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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
Chapter 2: 3. Three components are connected to form a system as shown in the accompanying diagram. Because the components in the 23 subsystem are connected in parallel, that subsystem will function if at least one of the two individual components functio
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: Introduction To Mathematical Statistics
Covariance, Expectation of Linear Combinations, & Conditional Expectation 1 Covariance 1.1 Motivation While the joint PDF of two random variables fully describes the relationship between two random variables, we would like to have a measurements to summa
School: UConn
Course: Introduction To Mathematical Statistics
Indepence & Expectation 1 Indepence 1.1 Motivation Much like how we discussed the concept of two events being independent of each other, we can also discuss the idea of random variables being independent When we say that two random variables are indepen
School: UConn
Course: Introduction To Mathematical Statistics
Marginal & Conditional Distributions 1 Motivation We began this section of the course discussing the situation in which we take multiple measurements at the same time and would like to model how the relate to each other In these cases sometimes we would
School: UConn
Course: Introduction To Mathematical Statistics
Multivariate Distributions 1 Motivation Often when we are studying a particular topic we will take various measures that pertain to that topic From hre the goal is usually to examine these measures and see what they tell us about the topic we are studyi
School: UConn
Course: Introduction To Mathematical Statistics
The Gamma Distirbution 1 Denition & Motivation Our last continuous Distribution is the Beta distribution The Beta distribution is very useful for modeling bounded random variables with a non uniform distribution of probability The Beta distribution is
School: UConn
Course: Introduction To Mathematical Statistics
The Gamma Distirbution 1 Denition & Motivation Often times we would like to model a continuous Random variable that only has a positive support In these cases, the Normal distribution is not an appropriate model because a Normally distibruted Random Var
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: Elem Concepts Of Stats
Basic Rules for Finding Probabilities Basic Rules for Suppoﬁe an ’chhw occurs. Wk is +k¢ «prob. M‘— m SMW mag 49 \M méowe ? mm- m:=‘33 .055; Com‘wm‘r: Pkkﬁﬁ) =\—.33=.¢1 .00? PM“ 7 . a .OS‘S'W my; ' mm Probability Examples - Example: If there are 2500 i
School: UConn
Course: Introduction To Statistics I
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: Elem Concepts Of Stats
Answer Key Test #1 Sections 15-24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Version 1 E C B B A B D B B B D C D B E B C B C Version 2 A B D E C B B D C D B B B B C B B E C Version 3 D B A E B C A D D D A A C C B A B D C Version 4 C E E A C A C B A C
School: UConn
Course: Elem Concepts Of Stats
Here is an interesting and fun example you can do ahead of time. It will appear (with one or two small changes) on Test#2 as the final question. The only changes will be the dollar amount ($10 million will be different) and which Team you are asked to cal
School: UConn
Course: Probability And Statistics Problems
Chapter 2: Probability 2.1 A = cfw_FF, B = cfw_MM, C = cfw_MF, FM, MM. Then, AB = 0 , BC = cfw_MM, C B = / cfw_MF, FM, A B =cfw_FF,MM, A C = S, B C = C. 2.2 a. AB b. A B c. A B d. ( A B ) ( A B ) 2.3 2.4 a. b. 8 Chapter 2: Probability 9 Instructors Soluti
School: UConn
Course: Probability And Statistics Problems
Chapter 15: Nonparametric Statistics 15.1 Let Y have a binomial distribution with n = 25 and p = .5. For the twotailed sign test, the test rejects for extreme values (either too large or too small) of the test statistic whose null distribution is the same
School: UConn
Course: Probability And Statistics Problems
Chapter 13: The Analysis of Variance 13.1 2 The summary statistics are: y1 = 1.875, s12 = .6964286, y 2 = 2.625, s 2 = .8392857, and n1 = n2 = 8. The desired test is: H0: 1 = 2 vs. Ha: 1 2, where 1, 2 represent the mean reaction times for Stimulus 1 and 2
School: UConn
Course: Probability And Statistics Problems
Chapter 14: Analysis of Categorical Data 14.1 a. H0: p1 = .41, p2 = .10, p3 = .04, p4 = .45 vs. Ha: not H0. The observed and expected counts are: A B AB O observed 89 18 12 81 expected 200(.41) = 82 200(.10) = 20 200(.04) = 8 200(.45) = 90 The chisquare s
