measurement on the Gray Scale) 3.8, 4.0, 4.0, 3.9, 3.8, 3.7, 3.9, 4.0, 4.0, 4.0, 3.9, 4.0 3.0, 3.7, 3.8, 3.0, 3.7, 4.0, 2.9, 3.5, 3.2,
School: UConn
Course: Statistical Inference I
Exercises 333 (c) Calculate a condence interval to compare the execution times of the two algorithms for the largest population size and largest sampling fraction. (Assume that you will want to calculate a large number of condence intervals for various co
School: UConn
Course: Statistical Inference I
331 Exercises The experiment was run as a randomized complete block design with four blocks, each block being dened by a different subject. The subjects were selected from the populations of male students in the 2030 year range in a statistics class. (a)
School: UConn
Course: Statistical Inference I
332 Exercises (a) State a suitable model for this experiment and check that the assumptions on your model hold for these data. (b) Use an appropriate multiple comparisons procedure to evaluate which treatment combination is best. (c) Evaluate whether bloc
School: UConn
Course: Statistical Inference I
329 Exercises Cue Stimulus at two levels auditory and visual (Factor A, coded 1, 2), and Cue Time at three levels 5, 10, and 15 seconds between cue and stimulus (Factor B, coded 1, 2, 3), giving a total of v 6 treatment combinations (coded 11, 12, 13, 21,
School: UConn
Course: Statistical Inference I
328 Exercises (b) Complete an analysis of variance table for the data and test for equality of treatment effects. (c) Evaluate whether blocking was worthwhile and whether the assumption of no treatmentblock interaction looks reasonable. (d) Compute sums o
School: UConn
Course: Statistical Inference I
330 Exercises 9 in2 (levels of factor D, coded 1, 2). The purpose of the experiment was not to see how close to the 5 cm that subjects could draw, but rather to compare the effects of the shape and area of the border on the length of the lines drawn. The
School: UConn
Course: Statistical Inference I
327 Exercises Table 10.20 Data for the candle experiment (in seconds) Color Block Tom Derek Tsai Yang Red 989 1032 1077 1019 899 912 911 943 898 840 955 1005 993 957 1005 982 White 1044 979 987 1031 847 880 879 830 840 952 961 915 987 960 920 1001 Blue 10
School: UConn
Course: Statistical Inference I
326 Exercises Table 10.19 Data (mpg) for the gasoline pilot experiment (order of observation within block is shown in parentheses) Car/Driver (Block) 1 2 3 87 33.48 (2) 33.23 (2) 32.95 (3) Octane 89 34.20 (3) 33.79 (3) 31.25 (1) 93 35.30 (1) 36.10 (1) 32.
School: UConn
Course: Statistical Inference I
325 Exercises Table 10.18 Respiratory exchange ratio data Subject 1 2 3 4 5 6 7 8 9 1 0.79 0.84 0.84 0.83 0.84 0.83 0.77 0.83 0.81 Protocol 2 0.80 0.84 0.93 0.85 0.78 0.75 0.76 0.85 0.77 3 0.83 0.81 0.88 0.79 0.88 0.86 0.71 0.78 0.72 Source: Bullough, R.
School: UConn
Course: Statistical Inference I
324 Exercises Table 10.17 SAS program output for the reduced model for the cotton-spinning experiment The SAS System General Linear Models Procedure Dependent Variable: Y Source Model Error Corrected Total DF 14 63 77 Sum of Squares 394.78103 319.85436 71
School: UConn
Course: Statistical Inference I
10.9 Table 10.16 323 Using SAS Software SAS output for the factorial main-effects model; cotton-spinning experiment The SAS System General Linear Models Procedure Source BLOCK FLYER TWIST DF 12 1 3 Type I SS 177.15538 130.78205 100.22410 Mean Square 14.76
School: UConn
Course: Statistical Inference I
322 Chapter 10 Table 10.15 Complete Block Designs SAS output for the blocktreatment model; cotton-spinning experiment The SAS System General Linear Models Procedure Dependent Variable: BREAK Source Model Error Corrected Total Source BLOCK TRTMT DF 12 5 Su
School: UConn
Course: Statistical Inference I
10.9 Table 10.14 Using SAS Software 321 A SAS program for analysis of the cotton-spinning experiment DATA COTTON; INPUT BLOCK TRTMT FLYER TWIST BREAK; LINES; 1 12 1 1.69 6.0 2 12 1 1.69 9.7 : : : : : 13 23 2 1.78 6.4 ; PROC PRINT; ; * block-treatment mode
School: UConn
Course: Statistical Inference I
10.8 Table 10.12 319 Factorial Experiments Percentage blackened banana skin Experimenter (Block) I Storage D 1 2 1 2 II 1 1 2 2 1 2 1 2 49 57 20 40 60 46 63 47 41 31 64 62 61 34 34 42 III Table 10.13 Light C 1 1 2 2 Percentage of blackened skin (yhijt ) 3
School: UConn
Course: Statistical Inference I
320 Chapter 10 Complete Block Designs sstot 9575.92. So, ssE sstot ss ssC ssD ss(CD) 8061.88 , and (bcds 1) (b 1) (c 1) (d 1) (c 1)(d 1) df 47 2 1 1 1 42 . These values are shown in the analysis of variance table, Table 10.13. We can see that the mean squ
School: UConn
Course: Statistical Inference I
