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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
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 9 Point Estimates for Variance Components in One Way Random ANOVA We use the methods of moments approach to derive point estimates for the variance components: 1. To estimate 2 " we use the fact that 2 " E(M SE ) = and theref
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 16 Statistical Analysis of a Four Factorial Fixed Eects Model The model for a four way ANOVA model is Yijklm = + Ai + Bj + Ck + Dl +ABij + ACik + ADil + BCjk + BDjl + CDkl +ABCijk + ABDijl + ACDikl + BCDjkl +ABCDijkl + "m(ikk
School: UConn
Course: Design Of Experiments
Handout 17 Blocking in a Factorial Design In some experiments in which factorial designs are used, because of experimental conditions the experiment cannot be carried out in one time period or one location. In such situations the experiment is done in blo
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 18 2k Factorial Design of Experiments Example: A mechanical engineer is studying the thrust of force developed by a drill press. The following two factors are considered to be the most important ones: Feed Rate of the materia
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375 Review 1 Dierentiation 1.1 Denitions Let f be a function which is dened on some interval (c, d) and let a be some number in this interval. The derivative of the function f at a is the value of the limit f (a) = lim xa f (x) f (a) . xa f is said
School: UConn
Course: Statistical Computing
Globalization Globalizationisanincreaseinconnectedness ofcountriestooneanothereconomically, politicallyandsocially Itisamovementofpeopleacrossborders Globalizationpromotesinterconnectednessand interdependenceofcountries Economicdimension: Integrationofec
School: UConn
Course: Statistical Computing
GenderedIdentitiesinNations andStates 1 WhatareessentialcategoriesofIdentity? Whyarethesecategoriesimportant? Whyistheanalysisofgenderimportant? GenderedIdentitiesinNationsand States 2 Essentialcategoriesofidentitiesinthe nationstate Name Address Gender B
School: UConn
Course: Statistical Computing
Reproductive Rights Warm up: What are the things that are included in reproductive health and rights? 1 Reproductive Rights Reproductive health and rights may include: right to birth control right to legal or safe abortion right to access quality repro
School: UConn
Course: Statistical Computing
SocialandHistorical Constructionof Gender Whatmakesmendifferentfrom women? Howdoeswesternsciencedefine thecategoriesofmaleand female? AND Howisgenderinterconnected withothercategoriessuchas race,class,abilityetc.? 1 Biologyvs.culture Feministsareintereste
School: UConn
Course: Statistical Computing
Final Exam Review Sheet Fall 2014 Chapter 1 Know the six characteristics of intimate relationships Understand the nature of sex/gender differences in relationships Understand the importance of intimate relationships for people (why do we need them, what
School: UConn
Course: 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: Design Of Experiments
Statistics;-: _ A Nested Factorial Design Example: An investigator who wished to increase the number of rounds per minute that could be fired from a naval gun devised a new loading method (method I) with the intent of improving performance in this task ov
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 9 Point Estimates for Variance Components in One Way Random ANOVA We use the methods of moments approach to derive point estimates for the variance components: 1. To estimate 2 " we use the fact that 2 " E(M SE ) = and theref
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 16 Statistical Analysis of a Four Factorial Fixed Eects Model The model for a four way ANOVA model is Yijklm = + Ai + Bj + Ck + Dl +ABij + ACik + ADil + BCjk + BDjl + CDkl +ABCijk + ABDijl + ACDikl + BCDjkl +ABCDijkl + "m(ikk
School: UConn
Course: Design Of Experiments
Handout 17 Blocking in a Factorial Design In some experiments in which factorial designs are used, because of experimental conditions the experiment cannot be carried out in one time period or one location. In such situations the experiment is done in blo
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 18 2k Factorial Design of Experiments Example: A mechanical engineer is studying the thrust of force developed by a drill press. The following two factors are considered to be the most important ones: Feed Rate of the materia
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 20 Confounding in a 2k Factorial Experiments In many experiments it is impossible to perform a complete replicate of a factorial design. In which case one performs the experiment in several blocks, where the block size is sma
School: UConn
Course: Introduction To Mathematical Statistics
l. 'Iiue or False For each statement below, use "a" if you think it is true; " x" if you think it is false. You do not need to justify your answer nor need to show werk. For the following True of False questions, dene X, Y to be continuous random variable
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375: Practice Final Exam 1. Consider the discrete random variable Y with probability function p(y) = 4 , for y = 1, 2, 3, 5y (a) Find P (Y 3 | Y 1). (b) Find P (Y 3 | Y 1). (c) Find the moment generating function of Y . (d) Find E(Y ). (e) Find V (
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375 Practice Midterm Exam I Solution Fall 2013 September 28, 2013 Student Name (Print): _ PeopleSoft ID: _ 1. True or False For each statement below, use " if you think it is true; " if you think it is false. You do not need to justify your answer n
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375: Practice Final Exam 1. Consider the discrete random variable Y with probability function p(y) = (a) (b) (c) (d) (e) 4 , for y = 1, 2, 3, 5y Find P (Y 3 | Y 1). Find P (Y 3 | Y 1). Find the moment generating function of Y . Find E(Y ). Find V (
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375 Practice Midterm Exam I Fall 2013 September 28, 2013 NAME: _ STUDENT ID: _ Please read all of the following information before starting the exam: You are allowed to use ONE page of formula sheet, double sided. Calculator is not needed nor allo
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375 Practice Midterm Exam I Solution Student Name (Print): _ PeopleSoft ID: _ 1. True or False For each statement below, use " if you think it is true; " if you think it is false. You do not need to justify your answer nor need to show work. (a) (b)
School: UConn
Course: Introduction To Mathematical Statistics
3.107 The random variable Y follows a hypergeometric distribution with N = 6, n = 2, and r = 4. 3.127 Let Y = # of typing errors per page. Then, Y is Poisson with = 4 and P(Y 4) = .6288.
School: UConn
Course: Introduction To Mathematical Statistics
3.1. P(Y = 0) = P(no impurities) = 0.2 P A B 1 P Y 0 0.8 , so P Y 2 P A B P A P B P A B 0.1 P(Y = 1) = P(exactly one impurity) = P A P B 2P A B 0.7 3.9 The random variable Y takes on vales 0, 1, 2, and 3. a. Let E denote an error on a single entry and let
School: UConn
Course: Introduction To Mathematical Statistics
Solutions for HW3 2.85(10) 2.89(10) 2.135(20) 2.91(10) 2.95(12) 2.110(10) 2.129(10) 2.131(8) 2.133(10) 2.89 a. 0, since they could be disjoint. b. the smaller of P(A) and P(B). 2.91 If A and B are M.E., P( A B) = P(A) + P(B). This value is greater than 1
School: UConn
Course: Introduction To Mathematical Statistics
2.37 a. There are 6! = 720 possible itineraries. b. In the 720 orderings, exactly 360 have Denver before San Francisco and 360 have San Francisco before Denver. So, the probability is .5. 2.38 By the mn rule, 4(3)(4)(5) = 240. 2.42 There are three differe
School: UConn
Course: Introduction To Mathematical Statistics
Solution Find the derivative of the function: 1. f (x) = (2x 3) 3 (x2 + x + 1)5 2 2 1 2 f (x) = (2x 3) 3 2 (x2 + x + 1)5 + (2x 3) 3 5(x2 + x + 1)4 (2x + 1) 3 2 4(x2 + x + 1)5 = + 5(2x 3) 3 (x2 + x + 1)4 (2x + 1) 1 3(2x 3) 3 (x2 + x + 1)4 = 4(x2 + x + 1) +
School: UConn
Course: Intro Mathematical Statistics
Hw1 Stat 3445 s2013 due at the beginning of class Thursday, Jan. 30 A. Suppose that Z has a standard normal distribution and Y = a + bZ, where b > 0. Find the density f (y) of Y and identify the distribution of Y by name. B. Suppose that X, Y are random v
School: UConn
Course: Statistical Computing
Short Response Answers: 1. I want to be orientation leader because I want to represent how much this campus means to me and hopefully translate that into the incoming freshmen. I love meeting, working, and building relationships with people. Its one of th
School: UConn
Course: Statistical Computing
Local University GPA and SAT scores 4.00 3.75 GPA 3.50 3.25 3.00 2.75 2.50 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 S R-Sq R-Sq(adj) 0.397020 2.5% 0.0% Sat Score YimingZhangSe ction015D Regression Line for GPA AND SAT scores GPA = 2.570 + 0.00028
School: UConn
Course: Statistical Computing
Yiming Zhang Husky CT Assignment: The article by Shelia Grant Bunch discusses takes a look at the different experiences of 23 grandmothers who are providing full time child care to their grandchildren due to military deployment of their own child. The aut
School: UConn
Course: Statistical Computing
Yiming Zhang Group Presentation Response: Domestic Violence in the NFL Our group agreed on this topic because domestic violence resulted in some of the most viral and controversial gender inequality incidents that has occurred this year. The topic has aff
School: UConn
Final Exam Information Instructor: Steven Chiou General Information The nal examination for this course will be given at 10:30 a.m. on Tuesday, December 11 in our regular class room, CB 206. The exam will be roughly the same length of the second midterm.
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375Q: Introduction to Mathematical Statistics Kun Chen Assistant Professor Department of Statistics University of Connecticut Storrs, CT November 21, 2013 Outline I 1 Chapter 6 Function of Random Variables 6.3 The Method of Distribution Functions 6.
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
Statistics 3115Q-01/5315-01 Analysis of Experiments Fall 2014 Textbooks: 1. Applied Regression Analysis and Other Multivariable Methods, 4th Edition, 2008 by Kleinbaum, D. G., Kupper, L.L., Nizam, A. and Muller, K. E., Duxbury Press & Thomson Brooks/Cole.