School: UConn
Course: Intro To Stats
QMz#1 Name: 1. The ages of a sample of 25 salespersons are as follows: 21 24 24 26 28 28 30 3O 31 32 34 34 35 35 37 38 4O 41 43 45 45 45 47 53 56 Manually draw a histogram with six classes. Range. 510 é“? 56‘ l”! =- 85 2. Consider the following artificial
School: UConn
Course: Intro To Stats
’1. Consider the following artificial data set: 5, 5, -1o, 6, —3, —3 I What is the sample size? SO’Z Find the sample mean. . _ 3 . 3 5‘, 5) 6‘ I l I Find the median. Find the range. Find the sample variance 32. Find the sample standard deviation 5. Find
School: UConn
Course: Elem Concepts Of Stats
6,453 Perez Votes: No Vindication - Courant.com Page 1 of 2 courant.com/news/opinion/commentary/hc-cun'yl 1 l 1 .artnovl lcol,0,1988296.column Courant.com 6,453 Perez Votes: No Vindication Bill Curry November I 1, 2007 On Tuesday, Hartford, New Haven and
School: UConn
Course: Elem Concepts Of Stats
Project Ideas: Here are some topics from previous semesters. Hopefully this will give you some ideas to think about. You can do a study that has been done in the past so you can actually choose one of these topics if you would like to. Also, I have many p
School: UConn
Course: Applied Time Series
STAT 5825 HW4 Heng Yan Problem 1. [ ( s , t )=E ( x sE [ x s ] )( x t E [ x t ] ) ] E [ ( x su s ) ( x tu t ) ] E ( x s x t ut x sus xt +us ut ) E ( x s x t )E ( ut x s ) E ( u s x t ) + E ( u s ut ) E ( x s x t )ut E ( x s ) u s E ( x t ) +u s ut E
School: UConn
Course: Applied Time Series
STAT 4825 HW3 Jiayue Ding 1. (a) M3: additive Holt-Winters procedure allowing for level, trend and seasonality updating. R output: Call: HoltWinters(x = jj1, seasonal = "additive") Smoothing parameters: alpha: 0.2419952 beta : 0.1429747 gamma: 0.7763187 C
School: UConn
Course: Applied Statistics I
1.0 0.6 0.8 We consider two possible values for the population mean (null and alternative) a 0.0 0.2 0.4 0 4 2 0 2 4 6 1.0 0.6 0.8 Using the null hypothesis, set up a rejection region (based on the (null) mean and the confidence level) The red segment in
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
Course: Introduction To Mathematical Statistics
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: Applied Statistics I
Applied Statistics I STAT5505-002 Fall 2015: Tu Th 9:30am - 10:45am; AUST 313 Instructor: Oce Hours: Grader: Elizabeth Schifano <elizabeth.schifano@uconn.edu> AUST 317; Tuesday 10:45am-11:45am or by appointment John (Anthony) Labarga <john.labarga@uconn.e
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Spring 2015 SYLLABUS I Instructor: J. Glaz Office Hours: Tu & Th 12:45-1:45 AUST 323B Textbook: Design and Analysis of Experiments. Douglas C. Montgomery, 8th edition, Wiley Recommended Primer for SAS: The Little SAS Book - a Primer,
School: UConn
Course: Intro To Stats
University of Connecticut Fall 2013 STAT 1000, Introduction to Statistics Section 71, 72 Instructor Oce Email Oce Hours Vladimir Pozdnyakov HART 121 Vladimir.Pozdnyakov@uconn.edu Tue/Thu 12:30-1:30pm Lectures Section 71 Section 72 Tue/Thu 9:30-10:45am,
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
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
Chapter 2: 3. Three components are connected to form a system as shown in the accompanying diagram. Because the components in the 23 subsystem are connected in parallel, that subsystem will function if at least one of the two individual components functio
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
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: Statistics For Enginering
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
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
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
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
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
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
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 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
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: Introduction To Mathematical Statistics
Covariance, Expectation of Linear Combinations, & Conditional Expectation 1 Covariance 1.1 Motivation While the joint PDF of two random variables fully describes the relationship between two random variables, we would like to have a measurements to summa
