STAT 501 Assignment 1 Name Spring Written Assignment: Due Monday, January 22, in class. Please write your answers on this assignment
|
|
- Ashley O’Connor’
- 5 years ago
- Views:
Transcription
1 STAT 5 Assignment Name Spring Reading Assignment: Johnson and Wichern, Chapter, Sections.5 and.6, Chapter, and Chapter. Review matrix operations in Chapter and Supplement A. Examine the matrix properties presented in exercises.,.,.,. and. at the end of Chapter. Written Assignment: Due Monday, January, in class. Please write your answers on this assignment. Consider a random vector (,, ) with mean vector µ (,, ) and covariance matrix Ó. The eigenvalues of Ó are ë 5, ë 9, ë 5 and the corresponding eigenvectors are ( 5 ) / ( ) ( ) 5 / / 5 e e 5 / / 5 5/ / e / Evaluate the following quantities. Parts (a), (b), (c), (d) and (g) can be easily done without using either a computer or a calculator. (a) Ó (b) trace(ó) (c) V(e ) (d) (e)
2 (f) / ' ' (g) Define Y e, Y e, and Y e. Find the mean vector and covariance matrix for Y (Y Y Y )' à ', where the i-th column of à is the i-th eigenvector for Ó. Evaluate ' (Y) E (Y) V Γ ' Γ (h) The linear transformation, Y Γ, examined in part (g) is often called a rotation because it corresponds to simply rotating the coordinate axes. Use the definition of eigenvectors to show that lengths of vectors are not changed by this transformation, i.e., show Y ' Y ' when Y Γ. (i) Let à be defined as in part (g), and let Y Γ, µ E( ), V (), µ Y Γ ' µ and Y V(Y) Γ ' Γ. Determine which of the following measures of variability or distance are unaffected by rotations. Justify your answers. (a) Is Ó Ó Y? (b) Is trace (Ó ) trace (Ó Y )? (c) Is Ó Ó Y?
3 (d) Is ( µ )' ( µ ) (Y µ )' (Y )? Y Y µ Y (e) Is ( µ )'( µ ) (Y µ )' (Y )? Y µ Y. Consider a bivariate normal population with mean vector where µ and 9 µ and covariance matrix Ó, 5 (a) Write down a formula for the joint density function. (b) Find the eigenvalues and eigenvectors for Ó. λ λ e e (c) Find values for Ó trace (Ó) (d) Write down the formula for the boundary of the smallest region such that there is probability.5 that a randomly selected observation will be inside the boundary. (e) Sketch the boundary of the region in part (d)
4 - (f) Determine the area of the region described in parts (d) and (e). (g) Determine the area of the smallest region such that there is probability.95 that a randomly selected observation will be in the region.. Let be a normally distributed random vector with µ and (a) Indicate which of the following are pairs of independent random variables by drawing a circle around the appropriate pairs. (i) and (iv) and + (ii) and (v) + and - (iii) and - (vi) + + and - + (b) What is the distribution of Y (, )? (c) What is the conditional distribution of Y (, ) given x? (d) Find the correlation between and and a formula for the partial correlation between and given x. ρ ρ (e) What is the conditional distribution of given x and x?
5 5. Suppose, N µ where 9 µ (a) What is the distribution of Z +? (b) What is the joint distribution of Z in part (a) and Z +. (c) Find the conditional distribution of given x, x. (d) Find the partial correlation between and given x and x.. ρ 5. Let be Y and, N µ be, N µ where and Y are independent and 8 6 µ µ (a) Find + Y Y, Cov
6 6 (b) Are Y and + Y independent random vectors? Explain. (c) Show that the joint distribution for the four dimensional random vector + Y Y is a multivariate normal distribution. 6. Let d(p,q) denote a measure of distance between p and q. Johnson and Wichern indicate that any distance measure should possess the following four properties: (i) d(p, q) d (q, p) (ii) d(p, q) > if (iii) d(p, q) if p p q q (iv) d(p, q) d(p, r) + d(r,q), where r ( r,r )'. These properties are satisfied, for example, by Euclidean distance, i.e.,. d(p, q) / [ (p q)' (p q)] (a) In this class we will often consider distance measures of the form / d(p, q) [ (p q)' A (p q)]. Sometimes A will be the inverse of a covariance matrix which implies that A is symmetric and positive definite. Show that properties (i) through (iv) are satisfied when A is symmetric and positive definite.