School: UConn
Course: Mathematical Statistics II
STAT 5685 Spring 2013 STAT 5685: Mathematical Statistics II, Spring, 2013 January 22, 2013 Instructor: Jun Yan Department of Statistics CLAS 328 860/486-3416 jun.yan@uconn.edu Late homework will not be accepted for any reason. You are encouraged to discus
School: UConn
SYLLABUS Dishwasher Safe STAT 3345Q-01: PROBABILITY MODELS FOR ENGINEERS Class Hour and Class Room Class Hour: Tuesdays and Thursdays 11:00am - 12:30pm every week. (Saturday and Sunday: 4:30am-6:00am ) Class Room: CLAS 313. Website for Stat STAT 3345Q-01
School: UConn
SYLLABUS STAT 1100QC : ELEMENTARY CONCEPT OF STATISTICS Class Hour and Class Room Class Hour: Monday, Tuesday, Wednesday, and Thursday - 11:00am - 1:00pm every week from June 1st to July 10th. Lab Hour (with D. Bhattacharjee): Friday - 11:00am -
School: UConn
University of Connecticut STAT 1000, Introduction to Statistics Instructor Oce Phone Email Lectures Class Web Page May 2009 May Session Vladimir Pozdnyakov CLAS 336 (860) 486-6979 Vladimir.Pozdnyakov@uconn.edu Mon/Tue/Wed/Thu/Fri 9:00am-1pm, CLAS 1
School: UConn
Course: Intro To Stat 1
Parag Santhosh Peoplesoft: 1576431 Section 25D Greg Matthews I prefer using a bar chart because it is easy to see the differences in categorical data, and it is easier to compare them to the other categories. When I used a pie chart, it was annoying
School: UConn
Course: Intro To Stat 1
Parag Santhosh Peoplesoft: 1576431 Section 25D Greg Matthews For Class 1, the data resembled a mound-shaped, atleast it was closer to the shape than class 2. The most prevalent score was 65, with over 20 students attaining that score. The mean of the
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 9 Point Estimates for Variance Components in One Way Random ANOVA We use the methods of moments approach to derive point estimates for the variance components: 1. To estimate 2 " we use the fact that 2 " E(M SE ) = and theref
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 16 Statistical Analysis of a Four Factorial Fixed Eects Model The model for a four way ANOVA model is Yijklm = + Ai + Bj + Ck + Dl +ABij + ACik + ADil + BCjk + BDjl + CDkl +ABCijk + ABDijl + ACDikl + BCDjkl +ABCDijkl + "m(ikk
School: UConn
Course: Design Of Experiments
Handout 17 Blocking in a Factorial Design In some experiments in which factorial designs are used, because of experimental conditions the experiment cannot be carried out in one time period or one location. In such situations the experiment is done in blo
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 18 2k Factorial Design of Experiments Example: A mechanical engineer is studying the thrust of force developed by a drill press. The following two factors are considered to be the most important ones: Feed Rate of the materia
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 20 Confounding in a 2k Factorial Experiments In many experiments it is impossible to perform a complete replicate of a factorial design. In which case one performs the experiment in several blocks, where the block size is sma
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 21 Fractional Replication for a 2k Factorial Design. Quite often a single replication of a factorial experiment is beyond the resources of an investigator, or there is no real need to execute the entire replicate of the exper
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 19 A Single Replicate in a 2k Design In some experiments only one replicate of all the 2k treatments combinations are observed. In that case one cannot analyze all the effects in the model. To address this issue one can emplo
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Model: Handout 22 Yijk = + Ai + Bj + (AB)ij + k(ij), i =1,.,a, j=1,.,b, k=1,.,n. We have discussed this model for the case when both A and B are fixed factors. If both factors are random we will say that it is a random model and if o
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 23 Analysis of a Random Model Example: The factors that influence the braking strength of a synthetic fiber are being studied. Four production machines and three operators were chosen at random and a factorial experiment with
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 25 Nested Designs In certain multifactor experiments the levels of one factor (B) are similar but not identical for different levels of another factor (A). A design of that nature is called a nested design or a hierarchical d
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515Q Handout 26 A General Multi Stage Balanced Nested Design The two-stage nested design can be easily extended to a general m-stage nested design. The following example describes an experiment to be modeled by a three-stage nested desig
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 24 Miltifactorial Designs - Mixed Models Example: An experiment on diesel engines was conducted to determine the causes of variability in brake specific fuel consumption. Four different engines (E) of the same type and four d
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 28 Multifactor Experiments with Randomization Restrictions The Split-Plot Design Example: A paper manufacturer is interested to study the effect of three different methods of preparing the pulp and four different cooking temp
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series Forecasting ARMA Processes Copyright: Nalini Ravishanker, Univ. of Connecticut Forecasting ARMA Processes Consider a stationary and invertible ARMA(p,q) process (B)Xt = (B)wt with MA representation given by Xt = j=0 j w
School: UConn
Course: Design Of Experiments
Statistics;-: _ A Nested Factorial Design Example: An investigator who wished to increase the number of rounds per minute that could be fired from a naval gun devised a new loading method (method I) with the intent of improving performance in this task ov
School: UConn
Foundations of Probability: Part I Cyr Emile MLAN, Ph.D. mlan@stat.uconn.edu Foundations of Probability: Part I p. 1/36 Introduction Text Reference: Introduction to Probability and Its Application, Chapter 2. Reading Assignment: Sections 2.1-2.3, Januar
School: UConn
Foundations of Probability: Part II Cyr Emile MLAN, Ph.D. mlan@stat.uconn.edu Foundations of Probability: Part II p. 1/25 Counting Rules Text Reference: Introduction to Probability and Its Application, Chapter 2. Reading Assignment: Sections 2.4-2.5, Ja
School: UConn
Course: Introduction To Statistics
9/2/08 Statistics Histograms No Spaces in-between bars (unlike bar graphs) No Overlap (class: 30-39, 40-49, 50-59, etc.) o Class Frequency Relative Frequency o 30-39 3 .12 or 12% o 40-49 4 .16 or 16% o 50-59 7 .28 or 28% o 60-69 6 .24 or 24% o 70-79 2 .
School: UConn
Course: Introduction To Statistics
8/28/08 Statistics Syllabus on HuskyCT Example: choose 4 out of 30 containers to test purity and potency Random sample of the population o How to select samples at random Label all containers (01-30) Pick a line from random number table Go by two digi
School: UConn
Course: Introduction To Statistics
8/26/08 Statistics Office Hours: 6:15-7:15 on Tuesdays and Thursdays Email: William.Congero@uconn.edu All assignments and notes on HuskyCT Text: At First Course in Business Statistics o McClave, Benson & Sincich 10th edition MINITAB Lab Manual o An Intr
School: UConn
Course: Introduction To Statistics
9/19 Statistics Experiment act that produces only one outcome Sample point basic outcome Sample space collection of all possible outcomes Probability assign a number between 0p1 to each basic outcome Event collection of sample points A=Event=P(A)=Pi A=cfw
School: UConn
Course: Introduction To Statistics
9/5/08 Statistics STAT 1000 QC Discussion Section 30 Xun (Tony) Jiang tonyjiangxun@gmail.com Office hours: Tuesday 1:00-3:00PM , Thursday 2:00-3:00 PM Bring Minitab to discussion Chapter 1 o Hand in next Friday o Set up Minitab on laptop If I dont have
School: UConn
Functions of Random Variables Cyr Emile MLAN, Ph.D. mlan@stat.uconn.edu Functions of Random Variables p. 1/31 Introduction Text Reference: Introduction to Probability and Its Applications, Chapter 7. Reading Assignment: Sections 7.1-7.6, April 27 Often
School: UConn
Multivariate Probability Distributions: Part III Cyr Emile MLAN, Ph.D. mlan@stat.uconn.edu Multivariate Probability Distributions: Part III p. 1/1 Introduction Text Reference: Introduction to Probability and Its Applications, Chapter 6. Reading Assignme
School: UConn
Multivariate Probability Distributions: Part II Cyr Emile MLAN, Ph.D. mlan@stat.uconn.edu Multivariate Probability Distributions: Part II p. 1/2 Introduction Text Reference: Introduction to Probability and Its Applications, Chapter 6. Reading Assignment
School: UConn
Multivariate Probability Distributions: Part I Cyr Emile MLAN, Ph.D. mlan@stat.uconn.edu Multivariate Probability Distributions: Part I p. 1/34 Introduction Text Reference: Introduction to Probability and Its Applications, Chapter 6. Reading Assignment:
School: UConn
Continuous Random Variable and Their Probability Distributions: Part III Cyr Emile MLAN, Ph.D. mlan@stat.uconn.edu Continuous Random Variables: Part III p. 1/21 Introduction Text Reference: Introduction to Probability and Its Application, Chapter 5. Rea
School: UConn
Continuous Random Variable and Their Probability Distributions: Part II Cyr Emile MLAN, Ph.D. mlan@stat.uconn.edu Continuous Data Models: Part II p. 1/30 Some Common Continuous Random Variables Text Reference: Introduction to Probability and Its Applica
School: UConn
Continuous Random Variable and Their Probability Distributions: Part I Cyr Emile MLAN, Ph.D. mlan@stat.uconn.edu Continuous Random Variables: Part I p. 1/34 Introduction Text Reference: Introduction to Probability and Its Application, Chapter 5. Reading
School: UConn