School: UConn
Course: Introduction To Mathematical Statistics
Indepence & Expectation 1 Indepence 1.1 Motivation Much like how we discussed the concept of two events being independent of each other, we can also discuss the idea of random variables being independent When we say that two random variables are indepen
School: UConn
Course: Introduction To Mathematical Statistics
Marginal & Conditional Distributions 1 Motivation We began this section of the course discussing the situation in which we take multiple measurements at the same time and would like to model how the relate to each other In these cases sometimes we would
School: UConn
Course: Introduction To Mathematical Statistics
Multivariate Distributions 1 Motivation Often when we are studying a particular topic we will take various measures that pertain to that topic From hre the goal is usually to examine these measures and see what they tell us about the topic we are studyi
School: UConn
Course: Introduction To Mathematical Statistics
The Gamma Distirbution 1 Denition & Motivation Our last continuous Distribution is the Beta distribution The Beta distribution is very useful for modeling bounded random variables with a non uniform distribution of probability The Beta distribution is
School: UConn
Course: Introduction To Mathematical Statistics
The Gamma Distirbution 1 Denition & Motivation Often times we would like to model a continuous Random variable that only has a positive support In these cases, the Normal distribution is not an appropriate model because a Normally distibruted Random Var
School: UConn
Course: Introduction To Mathematical Statistics
The Normal Distribution 1 Denition & Motivation Known by several names including Normal, Gaussian, Bell curve/distribution (here we will call it the Normal distirbution), the Normal distribution is probably the most inuential and impactful distribution i
School: UConn
Course: Introduction To Mathematical Statistics
The Gamma Function 1 Denition Here we will go over the Gamma Function, a function used to verify the Gamma distribution and the Normal distribution. Denition 1. The Gamma Function is the function (dened for non-negative values) such that (t) = xt1 ex dx
School: UConn
Course: Introduction To Mathematical Statistics
Continuous Uniform Random Variables 1 Motivation & Denition We will begin to examine various Continuous Random variables, and we will start with one of the Simplest, the continuous Uniform Random Variable Suppose that you are waiting for the bus and the
School: UConn
Course: Introduction To Mathematical Statistics
Continuous Random Variables 1 Denition Now that we have discussed Discrete Random Variables we will now move on to continuous Random Variables Like Discrete Random Variables, continuous Random Variables are numerical representations of the outcomes of a
School: UConn
Course: Introduction To Mathematical Statistics
Moment Generating functions (MGFs) 1 Denitions Before we can discuss what a moment generating function is, we must rst dene what we mean by a moment Denition 1. Let k be a non-negative integer, and let X be a random variable with support S and PDF pX (x)
School: UConn
Course: Introduction To Mathematical Statistics
Hypergeometric & Poisson 1 Hypergeometric 1.1 Description & Denition Earlier we discussed the binomial distribution in terms of ipping a coin repeatedly, or ipping multiple coins at the same time. Either way it was done such that the ips were independent
School: UConn
Course: Introduction To Mathematical Statistics
Geometric & Negative Binomial 1 Geometric Distribution Imagine we are again working with a coin that has a probability of landing heads up of p where 0 < p < 1 Suppose that instead of ipping the coin a certain number of times like we did to generate the
School: UConn
Course: Introduction To Mathematical Statistics
Bernoulli & Binomial Distributions 1 Revisiting the Coin Flip Consider the experiment where we ip a fair coin Sample space cfw_H, T Can we make a random variable for this experiment? Let X = # of heads we observe when we ip a coin X is 1 if we get a
School: UConn
Course: Introduction To Mathematical Statistics
1 Random Variables Up until now, we have discussed probability in terms of events and samples spaces of probability experiments Anothe way we can discuss probability is in terms of Random Variables Random Variables A Random Variable is a numerical rep