7 7 (b) Does A have to be both symmetric and positive definite for / d(p, q) [ (p q)' A (p q)] to satisfy properties (i) through (iv)? Present your proofs or explanations. (c) It is obvious that the measure d(p,q) max{ p q, p q,..., p q } satisfies properties (i), (ii), (iii). Either prove or disprove that it satisfies property (iv), the triangle in equality. k k 7. One frequently used technique in multivariate analysis first reduces a multivariate problem to a more familiar univariate problem by considering linear combinations of the elements of a random vector, and then defines the "optimal" linear combination as a multivariate measure. For example, consider a p-dimensional random vector (,,..., p) ' with mean vector µ ( µ, µ,..., µ p) ' and covariance matrix Ó. Using a vector of constants, a (a, a,..., ap) ', define a random variable
8 Y a' a + a a. Then the mean and variance of Y are p p a' µ and a' a, respectively, and the standardized distance of Y from its mean is 8 d (a) / Y a' µ / a' a. Define D² maximum {d²( a ): ' a a and p a ε R }. as the maximum squared "standardized distance" of from µ. (a) Use the extended Cauchy-Schwarz inequality in Section.7 of Johnson and Wichern to show a c ( µ ) provides the maximum of d²( a ) in the definition of D², where c is a normalizing constant selected to make a '. a (b) Do you recognize D² as a measure presented in the lectures for STAT 5? If so, what is its name? (Note that we did not assume that has a multivariate normal distribution.) For additional practice you could do problems.6,.8,.9,.,.,.6 at the end of Chapter and problems.,.,.,.5,.,.5,.6,.7 at the end of Chapter. Problem.8 gives a trivial example of a non-normal bivariate distribution with normal marginal distributions. Do not hand in these additional problems, but answers will be given on the answer sheet for this assignment.
STAT 501 Assignment 1 Name Spring 2005
STAT 50 Assignment Name Spring 005 Reading Assignment: Johnson and Wichern, Chapter, Sections.5 and.6, Chapter, and Chapter. Review matrix operations in Chapter and Supplement A. Written Assignment: Due
More informationEXAM # 3 PLEASE SHOW ALL WORK!
Stat 311, Summer 2018 Name EXAM # 3 PLEASE SHOW ALL WORK! Problem Points Grade 1 30 2 20 3 20 4 30 Total 100 1. A socioeconomic study analyzes two discrete random variables in a certain population of households
More informationT i t l e o f t h e w o r k : L a M a r e a Y o k o h a m a. A r t i s t : M a r i a n o P e n s o t t i ( P l a y w r i g h t, D i r e c t o r )
v e r. E N G O u t l i n e T i t l e o f t h e w o r k : L a M a r e a Y o k o h a m a A r t i s t : M a r i a n o P e n s o t t i ( P l a y w r i g h t, D i r e c t o r ) C o n t e n t s : T h i s w o
More informationDependence. Practitioner Course: Portfolio Optimization. John Dodson. September 10, Dependence. John Dodson. Outline.
Practitioner Course: Portfolio Optimization September 10, 2008 Before we define dependence, it is useful to define Random variables X and Y are independent iff For all x, y. In particular, F (X,Y ) (x,
More informationExam 2. Jeremy Morris. March 23, 2006
Exam Jeremy Morris March 3, 006 4. Consider a bivariate normal population with µ 0, µ, σ, σ and ρ.5. a Write out the bivariate normal density. The multivariate normal density is defined by the following
More informationSTAT 501 Assignment 2 NAME Spring Chapter 5, and Sections in Johnson & Wichern.