Discrete Random Variables and Their Probability Distribution: Part II Cyr Emile MLAN, Ph.D. mlan@stat.uconn.edu Discrete Random Variable: Part II p. 1/28 Introduction Text Reference: Introduction to Probability and Its Application, Chapter 4. Reading As
School: UConn
Discrete Random Variables and Their Probability Distribution: Part I Cyr Emile MLAN, Ph.D. mlan@stat.uconn.edu Discrete Random Variable: Part I p. 1/40 Introduction Text Reference: Introduction to Probability and Its Application, Chapter 4. Reading Assi
School: UConn
Conditional Probability and Independence Cyr Emile MLAN, Ph.D. mlan@stat.uconn.edu Conditional Probability and Independence p. 1/26 Introduction Text Reference: Introduction to Probability and Its Application, Chapter 2. Reading Assignment: Sections 3.1
School: UConn
Course: Introduction To Statistics
9/9/08 Statistics Symmetrical Distribution Multiple modes Skewed to the left mode is the largest value Skewed to the right mean s the largest value Measures of spread and variability Standard deviation o Interpreting standard deviation Chebyshevs The
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series Fitting ARIMA Models Copyright: Nalini Ravishanker, Univ. of Connecticut Fitting ARIMA Models There are three broad phases in tting ARIMA models: Model Identication Model Estimation Model Diagnostics - Model Adequacy an
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series Review of Regression Methods Copyright: Nalini Ravishanker, Univ. of Connecticut Review of Regression Methods A regression model is a mechanism that enables us to describe the eect of one or more explanatory variables (
School: UConn
Course: Variable Topics
Lecture 3: Confidence Intervals Confidence Interval: gives a range of values that have a set probability of containing the population parameter; the P of the CI containing that parameter is the confidence level o i.e. a 95% CI has P=.95 of containing the
School: UConn
Course: Variable Topics
Stats: Lecture 1 Basic Concepts Variable: a condition or characteristic that can have different values o Examples of variables: depression, conscientiousness o Independent variable (IV): variable that is manipulated and has an effect on a dependent variab
School: UConn
Course: Variable Topics
Stats: Lecture 2 Basic Concepts Degrees of freedom (df): number of data points free to vary o How many observations you need to know before having all information about a sample o i.e. We have four data points. How many data points do we need to calcula
School: UConn
Introduction to Biostatistics Stat 4625/5625 SAS Ofer Harel Department of Statistics University of Connecticut SAS p. 1/ SAS 1. SAS stands for "Statistical Analysis System" 2. The software was conceived 1966. 3. It started as code for ANOVA and Regression
School: UConn
Introduction to Biostatistics Stat 4625/5625 Chapter 5 Ofer Harel Department of Statistics University of Connecticut Chapter 5 p. 1/1 Introduction Our goal is to examine a scientic hypothesis about the population. We use only one study (experiment) on a s
School: UConn
Introduction to Biostatistics Stat 4625/5625 Chapter 8 Ofer Harel Department of Statistics University of Connecticut Chapter 8 p. 1/2 Introduction There are different type of associations that one might be interested in In this section we deal with test f
School: UConn
Introduction to Biostatistics Stat 4625/5625 Chapter 6 Ofer Harel Department of Statistics University of Connecticut Chapter 6 p. 1/4 Introduction A frequent problem in medical research is the comparison of two groups The 2 groups can formed from an exper
School: UConn
Introduction to Biostatistics Stat 4625/5625 Chapter 7 Ofer Harel Department of Statistics University of Connecticut Chapter 7 p. 1/4 Introduction In the previous chapter we talked about continuous variables or at least ordinal variables What can we do wh
School: UConn
Introduction to Biostatistics Stat 4625/5625 Introduction Ofer Harel Department of Statistics University of Connecticut Intro p. 1/15 Introduction to Biostatistics Monday 11:15-1:15, Wednesday 11:15-12:05, CLAS 344 On occasions we will meet in the teachin
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: Introduction To Mathematical Statistics
3.107 The random variable Y follows a hypergeometric distribution with N = 6, n = 2, and r = 4. 3.127 Let Y = # of typing errors per page. Then, Y is Poisson with = 4 and P(Y 4) = .6288.
School: UConn
Course: Introduction To Mathematical Statistics
3.1. P(Y = 0) = P(no impurities) = 0.2 P A B 1 P Y 0 0.8 , so P Y 2 P A B P A P B P A B 0.1 P(Y = 1) = P(exactly one impurity) = P A P B 2P A B 0.7 3.9 The random variable Y takes on vales 0, 1, 2, and 3. a. Let E denote an error on a single entry and let
School: UConn
Course: Introduction To Mathematical Statistics
Solutions for HW3 2.85(10) 2.89(10) 2.135(20) 2.91(10) 2.95(12) 2.110(10) 2.129(10) 2.131(8) 2.133(10) 2.89 a. 0, since they could be disjoint. b. the smaller of P(A) and P(B). 2.91 If A and B are M.E., P( A B) = P(A) + P(B). This value is greater than 1
School: UConn
Course: Introduction To Mathematical Statistics
2.37 a. There are 6! = 720 possible itineraries. b. In the 720 orderings, exactly 360 have Denver before San Francisco and 360 have San Francisco before Denver. So, the probability is .5. 2.38 By the mn rule, 4(3)(4)(5) = 240. 2.42 There are three differe
School: UConn
Course: Variable Topics
Lecture 5: t-tests Recall z-tests (review) o When we compared a single person to a population mean, we used: Z = score sample mean/sample SD o When we compared a sample mean to a population mean, we used: Z = (Xbar pop mean) / pop SD (or SE) o We used t
School: UConn
Course: Variable Topics
Lecture 8: Power 4 basic possible scenarios at the end of a study: ActualStateofAf TrueNull Decision Failto correct False typeII RejectNull RejectNull typeIerror o If the null hypothesis is true and we reject the null, this is a type 1 error .05 = probab
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series Time Series Regression Copyright: Nalini Ravishanker, Univ. of Connecticut Deterministic Time Series Regression Methods We will study the following: Structural Decomposition Trend Fitting by Polynomial Trend Models Tren
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series Introduction to Time Series Copyright: Nalini Ravishanker, Univ. of Connecticut Introduction: Characteristics of Time Series Text: Time Series Analysis and its Applications. R.H. Shumway and D.S. Stoer. Third edition, 2
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series Time Series Smoothing Methods Copyright: Nalini Ravishanker, Univ. of Connecticut Time Series Smoothing Methods In tting polynomial trend models, we assume that the parameter values are constant, and use of least square
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series Regression with Autocorrelated Errors Copyright: Nalini Ravishanker, Univ. of Connecticut Regression with Autocorrelated Errors Consider the SLR model in (2.2) or the MLR model in (2.3). When data is observed over time,
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series Conditional Heteroscedasticity - ARCH/GARCH Models Copyright: Nalini Ravishanker, Univ. of Connecticut Conditional Heteroscedasticity - ARCH/GARCH Models In some applications (nancial), we are interested not only in the
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series Stochastic Properties of Time Series Copyright: Nalini Ravishanker, Univ. of Connecticut Stationarity, Strict and Weak Stationarity Stationarity. A time series cfw_Xt , t = 0 1, 2, is stationary if its statistical prop
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375 Review 1 Dierentiation 1.1 Denitions Let f be a function which is dened on some interval (c, d) and let a be some number in this interval. The derivative of the function f at a is the value of the limit f (a) = lim xa f (x) f (a) . xa f is said
School: UConn
Course: Statistical Computing
Globalization Globalizationisanincreaseinconnectedness ofcountriestooneanothereconomically, politicallyandsocially Itisamovementofpeopleacrossborders Globalizationpromotesinterconnectednessand interdependenceofcountries Economicdimension: Integrationofec
School: UConn
Course: Statistical Computing
GenderedIdentitiesinNations andStates 1 WhatareessentialcategoriesofIdentity? Whyarethesecategoriesimportant? Whyistheanalysisofgenderimportant? GenderedIdentitiesinNationsand States 2 Essentialcategoriesofidentitiesinthe nationstate Name Address Gender B
School: UConn
Course: Statistical Computing
Reproductive Rights Warm up: What are the things that are included in reproductive health and rights? 1 Reproductive Rights Reproductive health and rights may include: right to birth control right to legal or safe abortion right to access quality repro
School: UConn
Course: Statistical Computing
SocialandHistorical Constructionof Gender Whatmakesmendifferentfrom women? Howdoeswesternsciencedefine thecategoriesofmaleand female? AND Howisgenderinterconnected withothercategoriessuchas race,class,abilityetc.? 1 Biologyvs.culture Feministsareintereste
School: UConn
Course: Statistical Computing
Final Exam Review Sheet Fall 2014 Chapter 1 Know the six characteristics of intimate relationships Understand the nature of sex/gender differences in relationships Understand the importance of intimate relationships for people (why do we need them, what
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
Notes 5: Multiple Regression: Hypothesis Testing 1 Testing Hypotheses in Multiple Regression Basic assumption The following linear regression model holds: Y = 0 + 1 X1 + + k Xk + E, E N (0, ). Overall test: is there any i = 0, i = 1, . . . , k? Test of
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
Notes 2: Simple Regression Example 1 How to predict sons height, given fathers height. Galtons approach Collect data: n pairs (Xi , Yi ), each for one father-son pair, Xi = fathers height, Yi = sons height. Find the curve (straight line, parabola, etc.)