School: UConn
Course: Introduction To Mathematical Statistics
This lecture will focus on Bayes Theorem, but rst we must cover some concepts 1 Preliminaries First, consider the following situation: Let A, B1 , B2 , ., Bn be events of the sample space S such that 1. S = B1 B2 . Bn 2. Bi Bj = i, j, where i = j (i.e. B
School: UConn
Course: Introduction To Mathematical Statistics
1 Calculating Probabilities To determine the probability of an event we must add up the probabilities of the individual sample points which are elements of that event Sometimes guring out the probability of a sample point is not so clear; we need a syst
School: UConn
Course: Introduction To Mathematical Statistics
1 Probability Experiments Experiments A probability experiement is an activity that involves chance that leads to results, that can be repeated Sample Points A Sample point is a possible result of a single trial, or repetition, of an experiment Event
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375 Review 1 Taylor Series 1.1 Denition Let f be a function which is dened and innately dierentiable at all points on some interval (c, d) and let a be some number in this interval. If f is analytic on this interval, then it can be shown that for al
School: UConn
Course: Introduction To Mathematical Statistics
1 Set Theory Denitions A set is simply a collection of objects: The set of counting numbers The set of students in this classroom The set of professors in the statistics department Typical notation: A = cfw_. . . Examples: Z = cfw_. . . 2, 1, 0, 1
School: UConn
Course: Applied Statistics I
Topic 21. Categorical Data Analysis Text Reference: Ravishankers Chapter 5 Reading Assignment: Ravishankers Sections 5.5 1/73 The measurement scale for a categorical variable consists of a set of categories. Categorical data are usually represented in tab
School: UConn
Course: Applied Statistics I
Topic 20. Pearson Chi-Square Goodness of Fit Test 1/8 Pearson Chi-Square Goodness-of-t Test Pearsons chi-squared test is used to assess two types of comparison: tests of independence/association (in the notes on Categorical Data Analysis) tests of goodnes
School: UConn
Course: Applied Statistics I
Topic 19. Nonparametric Inference Procedures Text Reference: Ravishankers Chapter 4 Reading Assignment: Ravishankers Sections 5.1-5.3 1/51 Nonparametric methods are used for inference when there is no specied mathematical model that is assumed to describe
School: UConn
Course: Applied Statistics I
The Bootstrap Procedure B. Efron (1979) To construct condence intervals for parameters of interest, or to perform hypothesis testing, one has to obtain the distributional properties of the estimator (for example, its variance, quantiles, etc.). In many si
School: UConn
Course: Applied Statistics I
Topic 15. Interval Estimation Text Reference: Ravishankers Chapter 4 Reading Assignment: Ravishankers Sections 4.4 1/35 Common Methods for Constructing Interval Estimates: Condence Intervals: Pivotal Method (this note set) Approximate Maximum Likelihood
School: UConn
Course: Applied Statistics I
Topic 17. Hypothesis Tests II Likelihood Ratio Tests, CI and Testing Relationship, p-values, One- and Two- Sample Problems from Normal Populations Text Reference: Ravishankers Chapter 4 Reading Assignment: Ravishankers Sections 4.5-4.7 1/50 Likelihood Rat
School: UConn
Course: Applied Statistics I
Topic 16. Hypothesis Tests I Text Reference: Ravishankers Chapter 4 Reading Assignment: Ravishankers Sections 4.4 1/38 In the classical framework, with point and interval estimation there was no supposition about the actual value of the parameter prior to
School: UConn
Course: Applied Statistics I
Supplement to Topic 15 (Example 15.6 in more detail): Suppose Y1 , Y2 , . . . , Yn is a random sample from a Bernoulli(p) distribution. Derive a 95% approximate condence interval (CI) for p. Solution: We consider here three ways to do this based on statis
School: UConn
Course: Applied Statistics I
Topic 14. Methods of Point Estimation Text Reference: Ravishankers Chapter 4 Reading Assignment: Ravishankers Sections 4.3-4.4 1/37 Point estimates for an unknown parameter () are based on a sample from the population. Common Methods: Method of Moments (M
School: UConn
Course: Applied Statistics I