STAT 01 Assignment NAME Spring 00 Reading Assignment: Written Assignment: Chapter, and Sections 6.1-6.3 in Johnson & Wichern. Due Monday, February 1, in class. You should be able to do the first four problems
More informationSTAT 501 EXAM I NAME Spring 1999
STAT 501 EXAM I NAME Spring 1999 Instructions: You may use only your calculator and the attached tables and formula sheet. You can detach the tables and formula sheet from the rest of this exam. Show your
More informationStat 206: Sampling theory, sample moments, mahalanobis
Stat 206: Sampling theory, sample moments, mahalanobis topology James Johndrow (adapted from Iain Johnstone s notes) 2016-11-02 Notation My notation is different from the book s. This is partly because
More informationRandom Vectors. 1 Joint distribution of a random vector. 1 Joint distribution of a random vector
Random Vectors Joint distribution of a random vector Joint distributionof of a random vector Marginal and conditional distributions Previousl, we studied probabilit distributions of a random variable.
More informationMATH 1553 SAMPLE FINAL EXAM, SPRING 2018
MATH 1553 SAMPLE FINAL EXAM, SPRING 2018 Name Circle the name of your instructor below: Fathi Jankowski Kordek Strenner Yan Please read all instructions carefully before beginning Each problem is worth
More informationAnnouncements (repeat) Principal Components Analysis
4/7/7 Announcements repeat Principal Components Analysis CS 5 Lecture #9 April 4 th, 7 PA4 is due Monday, April 7 th Test # will be Wednesday, April 9 th Test #3 is Monday, May 8 th at 8AM Just hour long
More informationDependence. MFM Practitioner Module: Risk & Asset Allocation. John Dodson. September 11, Dependence. John Dodson. Outline.
MFM Practitioner Module: Risk & Asset Allocation September 11, 2013 Before we define dependence, it is useful to define Random variables X and Y are independent iff For all x, y. In particular, F (X,Y
More informationBasic Concepts in Matrix Algebra
Basic Concepts in Matrix Algebra An column array of p elements is called a vector of dimension p and is written as x p 1 = x 1 x 2. x p. The transpose of the column vector x p 1 is row vector x = [x 1
More informationDef. The euclidian distance between two points x = (x 1,...,x p ) t and y = (y 1,...,y p ) t in the p-dimensional space R p is defined as
MAHALANOBIS DISTANCE Def. The euclidian distance between two points x = (x 1,...,x p ) t and y = (y 1,...,y p ) t in the p-dimensional space R p is defined as d E (x, y) = (x 1 y 1 ) 2 + +(x p y p ) 2
More informationWhitening and Coloring Transformations for Multivariate Gaussian Data. A Slecture for ECE 662 by Maliha Hossain
Whitening and Coloring Transformations for Multivariate Gaussian Data A Slecture for ECE 662 by Maliha Hossain Introduction This slecture discusses how to whiten data that is normally distributed. Data
More informationReview (Probability & Linear Algebra)
Review (Probability & Linear Algebra) CE-725 : Statistical Pattern Recognition Sharif University of Technology Spring 2013 M. Soleymani Outline Axioms of probability theory Conditional probability, Joint
More information(y 1, y 2 ) = 12 y3 1e y 1 y 2 /2, y 1 > 0, y 2 > 0 0, otherwise.
54 We are given the marginal pdfs of Y and Y You should note that Y gamma(4, Y exponential( E(Y = 4, V (Y = 4, E(Y =, and V (Y = 4 (a With U = Y Y, we have E(U = E(Y Y = E(Y E(Y = 4 = (b Because Y and
More informationReview. DS GA 1002 Statistical and Mathematical Models. Carlos Fernandez-Granda
Review DS GA 1002 Statistical and Mathematical Models http://www.cims.nyu.edu/~cfgranda/pages/dsga1002_fall16 Carlos Fernandez-Granda Probability and statistics Probability: Framework for dealing with
More informationNext is material on matrix rank. Please see the handout
B90.330 / C.005 NOTES for Wednesday 0.APR.7 Suppose that the model is β + ε, but ε does not have the desired variance matrix. Say that ε is normal, but Var(ε) σ W. The form of W is W w 0 0 0 0 0 0 w 0
More informationVector spaces. DS-GA 1013 / MATH-GA 2824 Optimization-based Data Analysis.