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
Notes 4: Multiple Regression 1 General Framework for Multiple Regression A multiple linear regression model has the form Y = 0 + 1 X 1 + . . . + k X k + E X1 , . . . , Xk : independent or predictor variables; Y : dependent or response variable; E: erro
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
Notes 3: Corr & ANOVA for SLR 1 Sample Correlation Coecient Recall, the population, or statistical , correlation coecient for X and Y is = Cov(X, Y ) X Y The sample correlation coecient for X and Y is r= SXY SX 1 . = SX SY SY r is dimensionless and 1 r
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
Notes 1: Basic Statistics 1 Descriptive Statistics Let X1 , . . . , Xn be n independent measurements on random variable X Sample mean to measure central tendency 1 X= n n Xi . i=1 Sample variance to measure variability 1 S = n1 n 2 i=1 (Xi X)2 Sample st
School: UConn
Course: Intro To Statistics II
Regression Equations Standardized regression equation with one predictor: zy = r zx Centered scores: cy = r(Sy/Sx)cx / cx = r(Sx/Sy)cy Raw Scores: y = r(Sy/Sx)x + My - bMx How much variance is not accounted for? : Correlation of non-determination = 1 r2 C
School: UConn
Course: Intro To Statistics II
Chapter 10: Linear Regression Linear regression o Regression line = where the line with the springs settles o Slope of the line tells you when you see changes across one variable (e.g., shoe size) see change in other variable (e.g., height) *Can quantify
School: UConn
Course: Intro To Statistics II
Chapter 9/Lecture 1: Linear Correlation Linear Correlation o Perfect Correlation what would it look like? E.g., An exact match on one measure with another E.g., Final grades that are all exactly 10 points lower than the midterm E.g., Shoe size and heig
School: UConn
Course: Intro To Statistics II
Chapter 17 Multiple Regression: An extension of linear regression in which there are multiple predictor variables predicting one criterion variable Two Uncorrelated Predictor Variables (two variables, each correlated with the criterion variable, but not w
School: UConn
Course: Intro To Statistics II
Lecture 3 Null Logic- The null hypothesis test is: o Weak the null hypothesis is always wrong o Cowardly it does not test what we think, only what we do not thing o Limited does not provide a model for everyday or applied inference Because we are removin
School: UConn
Chapter 2 - 1 STAT 2215 CHAPTER 2 T-TOOLS Chapter 2 - 2 Inference Using t-distributions This chapter focuses on t-tools These tools are often used in statistics, to make inferences about your data An inference is the act of using a sample data set to m
School: UConn
Chapter 7 - 1 STAT 2215 CHAPTER 7 SIMPLE LINEAR REGRESSION Chapter 7 - 2 Recap from Previous Chapters Chapters 1-5 covered methods for investigating the relationship between a categorical (group) variable and a numeric variable One-Sample t-tools Two-s
School: UConn
STAT 2215 CHAPTER 3 A CLOSER LOOK AT ASSUMPTIONS Chapter 3 - 2 Recap We previously covered some t-tools for looking at the mean of a single group, or comparing two groups. One-Sample t-test, One-Sample t-interval Matched Pairs t-test Two-Sample t-test
School: UConn
Chapter 4 - 1 STAT 2215 CHAPTER 4 ALTERNATIVES TO THE T-TOOLS Chapter 4 - 2 Chapter 4 Goals Introduce some alternatives to the t-Tools Four distribution-free methods Permutation test and Rank-sum test Two independent samples Sign Test and Signed-Rank
School: UConn
Chapter 1 - 1 STAT 2215 CHAPTER 1 DRAWING STATISTICAL CONCLUSIONS Chapter 1 - 2 The purpose of statistics is to examine data to answer questions of interest. Well focus on ways to make statistically valid conclusions from data sets. Statistical inferenc
School: UConn
STAT 2215 CHAPTER 5 COMPARISONS AMONG SEVERAL SAMPLES Chapter 5 - 2 OVERVIEW The previous tools weve seen are useful for making inference about a single mean, or comparing the means of two different groups. But what if we have more than two groups? Such a
School: UConn
STAT 2215 CHAPTER 6 LINEAR COMBINATIONS AND MULTIPLE COMPARISONS OF MEANS Chapter 6 - OVERVIEW In the last chapter we discussed how to test the equality of several group means. While these tests are quite useful, they do not inform us which groups have si
School: UConn
Outline 6.3 The Method of Distribution Functions 6.4 The Method of Transformations 6.5 The Method of Moment-Generatin STAT 3375Q 6.3 6.5 Steven Chiou University of Connecticut Steven Chiou STAT 3375Q 6.3 6.5 University of Connecticut Outline 6.3 The Metho
School: UConn
Outline 6.7 Order Statistics STAT 3375Q 6.7 Order Statistics Steven Chiou University of Connecticut Steven Chiou STAT 3375Q 6.7 Order Statistics University of Connecticut Outline 6.7 Order Statistics 1 6.7 Order Statistics Steven Chiou STAT 3375Q 6.7 Orde
School: UConn
Outline 5.2 Bivariate and Multivariate Probability Distributions STAT 3375Q 5.2 Steven Chiou University of Connecticut Steven Chiou STAT 3375Q 5.2 University of Connecticut Outline 5.2 Bivariate and Multivariate Probability Distributions 1 5.2 Bivariate a
School: UConn
Outline 5.5 The Expected Value of a Function of Random Variables 5.6 Special Theorems STAT 3375Q 5.5-5.6 Steven Chiou University of Connecticut Steven Chiou STAT 3375Q 5.5-5.6 University of Connecticut Outline 5.5 The Expected Value of a Function of Rando
School: UConn
Outline 5.8 The Expected Value and Variance of Linear Functions of Random Variables 5.11 Conditional Expectations STAT 3375Q 5.8 and 5.11 Steven Chiou University of Connecticut Steven Chiou STAT 3375Q 5.8 and 5.11 University of Connecticut Outline 5.8 The
School: UConn
Outline 5.7 The Covariance of Two Random Variables STAT 3375Q 5.7 Steven Chiou University of Connecticut Steven Chiou STAT 3375Q 5.7 University of Connecticut Outline 5.7 The Covariance of Two Random Variables 1 5.7 The Covariance of Two Random Variables
School: UConn
Outline 4.3 Expected Values for Continuous Random Variables 4.4 The Uniform Probability Distribution 4.5 The Norm Probab STAT 3375Q 4.3-4.5 Steven Chiou University of Connecticut Steven Chiou STAT 3375Q 4.3-4.5 University of Connecticut Outline 4.3 Expect
School: UConn
Outline 4.6 The Gamma Probability Distribution STAT 3375Q 4.6 Steven Chiou University of Connecticut Steven Chiou STAT 3375Q 4.6 University of Connecticut Outline 4.6 The Gamma Probability Distribution 1 4.6 The Gamma Probability Distribution Steven Chiou
School: UConn
Outline 4.2 The Probability Distribution for a Continuous Random Variable STAT 3375Q 4.2 Steven Chiou University of Connecticut Steven Chiou STAT 3375Q 4.2 University of Connecticut Outline 4.2 The Probability Distribution for a Continuous Random Variable
School: UConn
Outline 4.7 The Beta Probability Distribution 4.9 Other Expected Values STAT 3375Q 4.7-4.9 Steven Chiou University of Connecticut Steven Chiou STAT 3375Q 4.7-4.9 University of Connecticut Outline 4.7 The Beta Probability Distribution 4.9 Other Expected Va
School: UConn
Outline Section 3.7 - 3.9 Discrete Distributions Summary STAT 3375Q Chapter 3.7-3.9 Steven Chiou University of Connecticut 09/20/2012 Steven Chiou STAT 3375Q Chapter 3.7-3.9 University of Connecticut Outline Section 3.7 - 3.9 Discrete Distributions Summar
School: UConn
Outline Chapter 3 STAT 3375Q Chapter 3.1 - 3.3 Steven Chiou University of Connecticut 09/11/2012 Steven Chiou STAT 3375Q Chapter 3.1 - 3.3 University of Connecticut Outline Chapter 3 1 Chapter 3 3.1 Basic Denition 3.2 The Probability Distribution for a Di
School: UConn
Outline Section 3.5 - 3.6 STAT 3375Q Chapter 3.5-3.7 Steven Chiou University of Connecticut 09/18/2012 Steven Chiou STAT 3375Q Chapter 3.5-3.7 University of Connecticut Outline Section 3.5 - 3.6 1 Section 3.5 - 3.6 3.5 The Geometric Probability Distributi
School: UConn
Outline 3.4 The Binomial Probability Distribution STAT 3375Q Chapter 3, part 2 Steven Chiou University of Connecticut Steven Chiou STAT 3375Q Chapter 3, part 2 University of Connecticut Outline 3.4 The Binomial Probability Distribution 1 3.4 The Binomial
School: UConn
Outline Chapter 2 STAT 3375Q Chapter 2.7 2.9 Steven Chiou University of Connecticut 09/04/2012 Steven Chiou STAT 3375Q Chapter 2.7 2.9 University of Connecticut Outline Chapter 2 1 Chapter 2 2.7 Conditional Probability and the Independence of Events 2.8:
School: UConn
Outline Chapter 2 STAT 3375Q Chapter 2.6 Steven Chiou Department of Statistics University of Connecticut 08/30/2012 Steven Chiou STAT 3375Q Chapter 2.6 Department of Statistics University of Connecticut Outline Chapter 2 1 Chapter 2 2.6 Tools for Counting
School: UConn
Outline Chapter 2 STAT 3375Q Chapter 2 Steven Chiou Department of Statistics University of Connecticut 08/28/2012 Steven Chiou STAT 3375Q Chapter 2 Department of Statistics University of Connecticut Outline Chapter 2 1 Chapter 2 2.3 A Review of Set Notati
School: UConn
Outline Chapter 2 STAT 3375Q Chapter 2.4-2.5 Steven Chiou Department of Statistics University of Connecticut 08/30/2012 Steven Chiou STAT 3375Q Chapter 2.4-2.5 Department of Statistics University of Connecticut Outline Chapter 2 1 Chapter 2 Some extra not
School: UConn
Outline Chapter 1, section 3 STAT 3375Q Chapter 1 Steven Chiou University of Connecticut 08/28/2012 Steven Chiou STAT 3375Q Chapter 1 University of Connecticut Outline Chapter 1, section 3 1 Chapter 1, section 3 Steven Chiou STAT 3375Q Chapter 1 Universit
School: UConn
Course: Introduction To Statistics I
The objective of the sampling is to determine whether there is sufficient evidence to indicate that the fraction defective, p, is less than 0.05. Consequently, we will test the null hypothesis that p=0.05 against the alternative hypothesis that p<0.05. B
School: UConn
Course: Introduction To Statistics I
The least squared lines is one that has the following two properties: o The sum of the errors equals 0 o The sum of squared errors is smaller than for any other straight-line model Errors of prediction- the vertical distances between observed and predic
School: UConn
Course: Introduction To Statistics I
Conditions required a valid large-sample confidence interval for p o A random sample is selected from the target population o The sample size is large Conditions required for Valid Small-Sample Inferences about (mu1-mu2) o The two samples are randomly s
School: UConn
Course: Introduction To Statistics I
If a random sample of n observations is selected from a population with a normal distribution, the sampling distribution of x bar will be a normal distribution Consider a random sample of n observations selected from a population with mean and standard
School: UConn
Course: Introduction To Statistics I
The trials are independent The binomial random variable x is the number of Ss in n trials The standard normal distribution is a normal distribution with mu=0 and sigma=1. A random variable with a standard normal distribution, denoted by the symbol x, i
School: UConn
Course: Introduction To Statistics I
Events A and B are independent events if the occurrence of B does not alter the probability that A has occurred If events A and B are independent, the probability of the intersection of A and B equals the product of the probabilities of A and B If n el