Topic 13. Limiting Theory and Point Estimation Text Reference: Ravishankers Chapter 4 Reading Assignment: Ravishankers Sections 4.3-4.4 1/1 Weak Law of Large Numbers (WLLN) Under general conditions, the WLLN states that the sample mean approaches the popu
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: Elem Concepts Of Stats
Basic Rules for Finding Probabilities Basic Rules for Suppoﬁe an ’chhw occurs. Wk is +k¢ «prob. M‘— m SMW mag 49 \M méowe ? mm- m:=‘33 .055; Com‘wm‘r: Pkkﬁﬁ) =\—.33=.¢1 .00? PM“ 7 . a .OS‘S'W my; ' mm Probability Examples - Example: If there are 2500 i
School: UConn
Course: Introduction To Statistics I
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: Elem Concepts Of Stats
Answer Key Test #1 Sections 15-24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Version 1 E C B B A B D B B B D C D B E B C B C Version 2 A B D E C B B D C D B B B B C B B E C Version 3 D B A E B C A D D D A A C C B A B D C Version 4 C E E A C A C B A C
School: UConn
Course: Elem Concepts Of Stats
Here is an interesting and fun example you can do ahead of time. It will appear (with one or two small changes) on Test#2 as the final question. The only changes will be the dollar amount ($10 million will be different) and which Team you are asked to cal
School: UConn
Course: Probability And Statistics Problems
Chapter 2: Probability 2.1 A = cfw_FF, B = cfw_MM, C = cfw_MF, FM, MM. Then, AB = 0 , BC = cfw_MM, C B = / cfw_MF, FM, A B =cfw_FF,MM, A C = S, B C = C. 2.2 a. AB b. A B c. A B d. ( A B ) ( A B ) 2.3 2.4 a. b. 8 Chapter 2: Probability 9 Instructors Soluti
School: UConn
Course: Probability And Statistics Problems
Chapter 15: Nonparametric Statistics 15.1 Let Y have a binomial distribution with n = 25 and p = .5. For the twotailed sign test, the test rejects for extreme values (either too large or too small) of the test statistic whose null distribution is the same
School: UConn
Course: Probability And Statistics Problems
Chapter 13: The Analysis of Variance 13.1 2 The summary statistics are: y1 = 1.875, s12 = .6964286, y 2 = 2.625, s 2 = .8392857, and n1 = n2 = 8. The desired test is: H0: 1 = 2 vs. Ha: 1 2, where 1, 2 represent the mean reaction times for Stimulus 1 and 2
School: UConn
Course: Probability And Statistics Problems
Chapter 14: Analysis of Categorical Data 14.1 a. H0: p1 = .41, p2 = .10, p3 = .04, p4 = .45 vs. Ha: not H0. The observed and expected counts are: A B AB O observed 89 18 12 81 expected 200(.41) = 82 200(.10) = 20 200(.04) = 8 200(.45) = 90 The chisquare s
School: UConn
Course: Probability And Statistics Problems
Chapter 11: Linear Models and Estimation by Least Squares Using the hint, y ( x ) = 0 + 1 x = ( y 1 x ) + 1 x = y. 11.2 a. slope = 0, intercept = 1. SSE = 6. b. The line with a negative slope should exhibit a better fit. c. SSE decreases when the slope ch
School: UConn
Course: Probability And Statistics Problems
Chapter 12: Considerations in Designing Experiments ( 1 1 + 2 )n = ( )90 = 33.75 or 34 and n 3 3+ 5 12.1 (See Example 12.1) Let n1 = 12.2 = 90 34 = 56. (See Ex. 12.1). If n1 = 34 and n2 = 56, then 9 25 Y1 Y2 = 34 + 56 = .7111 2 In order to achieve this s
School: UConn
Course: Probability And Statistics Problems
Chapter 4: Continuous Variables and Their Probability Distributions 0.0 0.2 0.4 F(y) 0.6 0.8 1.0 4.1 y <1 0 .4 1 y < 2 a. F ( y ) = P(Y y ) = .7 2 y < 3 .9 3 y < 4 1 y4 0 1 2 b. The graph is above. 4.2 3 4 5 y a. p(1) = .2, p(2) = (1/4)4/5 = .2, p(3) = (1
School: UConn
Course: Probability And Statistics Problems
Chapter 7: Sampling Distributions and the Central Limit Theorem 7.1 a. c. Answers vary. d. The histogram exhibits a mound shape. The sample mean should be close to 3.5 = e. The standard deviation should be close to / 3 = 1.708/ 3 = .9860. f. Very similar
School: UConn
Course: Probability And Statistics Problems
Chapter 5: Multivariate Probability Distributions 5.1 a. The sample space S gives the possible values for Y1 and Y2: S AA AB AC BA BB BC CA CB CC (y1, y2) (2, 0) (1, 1) (1, 0) (1, 1) (0, 2) (1, 0) (1, 0) (0, 1) (0, 0) Since each sample point is equally li
School: UConn
Course: Probability And Statistics Problems
Chapter 6: Functions of Random Variables y 6.1 The distribution function of Y is FY ( y ) = 2(1 t )dt = 2 y y 2 , 0 y 1. 0 a. FU1 (u ) = P(U 1 u ) = P( 2Y 1 u ) = P(Y u +1 2 + + + ) = FY ( u2 1 ) = 2( u21 ) ( u2 1 ) 2 . Thus, fU1 (u ) = FU1 (u ) = 1u , 1
School: UConn
Course: Probability And Statistics Problems