318 Chapter 10 Example 10.8.1 Complete Block Designs Banana experiment The objectives section of the report of an experiment run in 1995 by K. Collins, D. Marriott, P. Kobrin, G. Kennedy, and S. Kini reads as follows: Recently a banana hanging device has
School: UConn
Course: Statistical Inference I
316 Chapter 10 Complete Block Designs We need to nd the minimum value of s that satises equation (10.6.10); that is, s 2v 2 2 b 2 (2)(5)(0.4)2 2 (3)(0.5)2 2.13 2 . The denominator (error) degrees of freedom for the blocktreatment interaction model is 2 b
School: UConn
Course: Statistical Inference I
10.8 317 Factorial Experiments For the blocktreatment interaction model (10.6.6), the (hit)th residual is ehit yhit yhit yhit y hi. . The error assumptions are checked by residual plots, as summarized in Table 10.11 and described in Chapter 5. 10.8 Factor
School: UConn
Course: Statistical Inference I
310 Chapter 10 Table 10.7 Complete Block Designs Analysis of variance for the general complete block design with negligible blocktreatment interaction and block size k vs Source of Variation Block Degrees of Freedom b1 bvs b v + 1 Treatment ssE v 1 Error
School: UConn
Course: Statistical Inference I
10.6 Analysis of General Complete Block Designs 315 signicantly different from each other. At 50% capacity, the only difference that we nd is a difference between brands 1 and 2 in block 1, with brand 2 being superior. Putting together all this informatio
School: UConn
Course: Statistical Inference I
314 Chapter 10 Complete Block Designs (ii) differences between brands for each capacity and each block separately. In terms of the parameters in the blocktreatment interaction model (10.6.6), the contrast that compares brand 1 with brand 2 averaged over c
School: UConn
Course: Statistical Inference I
10.6 313 Analysis of General Complete Block Designs model (6.2.3) with s observations per cell, formulae for multiple comparisons are similar to those given in (6.5.40), page 164, with a replaced by v and r replaced by s. Thus, a set of 100(1 )% simultane
School: UConn
Course: Statistical Inference I
10.6 Table 10.9 311 Analysis of General Complete Block Designs Resistances for the light bulb experiment. Low resistance implies high illumination. (Order of observations is shown in parentheses.) Block I (60 watt) 1 314 (12) 300 (13) 310 (15) 290 (22) 2
School: UConn
Course: Statistical Inference I
312 Chapter 10 Complete Block Designs y hi. Label is block (h) T 600 1. . . . . . 500 . . . . . . . . . . . .1 1 400 . . 1 300 . 1. . . . .1. . . 200 . 2. .2. . . .2 . 2 2. . . . . .2 Figure 10.4 E Plot of treatment averages for the light bulb experiment
School: UConn
Course: Statistical Inference I
10.6 309 Analysis of General Complete Block Designs y .i Label is type of yer T 11 1. . 10 . . . . 9 1. 8 . . . . . .2. . . 2. . . . 7 6 Figure 10.3 Mean number of breaks per 100 pounds for the cotton-spinning experiment . . . . . . . . . . . 1 2 5 E 1.63
School: UConn
Course: Statistical Inference I
306 Chapter 10 Complete Block Designs For each pairwise comparison i p , we have ci2 2, so using the Scheff method e of multiple comparisons and msE= 77217.7 from Table 10.5, the interval becomes i p y .i y .p 2.694 (77217.7)(2)/9 y .i y .p 352.89 . The t
School: UConn
Course: Statistical Inference I
308 Chapter 10 Table 10.6 Complete Block Designs Analysis of variance for the cotton-spinning experiment Source of Variation Block Treatment Error Total Degrees of Freedom 12 5 60 77 Sum of Squares 177.155 231.034 306.446 714.635 Mean Square 14.763 46.207
School: UConn
Course: Statistical Inference I
10.4 305 Analysis of Randomized Complete Block Designs 10.4.2 Multiple Comparisons The blocktreatment model (10.4.1) for the randomized complete block design is similar to the two-way main-effects model (6.2.3) for an experiment with two treatment factors
School: UConn
Course: Statistical Inference I
10.5 307 A Real ExperimentCotton-Spinning Experiment 10.5.2 Sample-Size Calculation Since the experimenters were interested in all pairwise comparisons of the effects of the treatment combinations, as well as some other special treatment contrasts, we wil
School: UConn
Course: Statistical Inference I
304 Chapter 10 Table 10.4 Complete Block Designs Data for the resting metabolic rate experiment Subject 1 2 3 4 5 6 7 8 9 1 7131 8062 6921 7249 9551 7046 7715 9862 7812 Protocol 2 6846 8573 7287 7554 8866 7681 7535 10087 7708 3 7095 8685 7132 7471 8840 69
School: UConn
Course: Statistical Inference I
10.4 303 Analysis of Randomized Complete Block Designs yhi 10000 9000 8000 7000 Label is level of protocol (i) T .2. 1 .3. . . . . . .1. . . . . . . . . .2. . . . 3. . . . .3. . .2. . . . . . . . . .3 . . . . . . .1. . . . . . . . . . . . 1 . . . .2.3. .