School: UConn
Course: Introduction To Statistics I
The measurement comes from a different population The measurement is correct but represents a rare event Lower quartile- the 25th percentile of a data set. The middle quartile is the median. the upper quartile is the 75th percentile Interquartile range
School: UConn
Course: Introduction To Statistics I
Mean- the sum of the measurements divided by the number of measurements contained in the data sest Median- the middle number when the measurements are arranged in ascending order Skewed- a data set is said to be skewed if one tail of the distribution h
School: UConn
Course: Introduction To Statistics I
Representative sample- exhibits characteristics typical f those possesd by a population of interest Random sample- a sample selected from the population in such a way that every sample of size n has an equal chance of selection Statistical thinking- in
School: UConn
Course: Introduction To Statistics I
Statistics- the science of data. It involves collecting, classifying, summarizing, organizing, analyzing, and interpreting numerical information Descriptive Statistics- utilizes numerical and graphical methods to look for patterns in a data set, to summ
School: UConn
Course: Introduction To Statistics
10/2/08 Statistics Reviewing Test Check grade for statistics test Do minitabs 3 and 4 tonight Email professor if score is not posted Check the extra credit and try Start looking through textbook Discrete countable only in whole numbers Continuous any
School: UConn
Course: Introduction To Statistics
9/23/08 Statistics Exam coming soon Look at syllabus Last time: 1. The compliment of A, AC a. P(A) + P(Ac) =1 b. P(Ac) = 1 P(A) c. P(A)= 1 P(Ac) 2. P(A or B)=P(A) + P(B) P(A and B) 3. If events are mutually exclusive P(A and B)=0 4. If events are mutually
School: UConn
Course: Introduction To Statistics
9/18/08 Statistics Box plot Graphical display using 5-number summary Combinations Factorials o 5! =5*4*3*2*1=120 o 3!=3*2*1=6 o 1!=1 o 0!=1 by definition Combination formula o (n things take (r) amount of time) = nCr= n!/r!(n-r)! o Example: 4 items (A
School: UConn
Course: Introduction To Statistics
9/11/08 Statistics Last Time Chebyshevs Rule Percentiles: o Order Data o Find location of relevant data values A) n(r) not a whole number; use data value in the next whole number position B) n(r) is a whole number; use average of data value in that po
School: UConn
Course: Introduction To Statistics
9/9/08 Statistics Symmetrical Distribution Multiple modes Skewed to the left mode is the largest value Skewed to the right mean s the largest value Measures of spread and variability Standard deviation o Interpreting standard deviation Chebyshevs The
School: UConn
Course: Introduction To Statistics
9/2/08 Statistics Histograms No Spaces in-between bars (unlike bar graphs) No Overlap (class: 30-39, 40-49, 50-59, etc.) o Class Frequency Relative Frequency o 30-39 3 .12 or 12% o 40-49 4 .16 or 16% o 50-59 7 .28 or 28% o 60-69 6 .24 or 24% o 70-79 2 .
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: Design Of Experiments
Statistics;-: _ A Nested Factorial Design Example: An investigator who wished to increase the number of rounds per minute that could be fired from a naval gun devised a new loading method (method I) with the intent of improving performance in this task ov
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 9 Point Estimates for Variance Components in One Way Random ANOVA We use the methods of moments approach to derive point estimates for the variance components: 1. To estimate 2 " we use the fact that 2 " E(M SE ) = and theref
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 16 Statistical Analysis of a Four Factorial Fixed Eects Model The model for a four way ANOVA model is Yijklm = + Ai + Bj + Ck + Dl +ABij + ACik + ADil + BCjk + BDjl + CDkl +ABCijk + ABDijl + ACDikl + BCDjkl +ABCDijkl + "m(ikk
School: UConn
Course: Design Of Experiments
Handout 17 Blocking in a Factorial Design In some experiments in which factorial designs are used, because of experimental conditions the experiment cannot be carried out in one time period or one location. In such situations the experiment is done in blo
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 18 2k Factorial Design of Experiments Example: A mechanical engineer is studying the thrust of force developed by a drill press. The following two factors are considered to be the most important ones: Feed Rate of the materia
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 20 Confounding in a 2k Factorial Experiments In many experiments it is impossible to perform a complete replicate of a factorial design. In which case one performs the experiment in several blocks, where the block size is sma
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 21 Fractional Replication for a 2k Factorial Design. Quite often a single replication of a factorial experiment is beyond the resources of an investigator, or there is no real need to execute the entire replicate of the exper
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 19 A Single Replicate in a 2k Design In some experiments only one replicate of all the 2k treatments combinations are observed. In that case one cannot analyze all the effects in the model. To address this issue one can emplo
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Model: Handout 22 Yijk = + Ai + Bj + (AB)ij + k(ij), i =1,.,a, j=1,.,b, k=1,.,n. We have discussed this model for the case when both A and B are fixed factors. If both factors are random we will say that it is a random model and if o
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 23 Analysis of a Random Model Example: The factors that influence the braking strength of a synthetic fiber are being studied. Four production machines and three operators were chosen at random and a factorial experiment with
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 25 Nested Designs In certain multifactor experiments the levels of one factor (B) are similar but not identical for different levels of another factor (A). A design of that nature is called a nested design or a hierarchical d
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515Q Handout 26 A General Multi Stage Balanced Nested Design The two-stage nested design can be easily extended to a general m-stage nested design. The following example describes an experiment to be modeled by a three-stage nested desig
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 24 Miltifactorial Designs - Mixed Models Example: An experiment on diesel engines was conducted to determine the causes of variability in brake specific fuel consumption. Four different engines (E) of the same type and four d
School: UConn
Course: Design Of Experiments
Statistics 3515Q/5515 Handout 28 Multifactor Experiments with Randomization Restrictions The Split-Plot Design Example: A paper manufacturer is interested to study the effect of three different methods of preparing the pulp and four different cooking temp
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series Forecasting ARMA Processes Copyright: Nalini Ravishanker, Univ. of Connecticut Forecasting ARMA Processes Consider a stationary and invertible ARMA(p,q) process (B)Xt = (B)wt with MA representation given by Xt = j=0 j w
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series Review of Regression Methods Copyright: Nalini Ravishanker, Univ. of Connecticut Review of Regression Methods A regression model is a mechanism that enables us to describe the eect of one or more explanatory variables (
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series Time Series Regression Copyright: Nalini Ravishanker, Univ. of Connecticut Deterministic Time Series Regression Methods We will study the following: Structural Decomposition Trend Fitting by Polynomial Trend Models Tren
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series Introduction to Time Series Copyright: Nalini Ravishanker, Univ. of Connecticut Introduction: Characteristics of Time Series Text: Time Series Analysis and its Applications. R.H. Shumway and D.S. Stoer. Third edition, 2
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series Time Series Smoothing Methods Copyright: Nalini Ravishanker, Univ. of Connecticut Time Series Smoothing Methods In tting polynomial trend models, we assume that the parameter values are constant, and use of least square
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series Regression with Autocorrelated Errors Copyright: Nalini Ravishanker, Univ. of Connecticut Regression with Autocorrelated Errors Consider the SLR model in (2.2) or the MLR model in (2.3). When data is observed over time,
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series Conditional Heteroscedasticity - ARCH/GARCH Models Copyright: Nalini Ravishanker, Univ. of Connecticut Conditional Heteroscedasticity - ARCH/GARCH Models In some applications (nancial), we are interested not only in the
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series Stochastic Properties of Time Series Copyright: Nalini Ravishanker, Univ. of Connecticut Stationarity, Strict and Weak Stationarity Stationarity. A time series cfw_Xt , t = 0 1, 2, is stationary if its statistical prop
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series Fitting ARIMA Models Copyright: Nalini Ravishanker, Univ. of Connecticut Fitting ARIMA Models There are three broad phases in tting ARIMA models: Model Identication Model Estimation Model Diagnostics - Model Adequacy an
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series ARIMA Processes Copyright: Nalini Ravishanker, Univ. of Connecticut ARIMA Processes We discuss AR, MA, ARMA and ARIMA models and their properties The process cfw_Xt is an AutoRegressive Moving Average process with AR o
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series State Space Models Copyright: Nalini Ravishanker, Univ. of Connecticut Examples of Time Series Let yt denote a possibly vector-valued time series. Goal: model patterns in yt and predict future values. In some examples,
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series Vector Time Series Copyright: Nalini Ravishanker, Univ. of Connecticut Vector Time Series Let cfw_Xt , t = 0, 1, 2, and cfw_Yt , t = 0, 1, 2, be two time series, with respective means x and y respectively. Joint Stati
School: UConn
Course: Applied Time Series
STAT 4825/5825: Applied Time Series Seasonal ARIMA Models Copyright: Nalini Ravishanker, Univ. of Connecticut Seasonal ARIMA Models Multiplicative seasonal ARIMA(p, d, q) (P, D, Q)s model: (B)(B s )(1 B)d (1 B s )D Xt ) = (B)(B s )Wt (10.1) where Wt WN(0,
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 August 22, 2013 Outline I 1 Introduction 2 Chapter 1 Introduction What is Statistics Statistics is the science o
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 18, 2013 Outline I 1 Chapter 6 Function of Random Variables 6.3 The Method of Distribution Functions 6.