Chapter 9: Properties of Point Estimators and Methods of Estimation 9.1 Refer to Ex. 8.8 where the variances of the four estimators were calculated. Thus, eff( 1 , 5 ) = 1/3 eff( 2 , 5 ) = 2/3 eff( 3 , 5 ) = 3/5. 9.2 a. The three estimators a unbias
School: UConn
Course: Probability And Statistics Problems
Chapter 8: Estimation 8.1 Let B = B() . Then, [ ] [ ] ( ) [ 2 MSE ( ) = E ( ) 2 = E ( E ( ) + B ) 2 = E E () + E ( B 2 ) + 2 B E E () = V ( ) + B 2 . 8.2 a. The estimator is unbiased if E( ) = . Thus, B( ) = 0. b. E( ) = + 5. 8.3 a. Using Definition 8.3,
School: UConn
Course: Probability And Statistics Problems
Chapter 1: What is Statistics? 1.1 a. Population: all generation X age US citizens (specifically, assign a 1 to those who want to start their own business and a 0 to those who do not, so that the population is the set of 1s and 0s). Objective: to estimate
School: UConn
Course: Probability And Statistics Problems
Chapter 10: Hypothesis Testing 10.1 See Definition 10.1. 10.2 Note that Y is binomial with parameters n = 20 and p. a. If the experimenter concludes that less than 80% of insomniacs respond to the drug when actually the drug induces sleep in 80% of insomn
School: UConn
Course: Probability And Statistics Problems
Chapter 3: Discrete Random Variables and Their Probability Distributions 3.1 P(Y = 0) = P(no impurities) = .2, P(Y = 1) = P(exactly one impurity) = .7, P(Y = 2) = .1. 3.2 We know that P(HH) = P(TT) = P(HT) = P(TH) = 0.25. So, P(Y = -1) = .5, P(Y = 1) = .2
School: UConn
Course: Elem Concepts Of Stats
Statistics 1100 Sections 11- 20 April 25,2013 W “4:, Kathleen McLaughlin Name “MN p tr If People SoftNo. H635“! Section Number Instructions for Completing this test: I. On this ﬁrst page, put your name, your PeopleSoft Number and your Section Number. Ifyo
School: UConn
Course: Elem Concepts Of Stats
ir w = w t . c 1 m i = o m M e c \ m n r w cfw_ n i an 1z M m a ' . r m + a u a zm . m in r m s p le w ha i r rw m = n m m c s am m i m e n i s e l c d ho w t n rn r . m \ d m o r l l c h asim a m n 1 w m 4 5 n % o f b to k 5 n s a n d se x w n s h ip s
School: UConn
Course: Elem Concepts Of Stats
Practice Examples for Testitz Do 5’“ scores reailv Predict success? Many might be inclined to agree with some educators who would like to abolish the SAT as a requirement for admission to colleges and universities. I analyzed a set of data from a “niVEFSi
School: UConn
Course: Elem Concepts Of Stats
STAT 1100 D . , W 10 Owe gamete hermit/teas tes'rs 015 a - m L portion or mews amt matched pairs 1' A prOVmCial POIitiCian claims that at least 80% of parents are satisﬁed with the public SChOO1 SYS’tem. A critic wishes to prove that the polit
School: UConn
Course: Design Of Experiments
Statistics 3515Q-01/5515-01 Spring 2015 Exam 2 Solutions _ Name Instructions: Please answer all 4 questions. Present your statements briefly and clearly. Good Luck! (30) 1. In studying the surface finish of steel, five factors were varied each at two leve
School: UConn
Course: Intro To Stats
Final Exam Statistics 1XXX 1. Condence 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% condence interval for the measu
School: UConn
Course: Intro To Stats
Midterm Exam Statistics 1XXX 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 exclusive? S
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: Intro To Stats
QMz#1 Name: 1. The ages of a sample of 25 salespersons are as follows: 21 24 24 26 28 28 30 3O 31 32 34 34 35 35 37 38 4O 41 43 45 45 45 47 53 56 Manually draw a histogram with six classes. Range. 510 é“? 56‘ l”! =- 85 2. Consider the following artificial
School: UConn
Course: Intro To Stats
’1. Consider the following artificial data set: 5, 5, -1o, 6, —3, —3 I What is the sample size? SO’Z Find the sample mean. . _ 3 . 3 5‘, 5) 6‘ I l I Find the median. Find the range. Find the sample variance 32. Find the sample standard deviation 5. Find
School: UConn
Course: Elem Concepts Of Stats
6,453 Perez Votes: No Vindication - Courant.com Page 1 of 2 courant.com/news/opinion/commentary/hc-cun'yl 1 l 1 .artnovl lcol,0,1988296.column Courant.com 6,453 Perez Votes: No Vindication Bill Curry November I 1, 2007 On Tuesday, Hartford, New Haven and
School: UConn
Course: Elem Concepts Of Stats
Project Ideas: Here are some topics from previous semesters. Hopefully this will give you some ideas to think about. You can do a study that has been done in the past so you can actually choose one of these topics if you would like to. Also, I have many p
School: UConn