School: UConn
Course: Introduction To Mathematical Statistics
Outline 6.3 The Method of Distribution Functions 6.4 The Method of Transformations 6.5 The Method of Moment-Generatin STAT 3375Q 6.3 6.5 December 2, 2014 STAT 3375Q 6.3 6.5 Outline 6.3 The Method of Distribution Functions 6.4 The Method of Transformations
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375Q Chapter 4.2-4.9 October 12, 2014 STAT 3375Q Chapter 4.2-4.9 October 12, 2014 1 / 60 1 4.2 The Probability Distribution for a Continuous Random Variable 4.2 The Probability Distribution for a Continuous Random Variable 2 4.3 Expected Values for
School: UConn
Course: Introduction To Mathematical Statistics
Outline 6.7 Order Statistics STAT 3375Q 6.7 Order Statistics August 18, 2014 STAT 3375Q 6.7 Order Statistics Outline 6.7 Order Statistics 1 6.7 Order Statistics STAT 3375Q 6.7 Order Statistics Outline 6.7 Order Statistics 6.7 Order Statistics The order st
School: UConn
Course: Introduction To Mathematical Statistics
Outline 5.8 The Expected Value and Variance of Linear Functions of Random Variables STAT 3375Q 5.8 and 5.11 August 18, 2014 STAT 3375Q 5.8 and 5.11 5.11 Conditional Expectations Outline 5.8 The Expected Value and Variance of Linear Functions of Random Var
School: UConn
Course: Introduction To Mathematical Statistics
Outline 5.3 Marginal and Conditional Probability Distributions STAT 3375Q 5.3-5.4 August 18, 2014 STAT 3375Q 5.3-5.4 5.4 Independent Random Variables Outline 5.3 Marginal and Conditional Probability Distributions 5.4 Independent Random Variables 1 5.3 Mar
School: UConn
Course: Introduction To Mathematical Statistics
Outline 5.2 Bivariate and Multivariate Probability Distributions STAT 3375Q 5.2 STAT 3375Q 5.2 Outline 5.2 Bivariate and Multivariate Probability Distributions 1 5.2 Bivariate and Multivariate Probability Distributions 5.2 Bivariate and Multivariate Proba
School: UConn
Course: Introduction To Mathematical Statistics
Outline 5.5 The Expected Value of a Function of Random Variables STAT 3375Q 5.5-5.6 August 18, 2014 STAT 3375Q 5.5-5.6 5.6 Special Theorems Outline 5.5 The Expected Value of a Function of Random Variables 5.6 Special Theorems 1 5.5 The Expected Value of a
School: UConn
Course: Introduction To Mathematical Statistics
Outline 5.7 The Covariance of Two Random Variables STAT 3375Q 5.7 August 18, 2014 STAT 3375Q 5.7 Outline 5.7 The Covariance of Two Random Variables 1 5.7 The Covariance of Two Random Variables 5.7 The Covariance of Two Random Variables STAT 3375Q 5.7 Outl
School: UConn
Course: Introduction To Mathematical Statistics
Outline 4.7 The Beta Probability Distribution STAT 3375Q 4.7-4.9 August 18, 2014 STAT 3375Q 4.7-4.9 4.9 Other Expected Values Outline 4.7 The Beta Probability Distribution 1 4.7 The Beta Probability Distribution 2 4.9 Other Expected Values STAT 3375Q 4.7-
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375Q 4.6 October 16, 2014 STAT 3375Q 4.6 October 16, 2014 1 / 21 1 4.6 The Gamma Probability Distribution STAT 3375Q 4.6 October 16, 2014 2 / 21 4.6 The Gamma Probability Distribution The gamma probability distribution has found applications in vari
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375Q Chapter 2 September 8, 2014 STAT 3375Q Chapter 2 September 8, 2014 1 / 59 2.3 A Review of Set Notation Set Theory Symbol Denition Union Intersection is an element of is not an element of Null Complement Subset C or n(X) Means. All points in the
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375Q Chapter 3.7-3.9 September 25, 2014 STAT 3375Q Chapter 3.7-3.9 September 25, 2014 1 / 54 1 Section 3.7 - 3.9 3.7 The Hypergeometric Probability Distribution 3.8 Poisson Probability Distribution 3.9 Moments and Moment-Generating Functions 2 Discr
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375Q Chapter 2 September 16, 2014 STAT 3375Q Chapter 2 September 16, 2014 1 / 42 1 Chapter 2 2.7 Conditional Probability and the Independence of Events 2.8: Two Laws of Probability 2.9 Calculating the Probability of an Event: The Event-Composition M
School: UConn
Course: Introduction To Mathematical Statistics
Outline 3.4 The Binomial Probability Distribution STAT 3375Q Chapter 3.4 August 18, 2014 STAT 3375Q Chapter 3.4 Outline 3.4 The Binomial Probability Distribution 1 3.4 The Binomial Probability Distribution 3.4 The Binomial Probability Distribution Appendi
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375Q Chapter 3.1 - 3.3 September 15, 2014 STAT 3375Q Chapter 3.1 - 3.3 September 15, 2014 1 / 34 1 Chapter 3 3.1 Basic Denition 3.2 The Probability Distribution for a Discrete Random Variable 3.3 The expected Value of a Random Variable or a Function
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375Q Chapter 3.5-3.7 September 25, 2014 STAT 3375Q Chapter 3.5-3.7 September 25, 2014 1 / 25 1 Section 3.5 - 3.6 3.5 The Geometric Probability Distribution 3.6 The Negative Binomial Probability Distribution STAT 3375Q Chapter 3.5-3.7 September 25, 2
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375Q Chapter 1 August 27, 2014 STAT 3375Q Chapter 1 August 27, 2014 1 / 10 1 Chapter 1, section 3 STAT 3375Q Chapter 1 August 27, 2014 2 / 10 1.3 Characterizing a Set of Measurements: Numerical Methods Denition 1.1 The mean of a sample of n measured
School: UConn
Course: Design Of Experiments
Statistics;-: _ A Nested Factorial Design Example: An investigator who wished to increase the number of rounds per minute that could be fired from a naval gun devised a new loading method (method I) with the intent of improving performance in this task ov
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 9 Point Estimates for Variance Components in One Way Random ANOVA We use the methods of moments approach to derive point estimates for the variance components: 1. To estimate 2 " we use the fact that 2 " E(M SE ) = and theref
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 16 Statistical Analysis of a Four Factorial Fixed Eects Model The model for a four way ANOVA model is Yijklm = + Ai + Bj + Ck + Dl +ABij + ACik + ADil + BCjk + BDjl + CDkl +ABCijk + ABDijl + ACDikl + BCDjkl +ABCDijkl + "m(ikk
School: UConn
Course: Introduction To Mathematical Statistics