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 October 24, 2013 Outline I 1 Chapter 5: Multivariate Probability Distributions 5.2 Bivariate and Multivariate Pr
School: UConn
Course: Variable Topics
Lecture 6: NHST for Discrete Data Binomial Distribution o Binomial = when we have two outcomes i.e. success vs. failures Heads vs. tails, red card vs. black card o Recall the pmf (probability mass function) and cmf (cumulative mass function) o Recall th
School: UConn
Course: Variable Topics
Lecture 7: Effect Sizes Just because something is statistically significant does not mean it is practically or clinically significant o You should use effect sizes in conjunction with NHST; effect sizes give you an indication of the magnitude of the effec
School: UConn
Course: Variable Topics
Lecture 8: Power 4 basic possible scenarios at the end of a study: ActualStateofAf TrueNull Decision Failto correct False typeII RejectNull RejectNull typeIerror o If the null hypothesis is true and we reject the null, this is a type 1 error .05 = probab
School: UConn
Course: Variable Topics
Lecture 5: t-tests Recall z-tests (review) o When we compared a single person to a population mean, we used: Z = score sample mean/sample SD o When we compared a sample mean to a population mean, we used: Z = (Xbar pop mean) / pop SD (or SE) o We used t
School: UConn
Course: Variable Topics
Lecture 3: Confidence Intervals Confidence Interval: gives a range of values that have a set probability of containing the population parameter; the P of the CI containing that parameter is the confidence level o i.e. a 95% CI has P=.95 of containing the
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Course: Variable Topics
Stats: Lecture 1 Basic Concepts Variable: a condition or characteristic that can have different values o Examples of variables: depression, conscientiousness o Independent variable (IV): variable that is manipulated and has an effect on a dependent variab
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Course: Variable Topics
Stats: Lecture 2 Basic Concepts Degrees of freedom (df): number of data points free to vary o How many observations you need to know before having all information about a sample o i.e. We have four data points. How many data points do we need to calcula
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Introduction to Biostatistics Stat 4625/5625 SAS Ofer Harel Department of Statistics University of Connecticut SAS p. 1/ SAS 1. SAS stands for "Statistical Analysis System" 2. The software was conceived 1966. 3. It started as code for ANOVA and Regression
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Introduction to Biostatistics Stat 4625/5625 Chapter 5 Ofer Harel Department of Statistics University of Connecticut Chapter 5 p. 1/1 Introduction Our goal is to examine a scientic hypothesis about the population. We use only one study (experiment) on a s
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Introduction to Biostatistics Stat 4625/5625 Chapter 8 Ofer Harel Department of Statistics University of Connecticut Chapter 8 p. 1/2 Introduction There are different type of associations that one might be interested in In this section we deal with test f
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Introduction to Biostatistics Stat 4625/5625 Chapter 6 Ofer Harel Department of Statistics University of Connecticut Chapter 6 p. 1/4 Introduction A frequent problem in medical research is the comparison of two groups The 2 groups can formed from an exper
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Introduction to Biostatistics Stat 4625/5625 Chapter 7 Ofer Harel Department of Statistics University of Connecticut Chapter 7 p. 1/4 Introduction In the previous chapter we talked about continuous variables or at least ordinal variables What can we do wh
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Introduction to Biostatistics Stat 4625/5625 Introduction Ofer Harel Department of Statistics University of Connecticut Intro p. 1/15 Introduction to Biostatistics Monday 11:15-1:15, Wednesday 11:15-12:05, CLAS 344 On occasions we will meet in the teachin
School: UConn
Course: Introduction To Mathematical Statistics
l. 'Iiue or False For each statement below, use "a" if you think it is true; " x" if you think it is false. You do not need to justify your answer nor need to show werk. For the following True of False questions, dene X, Y to be continuous random variable
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375: Practice Final Exam 1. Consider the discrete random variable Y with probability function p(y) = 4 , for y = 1, 2, 3, 5y (a) Find P (Y 3 | Y 1). (b) Find P (Y 3 | Y 1). (c) Find the moment generating function of Y . (d) Find E(Y ). (e) Find V (
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375 Practice Midterm Exam I Solution Fall 2013 September 28, 2013 Student Name (Print): _ PeopleSoft ID: _ 1. True or False For each statement below, use " if you think it is true; " if you think it is false. You do not need to justify your answer n
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375: Practice Final Exam 1. Consider the discrete random variable Y with probability function p(y) = (a) (b) (c) (d) (e) 4 , for y = 1, 2, 3, 5y Find P (Y 3 | Y 1). Find P (Y 3 | Y 1). Find the moment generating function of Y . Find E(Y ). Find V (
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375 Practice Midterm Exam I Fall 2013 September 28, 2013 NAME: _ STUDENT ID: _ Please read all of the following information before starting the exam: You are allowed to use ONE page of formula sheet, double sided. Calculator is not needed nor allo
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375 Practice Midterm Exam I Solution Student Name (Print): _ PeopleSoft ID: _ 1. True or False For each statement below, use " if you think it is true; " if you think it is false. You do not need to justify your answer nor need to show work. (a) (b)
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375 Practice Midterm Exam II Fall 2013 November 7, 2013 NAME: _ STUDENT ID: _ Please read all of the following information before starting the exam: You are allowed to use ONE page of formula sheet, double sided. Calculator is not needed nor allow
School: UConn
Course: Statistical Methods (Calculus Level I)
Stat3025StatisticalMethod(CalculusIlevel) FinalExamSample Instructor:BOZHAO Thissamplecontainsafewpracticeproblemsforyoutowarmup,butyoushouldneverexpect toonlyrelyonthefewproblemshere,pleasealsomakesuretoreviewallthelectureexamples andhomework(withtheexam
School: UConn
Course: Statistical Computing
Followup:libraryinformation Comingupwitharesearchtopic Doresearchononeimportantissueofconcerntoglobalfeministmovement,in aparticularplace,andhowcommunitiesinthatplaceareworkingforchange Thinkofwhichtopic/issueyouwanttoinvestigateandaplace Brainstormingver
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Course: Statistical Computing
FeministOrganizing Feminists organizing can be defined as social movements around the issues of importance to women and society i.e. campaigns that feed into a network of womens groups and increase awareness of womens problems and rights, such as : womens
School: UConn
Course: Statistical Computing
Topics for Final Exam 1. Measures of Central Tendency what they are and what is the advantage and disadvantage (if any) of each of them. 2. Calculate means and st. deviations for data in a single column or data in table form in two columns. 3. Use the ran
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Course: Statistical Computing
One sample hypothesis tests of a proportion or mean and matched pairs t-test 1. A provincial politician claims that at least 80% of parents are satisfied with the public school system. A critic wishes to prove that the politician's claim is wrong and that
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
Statistics 3115&5315 - Solutions to Practice Problems for Midterm Exam 1 Prof. Lynn Kuo Date: September 26, 2014 Name: 1. (a) R2 = SS(M odel)/SS(total) = 100/300 = 0.333, 33.3% of the variation in Y can be explained by the linear regression model, so the
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Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
Statistics 3115&5315 - Practice Problems for Midterm Exam 1 Prof. Lynn Kuo Date: September 22, 2014 Name: Answer all questions (with explanations if needed) in the spaces provided. 1. Suppose you t the model y = 0 + 1 x1 + 2 x2 + 3 x3 + E to n = 20 data p
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School: UConn
School: UConn
Course: Intro To Statistics II
Midterm Practice Problems Hints at the end for #6,12,13,16. 1. You are comparing two groups in terms of depression. You run an analysis and find the MS within groups equals 42.5. Youve also run a ttest to examine wh
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Course: Intro To Statistics II
More midterm practice questions: 1 Subjects' recall of a word list is measured both before and after imagery instructions as shown below. What is the value of Pearson's r? No Imagery Imagery
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Midterm 2 STAT 3375 - 04 Instructor: Steven Chiou 11/18/2011 Please read all of the following information before starting the exam: You are allowed to use only ONE page of cheat sheet, double sided. Answer each question completely. Show all work, clearl
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Course: Mathematical Statistics II
Quiz 3, STAT 5685 Mathematical Statistics II, Spring 2013 Name: Points: 1. (10 points) Suppose that X1 , . . . , Xn form a random sample from U (0, ). Let Xn:n be the largest order statistic. (a) (5 points) Find the UMVUE of 2 . 2 (b) (5 points) Find E [X
School: UConn
Course: Mathematical Statistics II
Quiz 2, STAT 5685 Mathematical Statistics II, Spring 2013 Name: Points: 1. (10 points) Let X be an exponential variable with mean > 0. (a) (5 points) Find IX (), the Fisher information about in X . (b) (5 points) Let = 1/ be the rate parameter of the expo
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Course: Mathematical Statistics II
Quiz 1, STAT 5685 Mathematical Statistics II, Spring 2013 Name: Points: 1. (10 points) Let X1 , . . . , Xn be an iid sample from N (, ), where > 0 is unknown. (a) (5 points) Does N (, ) belong to the exponential family? Justify. (b) (5 points) Derive a mi
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Course: Mathematical Statistics II
Quiz 5, STAT 5685 Mathematical Statistics II, Spring 2013 Name: Points: 1. (10 points) Suppose that X1 , . . . , Xn form a random sample from N (0, ). (a) (5 points) Find a variance stabilizing transformation for n i=1 n 2 Xi 4 . Note that E (X1 ) = 32 .