Vector spaces DS-GA 1013 / MATH-GA 2824 Optimization-based Data Analysis http://www.cims.nyu.edu/~cfgranda/pages/obda_fall17/index.html Carlos Fernandez-Granda Vector space Consists of: A set V A scalar
More informationExercise Sheet 1.
Exercise Sheet 1 You can download my lecture and exercise sheets at the address http://sami.hust.edu.vn/giang-vien/?name=huynt 1) Let A, B be sets. What does the statement "A is not a subset of B " mean?
More informationLecture Notes 1: Vector spaces
Optimization-based data analysis Fall 2017 Lecture Notes 1: Vector spaces In this chapter we review certain basic concepts of linear algebra, highlighting their application to signal processing. 1 Vector
More informationMultiple Random Variables
Multiple Random Variables This Version: July 30, 2015 Multiple Random Variables 2 Now we consider models with more than one r.v. These are called multivariate models For instance: height and weight An
More informationStat 5101 Lecture Notes
Stat 5101 Lecture Notes Charles J. Geyer Copyright 1998, 1999, 2000, 2001 by Charles J. Geyer May 7, 2001 ii Stat 5101 (Geyer) Course Notes Contents 1 Random Variables and Change of Variables 1 1.1 Random
More informationPrincipal Components Theory Notes
Principal Components Theory Notes Charles J. Geyer August 29, 2007 1 Introduction These are class notes for Stat 5601 (nonparametrics) taught at the University of Minnesota, Spring 2006. This not a theory
More informationSample Geometry. Edps/Soc 584, Psych 594. Carolyn J. Anderson
Sample Geometry Edps/Soc 584, Psych 594 Carolyn J. Anderson Department of Educational Psychology I L L I N O I S university of illinois at urbana-champaign c Board of Trustees, University of Illinois Spring
More informationLecture 21: Convergence of transformations and generating a random variable
Lecture 21: Convergence of transformations and generating a random variable If Z n converges to Z in some sense, we often need to check whether h(z n ) converges to h(z ) in the same sense. Continuous
More informationThis exam is closed book and closed notes. (You will have access to a copy of the Table of Common Distributions given in the back of the text.
TEST #3 STA 536 December, 00 Name: Please read the following directions. DO NOT TURN THE PAGE UNTIL INSTRUCTED TO DO SO Directions This exam is closed book and closed notes. You will have access to a copy
More informationMath 180B, Winter Notes on covariance and the bivariate normal distribution
Math 180B Winter 015 Notes on covariance and the bivariate normal distribution 1 Covariance If and are random variables with finite variances then their covariance is the quantity 11 Cov := E[ µ ] where
More information2. Matrix Algebra and Random Vectors
2. Matrix Algebra and Random Vectors 2.1 Introduction Multivariate data can be conveniently display as array of numbers. In general, a rectangular array of numbers with, for instance, n rows and p columns
More informationMultiple Integrals and Probability Notes for Math 2605
Multiple Integrals and Probability Notes for Math 605 A. D. Andrew November 00. Introduction In these brief notes we introduce some ideas from probability, and relate them to multiple integration. Thus
More informationMath 164-1: Optimization Instructor: Alpár R. Mészáros
Math 164-1: Optimization Instructor: Alpár R. Mészáros Final Exam, June 9, 2016 Name (use a pen): Student ID (use a pen): Signature (use a pen): Rules: Duration of the exam: 180 minutes. By writing your
More informationwhich has a check digit of 9. This is consistent with the first nine digits of the ISBN, since
vector Then the check digit c is computed using the following procedure: 1. Form the dot product. 2. Divide by 11, thereby producing a remainder c that is an integer between 0 and 10, inclusive. The check
More informationLecture 21 Theory of the Lasso II
Lecture 21 Theory of the Lasso II 02 December 2015 Taylor B. Arnold Yale Statistics STAT 312/612 Class Notes Midterm II - Available now, due next Monday Problem Set 7 - Available now, due December 11th
More informationMultivariate Random Variable
Multivariate Random Variable Author: Author: Andrés Hincapié and Linyi Cao This Version: August 7, 2016 Multivariate Random Variable 3 Now we consider models with more than one r.v. These are called multivariate