l. 'Iiue or False For each statement below, use "a" if you think it is true; " x" if you think it is false. You do not need to justify your answer nor need to show werk. For the following True of False questions, dene X, Y to be continuous random variable
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375: Practice Final Exam 1. Consider the discrete random variable Y with probability function p(y) = 4 , for y = 1, 2, 3, 5y (a) Find P (Y 3 | Y 1). (b) Find P (Y 3 | Y 1). (c) Find the moment generating function of Y . (d) Find E(Y ). (e) Find V (
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375 Practice Midterm Exam I Solution Fall 2013 September 28, 2013 Student Name (Print): _ PeopleSoft ID: _ 1. True or False For each statement below, use " if you think it is true; " if you think it is false. You do not need to justify your answer n
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375: Practice Final Exam 1. Consider the discrete random variable Y with probability function p(y) = (a) (b) (c) (d) (e) 4 , for y = 1, 2, 3, 5y Find P (Y 3 | Y 1). Find P (Y 3 | Y 1). Find the moment generating function of Y . Find E(Y ). Find V (
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375 Practice Midterm Exam I Fall 2013 September 28, 2013 NAME: _ STUDENT ID: _ Please read all of the following information before starting the exam: You are allowed to use ONE page of formula sheet, double sided. Calculator is not needed nor allo
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375 Practice Midterm Exam I Solution Student Name (Print): _ PeopleSoft ID: _ 1. True or False For each statement below, use " if you think it is true; " if you think it is false. You do not need to justify your answer nor need to show work. (a) (b)
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375 Practice Midterm Exam II Fall 2013 November 7, 2013 NAME: _ STUDENT ID: _ Please read all of the following information before starting the exam: You are allowed to use ONE page of formula sheet, double sided. Calculator is not needed nor allow
School: UConn
Course: Statistical Methods (Calculus Level I)
Stat3025StatisticalMethod(CalculusIlevel) FinalExamSample Instructor:BOZHAO Thissamplecontainsafewpracticeproblemsforyoutowarmup,butyoushouldneverexpect toonlyrelyonthefewproblemshere,pleasealsomakesuretoreviewallthelectureexamples andhomework(withtheexam
School: UConn
Course: Statistical Computing
Followup:libraryinformation Comingupwitharesearchtopic Doresearchononeimportantissueofconcerntoglobalfeministmovement,in aparticularplace,andhowcommunitiesinthatplaceareworkingforchange Thinkofwhichtopic/issueyouwanttoinvestigateandaplace Brainstormingver
School: UConn
Course: Statistical Computing
FeministOrganizing Feminists organizing can be defined as social movements around the issues of importance to women and society i.e. campaigns that feed into a network of womens groups and increase awareness of womens problems and rights, such as : womens
School: UConn
Course: Statistical Computing
Topics for Final Exam 1. Measures of Central Tendency what they are and what is the advantage and disadvantage (if any) of each of them. 2. Calculate means and st. deviations for data in a single column or data in table form in two columns. 3. Use the ran
School: UConn
Course: Statistical Computing
One sample hypothesis tests of a proportion or mean and matched pairs t-test 1. A provincial politician claims that at least 80% of parents are satisfied with the public school system. A critic wishes to prove that the politician's claim is wrong and that
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
Statistics 3115&5315 - Solutions to Practice Problems for Midterm Exam 1 Prof. Lynn Kuo Date: September 26, 2014 Name: 1. (a) R2 = SS(M odel)/SS(total) = 100/300 = 0.333, 33.3% of the variation in Y can be explained by the linear regression model, so the
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
Statistics 3115&5315 - Practice Problems for Midterm Exam 1 Prof. Lynn Kuo Date: September 22, 2014 Name: Answer all questions (with explanations if needed) in the spaces provided. 1. Suppose you t the model y = 0 + 1 x1 + 2 x2 + 3 x3 + E to n = 20 data p
School: UConn
School: UConn
School: UConn
Course: Intro To Statistics II
Midterm Practice Problems Hints at the end for #6,12,13,16. 1. You are comparing two groups in terms of depression. You run an analysis and find the MS within groups equals 42.5. Youve also run a ttest to examine wh
School: UConn
Course: Intro To Statistics II
More midterm practice questions: 1 Subjects' recall of a word list is measured both before and after imagery instructions as shown below. What is the value of Pearson's r? No Imagery Imagery
School: UConn
School: UConn
Midterm 2 STAT 3375 - 04 Instructor: Steven Chiou 11/18/2011 Please read all of the following information before starting the exam: You are allowed to use only ONE page of cheat sheet, double sided. Answer each question completely. Show all work, clearl
School: UConn
Course: Mathematical Statistics II
Quiz 3, STAT 5685 Mathematical Statistics II, Spring 2013 Name: Points: 1. (10 points) Suppose that X1 , . . . , Xn form a random sample from U (0, ). Let Xn:n be the largest order statistic. (a) (5 points) Find the UMVUE of 2 . 2 (b) (5 points) Find E [X
School: UConn
Course: Mathematical Statistics II
Quiz 2, STAT 5685 Mathematical Statistics II, Spring 2013 Name: Points: 1. (10 points) Let X be an exponential variable with mean > 0. (a) (5 points) Find IX (), the Fisher information about in X . (b) (5 points) Let = 1/ be the rate parameter of the expo
School: UConn
Course: Mathematical Statistics II
Quiz 1, STAT 5685 Mathematical Statistics II, Spring 2013 Name: Points: 1. (10 points) Let X1 , . . . , Xn be an iid sample from N (, ), where > 0 is unknown. (a) (5 points) Does N (, ) belong to the exponential family? Justify. (b) (5 points) Derive a mi
School: UConn
Course: Mathematical Statistics II
Quiz 5, STAT 5685 Mathematical Statistics II, Spring 2013 Name: Points: 1. (10 points) Suppose that X1 , . . . , Xn form a random sample from N (0, ). (a) (5 points) Find a variance stabilizing transformation for n i=1 n 2 Xi 4 . Note that E (X1 ) = 32 .