Course: Applied Time Series
STAT 5825 HW4 Heng Yan Problem 1. [ ( s , t )=E ( x sE [ x s ] )( x t E [ x t ] ) ] E [ ( x su s ) ( x tu t ) ] E ( x s x t ut x sus xt +us ut ) E ( x s x t )E ( ut x s ) E ( u s x t ) + E ( u s ut ) E ( x s x t )ut E ( x s ) u s E ( x t ) +u s ut E
School: UConn
Course: Applied Time Series
STAT 4825 HW3 Jiayue Ding 1. (a) M3: additive Holt-Winters procedure allowing for level, trend and seasonality updating. R output: Call: HoltWinters(x = jj1, seasonal = "additive") Smoothing parameters: alpha: 0.2419952 beta : 0.1429747 gamma: 0.7763187 C
School: UConn
Course: Applied Time Series
Jiayue Ding 0 -4 -2 resid_slr 2 4 STAT 4825 HW2 Problem 1 (a) Plot of residuals vs. time 0 10 20 Time ACF plot of OLS residuals: 30 40 0 .0 -0 .5 ACF 0 .5 1 .0 Series resid_slr 0 5 10 15 Lag PACF plot of OLS residuals: 0 .0 -0 .2 -0 .4 -0 .8 -0 .6 P a rti
School: UConn
Course: Elementary Concepts Of Statistics
STATISTICS 1100Q Fall 2014 Assignment 4 Professor Suman Majumdar Print your name below Solution After you complete the assignment, save it under the filename yourlastname4. General Instructions Answer the questions in the fields provided for and submit th
School: UConn
Course: Elementary Concepts Of Statistics
STATISTICS 1100Q Fall 2014 Assignment 3 Professor Suman Majumdar Print your name below Solution After you complete the assignment, save it under the filename yourlastname4. General Instructions Answer the questions in the fields provided for and submit th
School: UConn
Course: Elementary Concepts Of Statistics
STATISTICS 1100Q Fall 2014 Please read the following directions carefully and save them for future reference. You will need them for the subsequent assignments as well. DIRECTIONS: How to enter a "text" response in an assignment An example of a question t
School: UConn
Course: Elementary Concepts Of Statistics
STATISTICS 1100Q Fall 2014 Assignment 4 Professor Suman Majumdar Print your name below After you complete the assignment, save it under the filename yourlastname4. General Instructions Answer the questions in the fields provided for and submit the resulti
School: UConn
Course: Elementary Concepts Of Statistics
STATISTICS 1100Q Fall 2014 Assignment 3 Professor Suman Majumdar Print your name below After you complete the assignment, save it under the filename yourlastname4. General Instructions Answer the questions in the fields provided for and submit the resulti
School: UConn
Course: Elementary Concepts Of Statistics
STATISTICS 1100Q Fall 2014 Assignment 2 Professor Suman Majumdar Print your name below Solution After you complete the assignment, save it under the filename yourlastname2. General Instructions Answer the questions in the fields provided for and submit th
School: UConn
Course: Elementary Concepts Of Statistics
STATISTICS 1100Q Fall 2014 Assignment 2 Professor Suman Majumdar Print your name below After you complete the assignment, save it under the filename yourlastname2. General Instructions Answer the questions in the fields provided for and submit the resulti
School: UConn
Course: Statistical Computing
STAT 5361 Homework 4 Due at May 4. 1. Suppose X has the folling probability density function 1 f (x) = p x2 e 2 2 (x 2)2 2 1 < x < 1. , Consider using the importance sampling method to estimate E(X). a) Implement the important sampling method, with g(x) b
School: UConn
Course: Design Of Experiments
4.23. An industrial engineer is investigating the effect of four assembly methods (A, B, C, D) on the assembly time for a color television component. Four operators are selected for the study. Furthermore, the engineer knows that each assembly method prod
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 8 The Random Effects Model - Components of Variance In some situations the factor levels (treatments) are not of intrinsic interest in themselves, and the factor (treatment) has a large number of possible levels. If the exper
School: UConn
Course: Design Of Experiments
6.15. A nickel-titanium alloy is used to make components for jet turbine aircraft engines. Cracking is a potentially serious problem in the final part, as it can lead to non-recoverable failure. A test is run at the parts producer to determine the effects
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 5 One way ANOVA Diagnostics - Analysis of the Residuals. Most of the diagnostics are based on the analysis of various types of residuals. We proceed to dene these residuals and discuss their properties and applications. The r