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Course: Mathematical Statistics II
Quiz 4, STAT 5685 Mathematical Statistics II, Spring 2013 Name: Points: 1. (10 points) Suppose that X1 , . . . , Xn form a random sample from (a, ) distribution with mean a, and a is known. (a) (5 points) Does this distribution has MLR in some sucient sta
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Solutions to QUIZ 5 practice problems 5.6 .5 1 .5 .5 1dy1dy2 = [ y1 ]y2 +.5 dy2 = (.5 y2 )dy2 = .125. a. P(Y1 Y2 > .5) = P(Y1 > .5 + Y2 ) = 1 0 y 2 +.5 0 0 1 b. P(Y1Y2 < .5) = 1 P(Y1Y2 > .5) = 1 P(Y1 > .5 / Y2 ) = 1 1 1 1dy1dy2 = 1 (1 .5 / y2 )dy2 .5
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STAT 3375 (Fall 2011): Quiz-2 Solutions 1. This was a homework problem. Check homework solutions. 2. This tree diagram is helpful. From the gure, 3. Dene events : It rains on Maries wedding, ( 1 14 ) = 3 . 10 + 1 . 1 + 3 . 3 = 36 8 113 360 : The weatherma
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School: UConn
STAT 3345 PROBABILITY MODELS FOR ENGINEERS SPRING 2010 SAMPLE MIDTERM 2 EXAM Problem 1 The lifetime in hours of a certain kind of radio tube is a random variable having a probability density function given by 0 f (y ) = 100 y2 y < 100 if y > 100. (a) Find
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STAT 3345 PROBABILITY MODELS FOR ENGINEERS SPRING 2010 SAMPLE MIDTERM 1 EXAM Problem 1 A university librarian produced the following probability distribution of the number of times a student walks into the library over the period of a semester. x p(x) 0 .
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School: UConn
SOME SAMPLE EXAMS PROBLEMS Problem 1 Suppose the X and Y are jointly distributed according to the probability density function has the following form f (x, y ) = K x2 + 0, xy , if 0 < x < 1 and 0 < y < 2, 2 otherwise. (a) Find the constant K . (b) Find th
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Example. Exercise 2.4 How to read data from the web? 1. 2. 3. Data files for the MINITAB assignments are stored here: http:/www.stat.uconn.edu/~mlan First option. Launch Internet Explorer (not Netscape!). Click Cruise Ship Data. Select Open this file from
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Final Exam Statistics 1XX 1. Confidence interval for a population mean . A sample of 49 measurements of tensile strength (roof hanger) are calculated to have a mean of 2.45 and a standard deviation of 0.25. Determine the 95% confidence interval for the me
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Quiz # 7 Stat 220, Due Nov 16 Name: 1. A Gallup Poll asked a sample of Canadian adults if they thought the law should allow doctors to end the life of a patient who is in great pain and near death if the patient makes a request in writing. The pol
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Quiz # 6 Stat 220, Due Nov 9 Name: 1. The number of column-inches of classified advertisements appearing on Mondays in a certain daily newspaper is roughly normally distributed with mean 327 inches and standard deviation 34 inches. Assume that the
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Midterm Exam Statistics 1XX 1. Descriptive statistics: mean, median, standard deviation, IQR etc. 2. Probability rules, conditional probability, independence etc. P (A) = .4, P (B) = .3, and P (A or B) = .58 Are A and B independent? Mutually exclusi
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Final Exam Statistics 1XX 1. Confidence interval for a population mean . A sample of 49 measurements of tensile strength (roof hanger) are calculated to have a mean of 2.45 and a standard deviation of 0.25. Determine the 95% confidence interval for
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1 Introduction to Hypothesis Testing The purpose of hypothesis testing is to determine whether there is enough statistical evidence in favor of a certain belief about a parameter. Examples 1. Is there statistical evidence in a random sample of pot
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Example. Exercise 2.97 How to read data? 1. 2. 3. 4. To read data from the data CD-ROM (that goes with the textbook) click first File Open Worksheet. It opens the Open Worksheet window. Choose Data(*.dat) as Files of type. The data file is on the da
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Course: Quant Meth In The Behav Sci
1 STAT379MIDTERMEXAM Spring2007 PleasewriteallanswerssimplyandconciselyasifdirectedtoafellowgradstudentlookingforexplanationNOTasifwritinga textbookchapterorencyclopediaentry.Carryoutanycalculationsto4decimalplacestopreventroundingerror. 1. Onttestsa
School: UConn
Course: Introduction To Mathematical Statistics
3.107 The random variable Y follows a hypergeometric distribution with N = 6, n = 2, and r = 4. 3.127 Let Y = # of typing errors per page. Then, Y is Poisson with = 4 and P(Y 4) = .6288.
School: UConn
Course: Introduction To Mathematical Statistics
3.1. P(Y = 0) = P(no impurities) = 0.2 P A B 1 P Y 0 0.8 , so P Y 2 P A B P A P B P A B 0.1 P(Y = 1) = P(exactly one impurity) = P A P B 2P A B 0.7 3.9 The random variable Y takes on vales 0, 1, 2, and 3. a. Let E denote an error on a single entry and let
School: UConn
Course: Introduction To Mathematical Statistics
Solutions for HW3 2.85(10) 2.89(10) 2.135(20) 2.91(10) 2.95(12) 2.110(10) 2.129(10) 2.131(8) 2.133(10) 2.89 a. 0, since they could be disjoint. b. the smaller of P(A) and P(B). 2.91 If A and B are M.E., P( A B) = P(A) + P(B). This value is greater than 1
School: UConn
Course: Introduction To Mathematical Statistics
2.37 a. There are 6! = 720 possible itineraries. b. In the 720 orderings, exactly 360 have Denver before San Francisco and 360 have San Francisco before Denver. So, the probability is .5. 2.38 By the mn rule, 4(3)(4)(5) = 240. 2.42 There are three differe
School: UConn
Course: Introduction To Mathematical Statistics
Solution Find the derivative of the function: 1. f (x) = (2x 3) 3 (x2 + x + 1)5 2 2 1 2 f (x) = (2x 3) 3 2 (x2 + x + 1)5 + (2x 3) 3 5(x2 + x + 1)4 (2x + 1) 3 2 4(x2 + x + 1)5 = + 5(2x 3) 3 (x2 + x + 1)4 (2x + 1) 1 3(2x 3) 3 (x2 + x + 1)4 = 4(x2 + x + 1) +
School: UConn
Course: Intro Mathematical Statistics
Hw1 Stat 3445 s2013 due at the beginning of class Thursday, Jan. 30 A. Suppose that Z has a standard normal distribution and Y = a + bZ, where b > 0. Find the density f (y) of Y and identify the distribution of Y by name. B. Suppose that X, Y are random v
School: UConn
Course: Intro Mathematical Statistics
Hw1 - Stat 3445 s2015 due at the beginning of class Thursday Jan. 29 Z has a standard normal distribution and Y: a*bZ,where the density /(y) of Y and identify the distribution of Y by name. B. Suppose that X, Y are independent random variables with densit
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Course: Intro Mathematical Statistics
HwZ - Stat 3445 s2015 due at the beginning of class Thursday, Feb. 5 Notes: o Reminder: work must be shown to receive credit. o Results derived in cla.ss or in the text may be used without including a proof. o 'oldentify" means to give the family of the d
School: UConn
Course: Statistical Methods (Calculus Level I)
7.1 Basic Properties of Confidence Inten/als 275 In general, the upper and lower condence limits result from replacing each < in (7.6) by = and solving for 6. In the insulating uid example just considered, ZAEXl- = 34.170 gives A = 34.170/(221g) as the up
School: UConn
Course: Statistical Methods (Calculus Level I)
Chapter 4: Continuous Random Variables and Probability Distributions a. f(x = 1% for 25 S x S 35 and = 0 otherwise. b. P(X> 33): Lsdx=2 3 3 20 25 midpoint.) 30 i 2 is from 28 to 32 minutes: ' 32 ' 32 P08 <X< 32) = In +de =+0xllzs = 4' I 2 35 c. E(X) = Ex
School: UConn
Course: Statistical Methods (Calculus Level I)
(A) El nmmWsln-hw 2,712 medn 7- /0.S 623.113.; Mn=$6 mxslg'o (c) LlF= 2.7: (/flf 2/175 => 4/0 awfme Chapter 1: Overview and Descriptive Statistics 60. A comparative boxplot (created in Minitab) of this data appears below. The burst strengths for the tes
School: UConn
Course: Statistical Methods (Calculus Level I)
18. 19. Chapter 3: Discrete Random Variables and Probability Distributions u 3- 170) = P(M= 1) = P(cfw_(1,1)) = % ;p(2) = P(M= 2) = P(cfw_(1,2)(2,1)(2,2)) = 33g; 17(3) = P(M z 3) = P (cfw_(1,3)(2,3)(3,1)(3,2)(3,3) = 35g . Continuing the pattern, p(4) =
School: UConn
Course: Statistical Methods (Calculus Level I)
I Chapter 6: Point Estimation c With [2, i, ] =l~fyl , 132 x2, (22 =1[72 , the estimated standard error is plq + pzqz "I "2 n1 n2 ,. . I27 176 d. = =.635.880 = .245 0" p2) 200 200 . . e_ (.635)(.365) + (.880)(.120) z 041 200 200 E W =mE(SIZ)+E(SZZ) :
School: UConn
Course: Statistical Methods (Calculus Level I)
254 CHAPTER 6 Point Estimation An appropriate probability plot supports the use of the log- normal distribution (see Section 4.5) as a reasonable model for stream ow. a. Estimate the parameters of the distribution. [Hint Remember that X has a lognormal di
School: UConn
Course: Statistical Methods (Calculus Level I)
CHAPTER 7 Section 7.1 . 2,12 = 2.81 implies that (ii/2 = l (D(2.81) = .0025, so a = .005 and the condence level is 100(la)% = 99.5%. zwz = 1.44 implies that a = 2[l @(1.44)] = .15, and the condence level is 100(1-a)% = 85%. P b. c. 99.7% condence implie
School: UConn
Course: Statistical Methods (Calculus Level I)
M3023 M w I W? W 7 (4) ; Mvziwo v5 Ha: ULK/SW 7 0 Z = 1716:; and Z; 7- ~2a f_,_j,: 94>;wje4af aiw , ' U PW?H?SB>5 W Z? tiiwiiQ,E!,f%-39:EE'fS3 i 4 e) h<~; _> F9477 _ _., A. A #7.". _, _ V- . _ .m _._.A.,_,_, ,7, L420; M6) = (~90, Y? 34%