School: UConn
Course: Mathematical Statistics II
Quiz 4, STAT 5685 Mathematical Statistics II, Spring 2013 Name: Points: 1. (10 points) Suppose that X1 , . . . , Xn form a random sample from (a, ) distribution with mean a, and a is known. (a) (5 points) Does this distribution has MLR in some sucient sta
School: UConn
Solutions to QUIZ 5 practice problems 5.6 .5 1 .5 .5 1dy1dy2 = [ y1 ]y2 +.5 dy2 = (.5 y2 )dy2 = .125. a. P(Y1 Y2 > .5) = P(Y1 > .5 + Y2 ) = 1 0 y 2 +.5 0 0 1 b. P(Y1Y2 < .5) = 1 P(Y1Y2 > .5) = 1 P(Y1 > .5 / Y2 ) = 1 1 1 1dy1dy2 = 1 (1 .5 / y2 )dy2 .5
School: UConn
STAT 3375 (Fall 2011): Quiz-2 Solutions 1. This was a homework problem. Check homework solutions. 2. This tree diagram is helpful. From the gure, 3. Dene events : It rains on Maries wedding, ( 1 14 ) = 3 . 10 + 1 . 1 + 3 . 3 = 36 8 113 360 : The weatherma
School: UConn
School: UConn
School: UConn
STAT 3345 PROBABILITY MODELS FOR ENGINEERS SPRING 2010 SAMPLE MIDTERM 2 EXAM Problem 1 The lifetime in hours of a certain kind of radio tube is a random variable having a probability density function given by 0 f (y ) = 100 y2 y < 100 if y > 100. (a) Find
School: UConn
STAT 3345 PROBABILITY MODELS FOR ENGINEERS SPRING 2010 SAMPLE MIDTERM 1 EXAM Problem 1 A university librarian produced the following probability distribution of the number of times a student walks into the library over the period of a semester. x p(x) 0 .
School: UConn
School: UConn
SOME SAMPLE EXAMS PROBLEMS Problem 1 Suppose the X and Y are jointly distributed according to the probability density function has the following form f (x, y ) = K x2 + 0, xy , if 0 < x < 1 and 0 < y < 2, 2 otherwise. (a) Find the constant K . (b) Find th
School: UConn
Example. Exercise 2.4 How to read data from the web? 1. 2. 3. Data files for the MINITAB assignments are stored here: http:/www.stat.uconn.edu/~mlan First option. Launch Internet Explorer (not Netscape!). Click Cruise Ship Data. Select Open this file from
School: UConn
Final Exam Statistics 1XX 1. Confidence interval for a population mean . A sample of 49 measurements of tensile strength (roof hanger) are calculated to have a mean of 2.45 and a standard deviation of 0.25. Determine the 95% confidence interval for the me
School: UConn
Quiz # 7 Stat 220, Due Nov 16 Name: 1. A Gallup Poll asked a sample of Canadian adults if they thought the law should allow doctors to end the life of a patient who is in great pain and near death if the patient makes a request in writing. The pol
School: UConn
Quiz # 6 Stat 220, Due Nov 9 Name: 1. The number of column-inches of classified advertisements appearing on Mondays in a certain daily newspaper is roughly normally distributed with mean 327 inches and standard deviation 34 inches. Assume that the
School: UConn
Midterm Exam Statistics 1XX 1. Descriptive statistics: mean, median, standard deviation, IQR etc. 2. Probability rules, conditional probability, independence etc. P (A) = .4, P (B) = .3, and P (A or B) = .58 Are A and B independent? Mutually exclusi
School: UConn
Final Exam Statistics 1XX 1. Confidence interval for a population mean . A sample of 49 measurements of tensile strength (roof hanger) are calculated to have a mean of 2.45 and a standard deviation of 0.25. Determine the 95% confidence interval for
School: UConn
1 Introduction to Hypothesis Testing The purpose of hypothesis testing is to determine whether there is enough statistical evidence in favor of a certain belief about a parameter. Examples 1. Is there statistical evidence in a random sample of pot
School: UConn
Example. Exercise 2.97 How to read data? 1. 2. 3. 4. To read data from the data CD-ROM (that goes with the textbook) click first File Open Worksheet. It opens the Open Worksheet window. Choose Data(*.dat) as Files of type. The data file is on the da
School: UConn
Course: Quant Meth In The Behav Sci
1 STAT379MIDTERMEXAM Spring2007 PleasewriteallanswerssimplyandconciselyasifdirectedtoafellowgradstudentlookingforexplanationNOTasifwritinga textbookchapterorencyclopediaentry.Carryoutanycalculationsto4decimalplacestopreventroundingerror. 1. Onttestsa
School: UConn
Course: Statistical Methods (Calculus Level I)
3.71) 3.75) 3.81) 3.85) 4.2) 4.6)
School: UConn
Course: Statistical Methods (Calculus Level I)
HW#1 STAT 3025 Due Wednesday, Sep 3rd at the beginning of class Note: 1. No late submission. 2. For people who havent purchased textbook, please make sure that you have the correct problem from the book. 1. A stem and leaf plot is given below: 5|6 6| 7 |
School: UConn
Course: Introduction To Mathematical Statistics
3.107 The random variable Y follows a hypergeometric distribution with N = 6, n = 2, and r = 4. 3.127 Let Y = # of typing errors per page. Then, Y is Poisson with = 4 and P(Y 4) = .6288.