More information6-1. Canonical Correlation Analysis
6-1. Canonical Correlation Analysis Canonical Correlatin analysis focuses on the correlation between a linear combination of the variable in one set and a linear combination of the variables in another
More informationMATH c UNIVERSITY OF LEEDS Examination for the Module MATH2715 (January 2015) STATISTICAL METHODS. Time allowed: 2 hours
MATH2750 This question paper consists of 8 printed pages, each of which is identified by the reference MATH275. All calculators must carry an approval sticker issued by the School of Mathematics. c UNIVERSITY
More information8 Eigenvectors and the Anisotropic Multivariate Gaussian Distribution
Eigenvectors and the Anisotropic Multivariate Gaussian Distribution Eigenvectors and the Anisotropic Multivariate Gaussian Distribution EIGENVECTORS [I don t know if you were properly taught about eigenvectors
More informationMinimum Error Rate Classification
Minimum Error Rate Classification Dr. K.Vijayarekha Associate Dean School of Electrical and Electronics Engineering SASTRA University, Thanjavur-613 401 Table of Contents 1.Minimum Error Rate Classification...
More informationChapter 5. The multivariate normal distribution. Probability Theory. Linear transformations. The mean vector and the covariance matrix
Probability Theory Linear transformations A transformation is said to be linear if every single function in the transformation is a linear combination. Chapter 5 The multivariate normal distribution When
More informationThe Multivariate Normal Distribution
The Multivariate Normal Distribution Paul Johnson June, 3 Introduction A one dimensional Normal variable should be very familiar to students who have completed one course in statistics. The multivariate
More informationInner product spaces. Layers of structure:
Inner product spaces Layers of structure: vector space normed linear space inner product space The abstract definition of an inner product, which we will see very shortly, is simple (and by itself is pretty
More informationThe Multivariate Gaussian Distribution [DRAFT]
The Multivariate Gaussian Distribution DRAFT David S. Rosenberg Abstract This is a collection of a few key and standard results about multivariate Gaussian distributions. I have not included many proofs,
More informationLinear Analysis Lecture 5
Linear Analysis Lecture 5 Inner Products and V Let dim V < with inner product,. Choose a basis B and let v, w V have coordinates in F n given by x 1. x n and y 1. y n, respectively. Let A F n n be the
More informationBivariate distributions
Bivariate distributions 3 th October 017 lecture based on Hogg Tanis Zimmerman: Probability and Statistical Inference (9th ed.) Bivariate Distributions of the Discrete Type The Correlation Coefficient
More informationx. Figure 1: Examples of univariate Gaussian pdfs N (x; µ, σ 2 ).
.8.6 µ =, σ = 1 µ = 1, σ = 1 / µ =, σ =.. 3 1 1 3 x Figure 1: Examples of univariate Gaussian pdfs N (x; µ, σ ). The Gaussian distribution Probably the most-important distribution in all of statistics
More informationMath 180B Problem Set 3
Math 180B Problem Set 3 Problem 1. (Exercise 3.1.2) Solution. By the definition of conditional probabilities we have Pr{X 2 = 1, X 3 = 1 X 1 = 0} = Pr{X 3 = 1 X 2 = 1, X 1 = 0} Pr{X 2 = 1 X 1 = 0} = P
More informationSTAT 7032 Probability Spring Wlodek Bryc
STAT 7032 Probability Spring 2018 Wlodek Bryc Created: Friday, Jan 2, 2014 Revised for Spring 2018 Printed: January 9, 2018 File: Grad-Prob-2018.TEX Department of Mathematical Sciences, University of Cincinnati,
More informationMAS223 Statistical Inference and Modelling Exercises
MAS223 Statistical Inference and Modelling Exercises The exercises are grouped into sections, corresponding to chapters of the lecture notes Within each section exercises are divided into warm-up questions,
More informationLinear Algebra Massoud Malek
CSUEB Linear Algebra Massoud Malek Inner Product and Normed Space In all that follows, the n n identity matrix is denoted by I n, the n n zero matrix by Z n, and the zero vector by θ n An inner product
More informationEuclidean Space. This is a brief review of some basic concepts that I hope will already be familiar to you.
