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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
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: 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: 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: 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: 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: 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: 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
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: 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
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
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
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
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: 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: 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: 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 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 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
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
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 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 5.2 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 5.3 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 5.8 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 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
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 6.3 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: 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: Applied Statistics I
Topic 12. Multivariate Distribution Models Text Reference: Ravishankers Chapter 3 Reading Assignment: Ravishankers Sections 4.1-4.1 1/37 Multivariate Random Variables We dene a k-dimensional random vector Y = (Y1 , Y2 , . . . , Yk ) as a function from a s
School: UConn
Course: Applied Statistics I
Topic 11. Data Distributions: Part VI Theoretical Quantile-Quantile Plots Text Reference: Ravishankers Chapter 3 Reading Assignment: Ravishankers Sections 3.7-3.9 1/19 What are Theoretical Q-Q Plots? A theoretical Q-Q plot (or probability plot) is an impo
School: UConn
Course: Applied Statistics I
Topic 10. Data Distributions: Part V Continuous Data Models: Normal and Related Distributions Text Reference: Ravishankers Chapter 3 Reading Assignment: Ravishankers Sections 3.7-3.9 1/37 Standard Normal Distribution Density and Distribution Function Let
School: UConn
Course: Applied Statistics I
Topic 9. Data Distributions: Part IV Continuous Data Models Text Reference: Ravishankers Chapter 3 Reading Assignment: Ravishankers Sections 3.7-3.9 1/23 Uniform Distribution Standard Uniform A random variable Y is uniformly distributed over the unit int
School: UConn
Course: Applied Statistics I
Topic 8. Data Distributions: Part III Discrete Data Models - Graphical Assessment and Overdispersion Text Reference: Ravishankers Chapter 3 Reading Assignment: Ravishankers Sections 3.5-3.6 1/26 Graphical Assessment of Discrete Distributions We may use va
School: UConn
Course: Applied Statistics I
Topic 7. Data Distributions: Part II Discrete Data Models Text Reference: Ravishankers Chapter 3 Reading Assignment: Ravishankers Sections 3.5-3.6 September 16-18, 2014 1/1 Bernoulli Distribution Bernoulli Trial (i) Two possible outcomes: success or fail
School: UConn
Course: Applied Statistics I
Topic 6. Data Distributions: Part I Basic concepts of Random Variables and Probability Distributions. Text Reference: Ravishankers Chapter 3 Reading Assignment: Ravishankers Sections 3.3-3.4 1/1 Sample Space and Events Sample Space Sample space, S, is th
School: UConn
Course: Applied Statistics I
Topic 5. Summaries of Data: Part IV Graphical and Numerical Summaries of Multivariate Data Text Reference: Ravishankers Chapter 2 Reading Assignment: Ravishankers Sections 3.1-3.2 1/23 Suppose we aim to compare several sets of data, which are not associat
School: UConn
Course: Applied Statistics I
Topic 4. Summaries of Data: Part III More Graphical and Numerical Summaries of Quantitative Data Text Reference: Ravishankers Chapter 2 Reading Assignment: Ravishankers Sections 2.5 - 2.8 1/49 Box-and-Whisker Plots (Boxplots) The Box-and-Whisker plot (or
School: UConn
Course: Applied Statistics I
Topic 3. Summaries of Data: Part II Graphical and Numerical Summaries of Univariate Quantitative Data Text Reference: Ravishankers Chapter 2 Reading Assignment: Ravishankers Sections 2.5 - 2.8 September 2nd, 2014 1/42 Graphical Approaches for Univariate Q
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Course: Applied Statistics I
Topic 2. Summaries of Data: Part I Types of Data & Graphical Summaries of Qualitative Data Text Reference: Ravishankers Chapter 2 Reading Assignment: Ravishankers Sections 2.5 - 2.8 1/18 Variables and Data A variable is any characteristic that is being co
School: UConn
Course: Applied Statistics I
Applied Statistics I, Fall 2014 Class: AUST 344; Tu Th 3:30pm - 4:45pm Instructor: Oce Hours: Haim Bar <haim.bar@uconn.edu> AUST 315; Tu 12:00pm-1:30pm or by appointment TA: Brian Bader Syllabus: Available on course website: http:/learn.uconn.edu <brian.b
School: UConn
Course: Intro To Stats
1. Data, Graphical Descriptive Techniques Introduction Descriptive statistics involves the arrangement, summary, and presentation of data to enable meaningful interpretation and to support decision making. Descriptive statistics methods make use of graphi
School: UConn
Course: Intro To Stats
6. Discrete Random Variables and Probability Distributions Random variables and probability distributions A random variable is a function that assigns a numerical value to each outcome in a sample space. A random variable reflects the aspect of a random e
School: UConn
Course: Intro To Stats
7. Continuous Probability Distributions Cumulative distribution function We define a cumulative distribution function (cdf) () of a random variable as = ( ) Properties of the cdf 1. = 2. + = 3. () Probability density function If the cdf is differenti
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Course: Intro To Stats
5. Conditional Probability and Independence Conditional Probability The probability of an event when partial knowledge about the outcome of an experiment is known is called conditional probability. The conditional probability that event occurs given that
School: UConn
Course: Intro To Stats
2. Numerical Descriptive Measures of Central Tendency and Variability Measures of central tendency Usually, we focus our attention on two aspects of measures of central location: Measure of the central data point (mean, median) Measure of variability of t
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Course: Intro To Stats
3. Measures of Relative Standing, Box Plots and Linear Regression Percentile The th percentile of a set of measurements is a value for which: at least % of the measurements are less or equal than that value at least ( )% of all the measurements are greate
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Course: Intro To Stats
4. Probability Random experiment a random experiment is a process or course of action, whose outcome is uncertain performing the same random experiment repeatedly may result in different outcomes, therefore, the best we can do is to talk about the probabi
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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
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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: 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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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: 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: 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: 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 Handout 11 Latin Square Designs Example: Consider an experiment about the effects of five types of background music (A,B,C,D,E) on the productivity of bank tellers. Day of the week (Monday though Friday) and week of the experimental
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: Design Of Experiments
Stat 3515Q/5515 Handout 4 Model Adequacy - Checking the Underlying Assumptions The model for the one way anova is given by: Yij = + i + ij where i=1,.,a and j=1,.,ni. Fixed Effects Random Effects 1. i is a fixed parameter. 1. i are iid N(0, 2) 2. ij are i
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 2 Tests of Means After Experimentation If the null hypothesis H0 : 1 . . . . a is rejected then one is interested to find out which of the means are significantly different from each other. A multiple comparison procedure can
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 3 Tests on Means Set Prior to Experimentation in One Way ANOVA a. Orthogonal Contrasts A contrast in treatment means is a linear combinations of the form a = 3cii, i=1 where a 3ci = 0. i=1 We say that a linear combination of
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
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: 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
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