School: UConn
Course: Introduction To Mathematical Statistics
3.1. P(Y = 0) = P(no impurities) = 0.2 P A B 1 P Y 0 0.8 , so P Y 2 P A B P A P B P A B 0.1 P(Y = 1) = P(exactly one impurity) = P A P B 2P A B 0.7 3.9 The random variable Y takes on vales 0, 1, 2, and 3. a. Let E denote an error on a single entry and let
School: UConn
Course: Introduction To Mathematical Statistics
Solutions for HW3 2.85(10) 2.89(10) 2.135(20) 2.91(10) 2.95(12) 2.110(10) 2.129(10) 2.131(8) 2.133(10) 2.89 a. 0, since they could be disjoint. b. the smaller of P(A) and P(B). 2.91 If A and B are M.E., P( A B) = P(A) + P(B). This value is greater than 1
School: UConn
Course: Introduction To Mathematical Statistics
2.37 a. There are 6! = 720 possible itineraries. b. In the 720 orderings, exactly 360 have Denver before San Francisco and 360 have San Francisco before Denver. So, the probability is .5. 2.38 By the mn rule, 4(3)(4)(5) = 240. 2.42 There are three differe
School: UConn
Course: Introduction To Mathematical Statistics
Solution Find the derivative of the function: 1. f (x) = (2x 3) 3 (x2 + x + 1)5 2 2 1 2 f (x) = (2x 3) 3 2 (x2 + x + 1)5 + (2x 3) 3 5(x2 + x + 1)4 (2x + 1) 3 2 4(x2 + x + 1)5 = + 5(2x 3) 3 (x2 + x + 1)4 (2x + 1) 1 3(2x 3) 3 (x2 + x + 1)4 = 4(x2 + x + 1) +
School: UConn
Course: Intro Mathematical Statistics
Hw1 Stat 3445 s2013 due at the beginning of class Thursday, Jan. 30 A. Suppose that Z has a standard normal distribution and Y = a + bZ, where b > 0. Find the density f (y) of Y and identify the distribution of Y by name. B. Suppose that X, Y are random v
School: UConn
Course: Intro Mathematical Statistics
Hw1 - Stat 3445 s2015 due at the beginning of class Thursday Jan. 29 Z has a standard normal distribution and Y: a*bZ,where the density /(y) of Y and identify the distribution of Y by name. B. Suppose that X, Y are independent random variables with densit
School: UConn
Course: Intro Mathematical Statistics
HwZ - Stat 3445 s2015 due at the beginning of class Thursday, Feb. 5 Notes: o Reminder: work must be shown to receive credit. o Results derived in cla.ss or in the text may be used without including a proof. o 'oldentify" means to give the family of the d
School: UConn
Course: Statistical Methods (Calculus Level I)
7.1 Basic Properties of Confidence Inten/als 275 In general, the upper and lower condence limits result from replacing each < in (7.6) by = and solving for 6. In the insulating uid example just considered, ZAEXl- = 34.170 gives A = 34.170/(221g) as the up
School: UConn
Course: Statistical Methods (Calculus Level I)
Chapter 4: Continuous Random Variables and Probability Distributions a. f(x = 1% for 25 S x S 35 and = 0 otherwise. b. P(X> 33): Lsdx=2 3 3 20 25 midpoint.) 30 i 2 is from 28 to 32 minutes: ' 32 ' 32 P08 <X< 32) = In +de =+0xllzs = 4' I 2 35 c. E(X) = Ex
School: UConn
Course: Statistical Methods (Calculus Level I)
(A) El nmmWsln-hw 2,712 medn 7- /0.S 623.113.; Mn=$6 mxslg'o (c) LlF= 2.7: (/flf 2/175 => 4/0 awfme Chapter 1: Overview and Descriptive Statistics 60. A comparative boxplot (created in Minitab) of this data appears below. The burst strengths for the tes
School: UConn
Course: Statistical Methods (Calculus Level I)
18. 19. Chapter 3: Discrete Random Variables and Probability Distributions u 3- 170) = P(M= 1) = P(cfw_(1,1)) = % ;p(2) = P(M= 2) = P(cfw_(1,2)(2,1)(2,2)) = 33g; 17(3) = P(M z 3) = P (cfw_(1,3)(2,3)(3,1)(3,2)(3,3) = 35g . Continuing the pattern, p(4) =