School: UConn
Course: Statistical Computing
Yiming Zhang Husky CT Assignment: Amanda K. Baumle wrote this article Legislating the Family: The Effect of State Family Laws on the Presence of Children in Same-Sex Households due to the United States announcing several laws that prohibits gay male and l
School: UConn
Course: Statistical Computing
Yiming zhang Assignment #4 12/3/13 TBI Video Response 1. What video did you watch? Traumatic Brain Injury: Josh's Story 2. How did the patient injure his/her brain? Josh was on a night out with his friends when drunk driving lead to a car crash accident 3
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
Hw1 Solution: 11: a) P( Z > 5 ) b) a = 187.44, b = 192.56 12: Multiple is t0.975,27 = 2.052 13: (24.66,35.33) 16: T = -0.289, we cannot reject H0 for =0.05. 18: b) 20: a) 21: c) 22: b) 23: a). 1- b). c). d). 1- 24: If the significance level for the hypo
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
Fall 2014 Statistics 3115Q-01/5315-01 Homework Assignments Professor Lynn Kuo THIS IS A TENETATIVE LIST. IT MAY BE UPDATED SEVERAL TIMES DURING THE SEMESTER. SO YOU SHOULD ALWAYS CHECK YOUR HUSKYCT OR YOUR EMAIL BEFORE DOING THE HOMEWORK. Textbook: Applie
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
STAT 3115Q 12.1 a.) HW#5 For Nonsmokers (SMK=0) For Smokers (SMK=1) Y=49.311759+26.302825X Y=79.255330+20.118041X 12.5 12.6 12.8* /* sbpage.sas, options LS=72; title 'ANACOVA data SBP; input QUET SBP cards; 2.876 135 3.251 122 3.1 3.768 148 2.979 146 2.79
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
STAT 3115Q HW#3 Jordan Parley 8.2 a) Patient 5 had an actual score of 25 while the best fit line predicted Y= 16.09. The prediction value is not very reliable at this data point. b) Pathology Score from Thinking Disturbance & Hostile Suspiciousness The RE
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
5.3 a) It seems that a parabola is the best fit. Not a linear relationship b) Parameter Estimates Variable D Parameter F Estimate Standard t Value Pr > |t| Error Intercept 1 19.62575 5.21291 3.76 0.0014 Income 1 0.0007137 6 0.0003528 1 2.02 0.0582 Bo= 19.
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
STAT 3115Q HW#4 Jordan Parley 10.1 a. Based on the matrix, the largest proportion of the total variation in the dependent variable SBP is explained by AGE with largest r=.7752 OPTIONS LS=72; TITLE 'Systolic Blood Pressure Analysis'; DATA NUTRIDEF; INPUT S
School: UConn
Course: Intro To Statistics II
Homework 2 Due Wednesday March 6 Use the Data Set called hot hw2. It contains 16 variables including 8 personality variables. The file is in SPSS and the variables should all be self-explanatory 1) Compute the correlations among the 16 variables and creat
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School: UConn
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llvcfw_b S oZ,f.-o 3 o Zcfw_ szot> 6.t5e1Wtu-*-fr,2*/.(r,t*-g t tuse.-zrc i -i= : e a G.e f r" f -V.z.t; 2 r.' w z 4e ? _ _F .rro7 7 6.2 C l , u t a -x = t d / n , - . t L < ' t a 7 t 6 - , a t t/ -qE-.rn'-, , ; /- x. - = !b! r t =.la tb) l l-,.- Y - 4; ,
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32 The parameter of interest is = the true average dietary intake of zine among males aged 6574 years. The hypotheses are H0 : = 15 vs. Ha : < 15. Since the sample size n = 115 is large, z -test is used. Since this is a lower-tailed test, we need to compa
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Middle East Technical University Electrical and Electronics Engineering Department EE230 Homework 5 Due : Apr. 28, 2006 1. Let the random variable x be uniformly distributed over (0,3) and the function be ~ defined as follows: , x0 0 x , 0 x 1 g (x ) = 1
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School: UConn
School: UConn
School: UConn
School: UConn
School: UConn
School: UConn
School: UConn
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Instructor's Solutions Manual for Applied Regression Analysis and Other Multivariable Methods Chapter 9 c The two variables-added-in-order tests are: i H0: fr = 0 vs. HA: fa. + 0 in the model Y= fa + faX2 + E. From part (a) above: F= 0.59; df:l,51
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Instructor's Solutions Manual for Applied Regression Analysis and Other Multivariable Methods Chapter 16 Chapter 16 Note: wherever possible, values used in the solutions below are taken directly from the SAS output provided in the text. 1. a Forwar
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Instructor's Solutions Manual for Applied Regression Analysis and Other Multivariablc Methods Chapter 5 Chapter 5 Note: wherever possible, values used in the solutions below are taken directly from the SAS output provided in the text. 1. a Dry weig
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Instructor's Solutions Manual for Applied Regression Analysis and Other Multivariable Methods Chapter 5 f The line does not differ from the line plotted in (e). The evidence suggests that there is no significant linear relationship. Determining a
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Instructor's Solutions Manual for Applied Regression Analysis and Other Multivariable Methods Chapter 8 Chapter 8 1. a i 7 = 45.103 + 1.213(50) + 9.946(1) + 8.592(3.5) = 145.771 ii 7 = 45.103 + 1.213(50) + 9.946(0) + 8.592(3.5) = 135.825 iii Y = 45
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Instructor's Solutions Manual for Applied Regression Analysis and Other Multivariable Methods Chapter 12 d From the above tests, we would conclude that the straight lines for smokers and non-smokers are coincident since both tests failed to reject
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Instructor's Solutions Manual for Applied Regression Analysis and Other Multivariable Methods Chapter 10 Chapter 10 Note: wherever possible, values used in the solutions below are taken directly from the SAS output provided in the text. 1. a Age w
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Course: Intro To Applied Statistics -enrollment Restrictions-see Catalog
Homework 2 Solutions Problems 3.31, 3.37*, 3.44, 3.50*, 3.65*, 3.80*, 3.87*, 4.6, 4.9, 4.12, 4.16, 4.18, 4.24, 4.25, 4.29 * Problem should be done with MINITAB. * Problem should be done by hand and with MINITAB. 3.31 In a study of 1,329 American men
School: UConn
Course: Analysis Of Experiments -enrollment Restrictions-see Catalog
Instructor's Solutions Manual for Applied Regression Analysis and Other Multivariable Methods Chapter 9 1 1. a H0: # = fa = 0 vs. HA: pi * 0 and/or fa * 0 in the model 7 = fa + ftX} + /32X2 + E. F=7.18 df:2, 51 P = 0.0018 At a = 0.05, we would reje
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Course: Intro To Applied Statistics -enrollment Restrictions-see Catalog
Homework 5 Solutions Problems 5.7, 5.12, 5.21, 5.28, 5.30, 5.44, 5.52*, 5.59, 5.88*,5.100* * Problem should be done with MINITAB. * Problem should be done by hand and with MINITAB. 5.7 Because the company is collecting samples containing 25 boxes eve
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Stat 100 Worksheet #1 Name _ 1) The number of visitors to the local library for 25 randomly selected time periods is shown below. 15 38 88 53 62 76 48 89 31 19 67 47 38 39 53 86 26 41 63 28 68 98 35 79 54 a) Construct a stem and leaf plot for this
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Course: Statistical Computing
Short Response Answers: 1. I want to be orientation leader because I want to represent how much this campus means to me and hopefully translate that into the incoming freshmen. I love meeting, working, and building relationships with people. Its one of th
School: UConn
Course: Statistical Computing
Local University GPA and SAT scores 4.00 3.75 GPA 3.50 3.25 3.00 2.75 2.50 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 S R-Sq R-Sq(adj) 0.397020 2.5% 0.0% Sat Score YimingZhangSe ction015D Regression Line for GPA AND SAT scores GPA = 2.570 + 0.00028
School: UConn
Course: Statistical Computing
Yiming Zhang Husky CT Assignment: The article by Shelia Grant Bunch discusses takes a look at the different experiences of 23 grandmothers who are providing full time child care to their grandchildren due to military deployment of their own child. The aut
School: UConn
Course: Statistical Computing
Yiming Zhang Group Presentation Response: Domestic Violence in the NFL Our group agreed on this topic because domestic violence resulted in some of the most viral and controversial gender inequality incidents that has occurred this year. The topic has aff
School: UConn
Final Exam Information Instructor: Steven Chiou General Information The nal examination for this course will be given at 10:30 a.m. on Tuesday, December 11 in our regular class room, CB 206. The exam will be roughly the same length of the second midterm.
School: UConn
Course: Introduction To Mathematical Statistics
STAT 3375Q: Introduction to Mathematical Statistics Kun Chen Assistant Professor Department of Statistics University of Connecticut Storrs, CT November 21, 2013 Outline I 1 Chapter 6 Function of Random Variables 6.3 The Method of Distribution Functions 6.
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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
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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