Euclidean Space This is a brief review of some basic concepts that I hope will already be familiar to you. There are three sets of numbers that will be especially important to us: The set of all real numbers,
More informationA Peak to the World of Multivariate Statistical Analysis
A Peak to the World of Multivariate Statistical Analysis Real Contents Real Real Real Why is it important to know a bit about the theory behind the methods? Real 5 10 15 20 Real 10 15 20 Figure: Multivariate
More informationECON 4117/5111 Mathematical Economics Fall 2005
Test 1 September 30, 2005 Read Me: Please write your answers on the answer book provided. Use the rightside pages for formal answers and the left-side pages for your rough work. Do not forget to put your
More informationMAT 419 Lecture Notes Transcribed by Eowyn Cenek 6/1/2012
(Homework 1: Chapter 1: Exercises 1-7, 9, 11, 19, due Monday June 11th See also the course website for lectures, assignments, etc) Note: today s lecture is primarily about definitions Lots of definitions
More informationLecture Note 1: Probability Theory and Statistics
Univ. of Michigan - NAME 568/EECS 568/ROB 530 Winter 2018 Lecture Note 1: Probability Theory and Statistics Lecturer: Maani Ghaffari Jadidi Date: April 6, 2018 For this and all future notes, if you would
More informationCovariance. Lecture 20: Covariance / Correlation & General Bivariate Normal. Covariance, cont. Properties of Covariance
Covariance Lecture 0: Covariance / Correlation & General Bivariate Normal Sta30 / Mth 30 We have previously discussed Covariance in relation to the variance of the sum of two random variables Review Lecture
More informationLecture 5. Max-cut, Expansion and Grothendieck s Inequality
CS369H: Hierarchies of Integer Programming Relaxations Spring 2016-2017 Lecture 5. Max-cut, Expansion and Grothendieck s Inequality Professor Moses Charikar Scribes: Kiran Shiragur Overview Here we derive
More informationFunctional Analysis Exercise Class
Functional Analysis Exercise Class Week 9 November 13 November Deadline to hand in the homeworks: your exercise class on week 16 November 20 November Exercises (1) Show that if T B(X, Y ) and S B(Y, Z)
More informationBREVET BLANC N 1 JANUARY 2012
Exercise. (5 pts) duration: h Presentation and explanations (4 points) Numerical Activities Consider the figure opposite, which is made up of two squares.. a) Calculate the area A of the white part. b)
More informationApplied Linear Algebra in Geoscience Using MATLAB
Applied Linear Algebra in Geoscience Using MATLAB Contents Getting Started Creating Arrays Mathematical Operations with Arrays Using Script Files and Managing Data Two-Dimensional Plots Programming in
More informationHomework sheet 4: EIGENVALUES AND EIGENVECTORS. DIAGONALIZATION (with solutions) Year ? Why or why not? 6 9
Bachelor in Statistics and Business Universidad Carlos III de Madrid Mathematical Methods II María Barbero Liñán Homework sheet 4: EIGENVALUES AND EIGENVECTORS DIAGONALIZATION (with solutions) Year - Is
More informationECON 5111 Mathematical Economics
Test 1 October 1, 2010 1. Construct a truth table for the following statement: [p (p q)] q. 2. A prime number is a natural number that is divisible by 1 and itself only. Let P be the set of all prime numbers