School: UConn
Course: Statistical Methods (Calculus Level I)
I Chapter 6: Point Estimation c With [2, i, ] =l~fyl , 132 x2, (22 =1[72 , the estimated standard error is plq + pzqz "I "2 n1 n2 ,. . I27 176 d. = =.635.880 = .245 0" p2) 200 200 . . e_ (.635)(.365) + (.880)(.120) z 041 200 200 E W =mE(SIZ)+E(SZZ) :
School: UConn
Course: Statistical Methods (Calculus Level I)
254 CHAPTER 6 Point Estimation An appropriate probability plot supports the use of the log- normal distribution (see Section 4.5) as a reasonable model for stream ow. a. Estimate the parameters of the distribution. [Hint Remember that X has a lognormal di
School: UConn
Course: Statistical Methods (Calculus Level I)
CHAPTER 7 Section 7.1 . 2,12 = 2.81 implies that (ii/2 = l (D(2.81) = .0025, so a = .005 and the condence level is 100(la)% = 99.5%. zwz = 1.44 implies that a = 2[l @(1.44)] = .15, and the condence level is 100(1-a)% = 85%. P b. c. 99.7% condence implie
School: UConn
Course: Statistical Methods (Calculus Level I)
M3023 M w I W? W 7 (4) ; Mvziwo v5 Ha: ULK/SW 7 0 Z = 1716:; and Z; 7- ~2a f_,_j,: 94>;wje4af aiw , ' U PW?H?SB>5 W Z? tiiwiiQ,E!,f%-39:EE'fS3 i 4 e) h<~; _> F9477 _ _., A. A #7.". _, _ V- . _ .m _._.A.,_,_, ,7, L420; M6) = (~90, Y? 34%
School: UConn
Course: Statistical Computing
Yiming Zhang Husky CT Assignment: Amanda K. Baumle wrote this article Legislating the Family: The Effect of State Family Laws on the Presence of Children in Same-Sex Households due to the United States announcing several laws that prohibits gay male and l
School: UConn
Course: Statistical Computing
Yiming zhang Assignment #4 12/3/13 TBI Video Response 1. What video did you watch? Traumatic Brain Injury: Josh's Story 2. How did the patient injure his/her brain? Josh was on a night out with his friends when drunk driving lead to a car crash accident 3
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
Hw1 Solution: 11: a) P( Z > 5 ) b) a = 187.44, b = 192.56 12: Multiple is t0.975,27 = 2.052 13: (24.66,35.33) 16: T = -0.289, we cannot reject H0 for =0.05. 18: b) 20: a) 21: c) 22: b) 23: a). 1- b). c). d). 1- 24: If the significance level for the hypo
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
Fall 2014 Statistics 3115Q-01/5315-01 Homework Assignments Professor Lynn Kuo THIS IS A TENETATIVE LIST. IT MAY BE UPDATED SEVERAL TIMES DURING THE SEMESTER. SO YOU SHOULD ALWAYS CHECK YOUR HUSKYCT OR YOUR EMAIL BEFORE DOING THE HOMEWORK. Textbook: Applie
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
STAT 3115Q 12.1 a.) HW#5 For Nonsmokers (SMK=0) For Smokers (SMK=1) Y=49.311759+26.302825X Y=79.255330+20.118041X 12.5 12.6 12.8* /* sbpage.sas, options LS=72; title 'ANACOVA data SBP; input QUET SBP cards; 2.876 135 3.251 122 3.1 3.768 148 2.979 146 2.79
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
STAT 3115Q HW#3 Jordan Parley 8.2 a) Patient 5 had an actual score of 25 while the best fit line predicted Y= 16.09. The prediction value is not very reliable at this data point. b) Pathology Score from Thinking Disturbance & Hostile Suspiciousness The RE
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
5.3 a) It seems that a parabola is the best fit. Not a linear relationship b) Parameter Estimates Variable D Parameter F Estimate Standard t Value Pr > |t| Error Intercept 1 19.62575 5.21291 3.76 0.0014 Income 1 0.0007137 6 0.0003528 1 2.02 0.0582 Bo= 19.