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 10 Randomized Complete Block Design A randomized complete block design (rcbd) is a restricted randomization design in which experimental units are first selected into homogeneous groups, called blocks, and the treatments are
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 13 Statistical Analysis of the Fixed Eects Model The following notation will be used here: Let yi: denote the total of all observations for the i th level of factor A b n P P yi: = yijk ; j=1 k=1 y:j: denote the total of all
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 14 Factorial designs with two factors and one observation per cell When observations are extremely time-consuming or expensive to collect, an experiment may be designed to have one observation per cell. If one can assume that
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Lecture 6 Transformations on the Response Variable when the Assumptions of Homogeneity of Variances is Violated. One common cause for heterogeneity of variances between levels of the treatment factor is a non linear relationship betw
School: UConn
Course: Design Of Experiments
14.16. A structural engineer is studying the strength of aluminum alloy purchased from three vendors. Each vendor submits the alloy in standard-sized bars of 1.0, 1.5, or 2.0 inches. The processing of different sizes of bar stock from a common in'got invo
School: UConn
Course: Design Of Experiments
Chapter 7 Blocking and Confounding in the 2* Factorial Design Solutions 7.1 Consider the experiment described in Problem 6.1. Analyze this experiment assuming that each replicate represents a block of a single production shift. Source of Sum of Degrees of
School: UConn
Course: Design Of Experiments
Chapter 6 k The 2 Factorial Design Solutions 6.1. An engineer is interested in the effects of cutting speed (A), tool geometry (B), and cutting angle on the life (in hours) of a machine tool. Two levels of each factor are chosen, and three replicates of a
School: UConn
Course: Design Of Experiments
5.3. The yield of a chemical process is being studied. The two most important variables are thought to be the pressure and the temperature. Three levels of each factor are selected, and a factorial experiment with two replicates is performed. The yield da
School: UConn
Course: Design Of Experiments
ID " 7.26. Suppose that in Problem 6.7 ABCD was confounded in replicate I and ABC was confounded in replicate II. Perform the statistical analysis of variance. Source of Variation Sum of Squares Degrees of Freedom Mean Square 657.03 13.78 1 1 657.03 13.78
School: UConn
Course: Design Of Experiments
14.3. A manufacturing engineer is studying the dimensional variability of a particular component that is produced on three machines. Each machine has two spindles, and four components are randomly selected from each spindle. The results follow. Analyze th
School: UConn
Course: Applied Statistics I
1.0 0.6 0.8 We consider two possible values for the population mean (null and alternative) a 0.0 0.2 0.4 0 4 2 0 2 4 6 1.0 0.6 0.8 Using the null hypothesis, set up a rejection region (based on the (null) mean and the confidence level) The red segment in
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
Course: Introduction To Mathematical Statistics
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: Applied Statistics I
Applied Statistics I STAT5505-002 Fall 2015: Tu Th 9:30am - 10:45am; AUST 313 Instructor: Oce Hours: Grader: Elizabeth Schifano <elizabeth.schifano@uconn.edu> AUST 317; Tuesday 10:45am-11:45am or by appointment John (Anthony) Labarga <john.labarga@uconn.e
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Spring 2015 SYLLABUS I Instructor: J. Glaz Office Hours: Tu & Th 12:45-1:45 AUST 323B Textbook: Design and Analysis of Experiments. Douglas C. Montgomery, 8th edition, Wiley Recommended Primer for SAS: The Little SAS Book - a Primer,
School: UConn
Course: Intro To Stats
University of Connecticut Fall 2013 STAT 1000, Introduction to Statistics Section 71, 72 Instructor Oce Email Oce Hours Vladimir Pozdnyakov HART 121 Vladimir.Pozdnyakov@uconn.edu Tue/Thu 12:30-1:30pm Lectures Section 71 Section 72 Tue/Thu 9:30-10:45am,
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
Course: Statistics For Enginering
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