More informationChapter 5 continued. Chapter 5 sections
Chapter 5 sections Discrete univariate distributions: 5.2 Bernoulli and Binomial distributions Just skim 5.3 Hypergeometric distributions 5.4 Poisson distributions Just skim 5.5 Negative Binomial distributions
More informationVAR Model. (k-variate) VAR(p) model (in the Reduced Form): Y t-2. Y t-1 = A + B 1. Y t + B 2. Y t-p. + ε t. + + B p. where:
VAR Model (k-variate VAR(p model (in the Reduced Form: where: Y t = A + B 1 Y t-1 + B 2 Y t-2 + + B p Y t-p + ε t Y t = (y 1t, y 2t,, y kt : a (k x 1 vector of time series variables A: a (k x 1 vector
More informationSTAT 414: Introduction to Probability Theory
STAT 414: Introduction to Probability Theory Spring 2016; Homework Assignments Latest updated on April 29, 2016 HW1 (Due on Jan. 21) Chapter 1 Problems 1, 8, 9, 10, 11, 18, 19, 26, 28, 30 Theoretical Exercises
More informationImmerse Metric Space Homework
Immerse Metric Space Homework (Exercises -2). In R n, define d(x, y) = x y +... + x n y n. Show that d is a metric that induces the usual topology. Sketch the basis elements when n = 2. Solution: Steps
More informationDefinitions and Properties of R N
Definitions and Properties of R N R N as a set As a set R n is simply the set of all ordered n-tuples (x 1,, x N ), called vectors. We usually denote the vector (x 1,, x N ), (y 1,, y N ), by x, y, or
More informationConcentration Ellipsoids
Concentration Ellipsoids ECE275A Lecture Supplement Fall 2008 Kenneth Kreutz Delgado Electrical and Computer Engineering Jacobs School of Engineering University of California, San Diego VERSION LSECE275CE
More informationNotes on Linear Algebra and Matrix Theory
Massimo Franceschet featuring Enrico Bozzo Scalar product The scalar product (a.k.a. dot product or inner product) of two real vectors x = (x 1,..., x n ) and y = (y 1,..., y n ) is not a vector but a
More informationMore than one variable
Chapter More than one variable.1 Bivariate discrete distributions Suppose that the r.v. s X and Y are discrete and take on the values x j and y j, j 1, respectively. Then the joint p.d.f. of X and Y, to
More informationLECTURE 2 LINEAR REGRESSION MODEL AND OLS
SEPTEMBER 29, 2014 LECTURE 2 LINEAR REGRESSION MODEL AND OLS Definitions A common question in econometrics is to study the effect of one group of variables X i, usually called the regressors, on another
More informationA Tutorial on Data Reduction. Principal Component Analysis Theoretical Discussion. By Shireen Elhabian and Aly Farag
A Tutorial on Data Reduction Principal Component Analysis Theoretical Discussion By Shireen Elhabian and Aly Farag University of Louisville, CVIP Lab November 2008 PCA PCA is A backbone of modern data
More informationSTAT 418: Probability and Stochastic Processes
STAT 418: Probability and Stochastic Processes Spring 2016; Homework Assignments Latest updated on April 29, 2016 HW1 (Due on Jan. 21) Chapter 1 Problems 1, 8, 9, 10, 11, 18, 19, 26, 28, 30 Theoretical
More informationAlgebra 1 Correlation of the ALEKS course Algebra 1 to the Washington Algebra 1 Standards
Algebra 1 Correlation of the ALEKS course Algebra 1 to the Washington Algebra 1 Standards A1.1: Core Content: Solving Problems A1.1.A: Select and justify functions and equations to model and solve problems.