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
STAT 3115Q HW#4 Jordan Parley 10.1 a. Based on the matrix, the largest proportion of the total variation in the dependent variable SBP is explained by AGE with largest r=.7752 OPTIONS LS=72; TITLE 'Systolic Blood Pressure Analysis'; DATA NUTRIDEF; INPUT S
School: UConn
Course: Intro To Statistics II
Homework 2 Due Wednesday March 6 Use the Data Set called hot hw2. It contains 16 variables including 8 personality variables. The file is in SPSS and the variables should all be self-explanatory 1) Compute the correlations among the 16 variables and creat
School: UConn
School: UConn
School: UConn
llvcfw_b S oZ,f.-o 3 o Zcfw_ szot> 6.t5e1Wtu-*-fr,2*/.(r,t*-g t tuse.-zrc i -i= : e a G.e f r" f -V.z.t; 2 r.' w z 4e ? _ _F .rro7 7 6.2 C l , u t a -x = t d / n , - . t L < ' t a 7 t 6 - , a t t/ -qE-.rn'-, , ; /- x. - = !b! r t =.la tb) l l-,.- Y - 4; ,
School: UConn
32 The parameter of interest is = the true average dietary intake of zine among males aged 6574 years. The hypotheses are H0 : = 15 vs. Ha : < 15. Since the sample size n = 115 is large, z -test is used. Since this is a lower-tailed test, we need to compa
School: UConn
Middle East Technical University Electrical and Electronics Engineering Department EE230 Homework 5 Due : Apr. 28, 2006 1. Let the random variable x be uniformly distributed over (0,3) and the function be ~ defined as follows: , x0 0 x , 0 x 1 g (x ) = 1
School: UConn
School: UConn
School: UConn
School: UConn
School: UConn
School: UConn
School: UConn
School: UConn
School: UConn
School: UConn
School: UConn
Instructor's Solutions Manual for Applied Regression Analysis and Other Multivariable Methods Chapter 9 c The two variables-added-in-order tests are: i H0: fr = 0 vs. HA: fa. + 0 in the model Y= fa + faX2 + E. From part (a) above: F= 0.59; df:l,51
School: UConn
Instructor's Solutions Manual for Applied Regression Analysis and Other Multivariable Methods Chapter 16 Chapter 16 Note: wherever possible, values used in the solutions below are taken directly from the SAS output provided in the text. 1. a Forwar
School: UConn
Instructor's Solutions Manual for Applied Regression Analysis and Other Multivariablc Methods Chapter 5 Chapter 5 Note: wherever possible, values used in the solutions below are taken directly from the SAS output provided in the text. 1. a Dry weig
School: UConn
Course: Statistical Computing
Short Response Answers: 1. I want to be orientation leader because I want to represent how much this campus means to me and hopefully translate that into the incoming freshmen. I love meeting, working, and building relationships with people. Its one of th
School: UConn
Course: Statistical Computing
Local University GPA and SAT scores 4.00 3.75 GPA 3.50 3.25 3.00 2.75 2.50 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 S R-Sq R-Sq(adj) 0.397020 2.5% 0.0% Sat Score YimingZhangSe ction015D Regression Line for GPA AND SAT scores GPA = 2.570 + 0.00028
School: UConn
Course: Statistical Computing
Yiming Zhang Husky CT Assignment: The article by Shelia Grant Bunch discusses takes a look at the different experiences of 23 grandmothers who are providing full time child care to their grandchildren due to military deployment of their own child. The aut
School: UConn
Course: Statistical Computing
Yiming Zhang Group Presentation Response: Domestic Violence in the NFL Our group agreed on this topic because domestic violence resulted in some of the most viral and controversial gender inequality incidents that has occurred this year. The topic has aff
School: UConn
Final Exam Information Instructor: Steven Chiou General Information The nal examination for this course will be given at 10:30 a.m. on Tuesday, December 11 in our regular class room, CB 206. The exam will be roughly the same length of the second midterm.
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375Q: Introduction to Mathematical Statistics Kun Chen Assistant Professor Department of Statistics University of Connecticut Storrs, CT November 21, 2013 Outline I 1 Chapter 6 Function of Random Variables 6.3 The Method of Distribution Functions 6.
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
Statistics 3115Q-01/5315-01 Analysis of Experiments Fall 2014 Textbooks: 1. Applied Regression Analysis and Other Multivariable Methods, 4th Edition, 2008 by Kleinbaum, D. G., Kupper, L.L., Nizam, A. and Muller, K. E., Duxbury Press & Thomson Brooks/Cole.
School: UConn
Course: Mathematical Statistics II
STAT 5685 Spring 2013 STAT 5685: Mathematical Statistics II, Spring, 2013 January 22, 2013 Instructor: Jun Yan Department of Statistics CLAS 328 860/486-3416 jun.yan@uconn.edu Late homework will not be accepted for any reason. You are encouraged to discus
School: UConn
SYLLABUS Dishwasher Safe STAT 3345Q-01: PROBABILITY MODELS FOR ENGINEERS Class Hour and Class Room Class Hour: Tuesdays and Thursdays 11:00am - 12:30pm every week. (Saturday and Sunday: 4:30am-6:00am ) Class Room: CLAS 313. Website for Stat STAT 3345Q-01
School: UConn
SYLLABUS STAT 1100QC : ELEMENTARY CONCEPT OF STATISTICS Class Hour and Class Room Class Hour: Monday, Tuesday, Wednesday, and Thursday - 11:00am - 1:00pm every week from June 1st to July 10th. Lab Hour (with D. Bhattacharjee): Friday - 11:00am -
School: UConn
University of Connecticut STAT 1000, Introduction to Statistics Instructor Oce Phone Email Lectures Class Web Page May 2009 May Session Vladimir Pozdnyakov CLAS 336 (860) 486-6979 Vladimir.Pozdnyakov@uconn.edu Mon/Tue/Wed/Thu/Fri 9:00am-1pm, CLAS 1
School: UConn
University of Connecticut STAT 1000, Introduction to Statistics Instructor Office Phone Email Office Hours Lectures Section 71 Section 72 Discussions/Computer Lab Section 71 Section 72 Class Web Page Fall 2008 Section 71, 72 Vladimir Pozdnyakov
School: UConn
University of Connecticut STAT 3345Q, Probability Models for Engineers Instructor Office Email Office Hours Lectures Class Web Page Text Syllabus Spring 2009 Section 001 Vladimir Pozdnyakov CLAS 336 vladimir.pozdnyakov@uconn.edu Tu/Th 12:30-130