More informationCommon-Knowledge / Cheat Sheet
CSE 521: Design and Analysis of Algorithms I Fall 2018 Common-Knowledge / Cheat Sheet 1 Randomized Algorithm Expectation: For a random variable X with domain, the discrete set S, E [X] = s S P [X = s]
More information01 Probability Theory and Statistics Review
NAVARCH/EECS 568, ROB 530 - Winter 2018 01 Probability Theory and Statistics Review Maani Ghaffari January 08, 2018 Last Time: Bayes Filters Given: Stream of observations z 1:t and action data u 1:t Sensor/measurement
More informationCh. 5 Joint Probability Distributions and Random Samples
Ch. 5 Joint Probability Distributions and Random Samples 5. 1 Jointly Distributed Random Variables In chapters 3 and 4, we learned about probability distributions for a single random variable. However,
More informationLecture 11. Multivariate Normal theory
10. Lecture 11. Multivariate Normal theory Lecture 11. Multivariate Normal theory 1 (1 1) 11. Multivariate Normal theory 11.1. Properties of means and covariances of vectors Properties of means and covariances
More informationDesigning Information Devices and Systems I Fall 2018 Lecture Notes Note 21
EECS 16A Designing Information Devices and Systems I Fall 2018 Lecture Notes Note 21 21.1 Module Goals In this module, we introduce a family of ideas that are connected to optimization and machine learning,
More informationLecture 14: Multivariate mgf s and chf s
Lecture 14: Multivariate mgf s and chf s Multivariate mgf and chf For an n-dimensional random vector X, its mgf is defined as M X (t) = E(e t X ), t R n and its chf is defined as φ X (t) = E(e ıt X ),
More informationLecture 2: Linear Algebra Review
EE 227A: Convex Optimization and Applications January 19 Lecture 2: Linear Algebra Review Lecturer: Mert Pilanci Reading assignment: Appendix C of BV. Sections 2-6 of the web textbook 1 2.1 Vectors 2.1.1
More informationLecture 12: Diagonalization
Lecture : Diagonalization A square matrix D is called diagonal if all but diagonal entries are zero: a a D a n 5 n n. () Diagonal matrices are the simplest matrices that are basically equivalent to vectors
More informationMultivariate Gaussian Distribution. Auxiliary notes for Time Series Analysis SF2943. Spring 2013
Multivariate Gaussian Distribution Auxiliary notes for Time Series Analysis SF2943 Spring 203 Timo Koski Department of Mathematics KTH Royal Institute of Technology, Stockholm 2 Chapter Gaussian Vectors.
More informationTAMS39 Lecture 2 Multivariate normal distribution
TAMS39 Lecture 2 Multivariate normal distribution Martin Singull Department of Mathematics Mathematical Statistics Linköping University, Sweden Content Lecture Random vectors Multivariate normal distribution
More informationMath 302 Outcome Statements Winter 2013
Math 302 Outcome Statements Winter 2013 1 Rectangular Space Coordinates; Vectors in the Three-Dimensional Space (a) Cartesian coordinates of a point (b) sphere (c) symmetry about a point, a line, and a
More informationGeometry and Motion, MA 134 Week 1
Geometry and Motion, MA 134 Week 1 Mario J. Micallef Spring, 2007 Warning. These handouts are not intended to be complete lecture notes. They should be supplemented by your own notes and, importantly,
More informationSTAT 7032 Probability. Wlodek Bryc
STAT 7032 Probability Wlodek Bryc Revised for Spring 2019 Printed: January 14, 2019 File: Grad-Prob-2019.TEX Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH 45221 E-mail address:
More informationArchive of past papers, solutions and homeworks for. MATH 224, Linear Algebra 2, Spring 2013, Laurence Barker
Archive of past papers, solutions and homeworks for MATH 224, Linear Algebra 2, Spring 213, Laurence Barker version: 4 June 213 Source file: archfall99.tex page 2: Homeworks. page 3: Quizzes. page 4: Midterm
More informationMATH 240 Spring, Chapter 1: Linear Equations and Matrices
MATH 240 Spring, 2006 Chapter Summaries for Kolman / Hill, Elementary Linear Algebra, 8th Ed. Sections 1.1 1.6, 2.1 2.2, 3.2 3.8, 4.3 4.5, 5.1 5.3, 5.5, 6.1 6.5, 7.1 7.2, 7.4 DEFINITIONS Chapter 1: Linear
More informationMath 164-1: Optimization Instructor: Alpár R. Mészáros
Math 164-1: Optimization Instructor: Alpár R. Mészáros First Midterm, April 20, 2016 Name (use a pen): Student ID (use a pen): Signature (use a pen): Rules: Duration of the exam: 50 minutes. By writing
More informationVectors and Matrices Statistics with Vectors and Matrices
Vectors and Matrices Statistics with Vectors and Matrices Lecture 3 September 7, 005 Analysis Lecture #3-9/7/005 Slide 1 of 55 Today s Lecture Vectors and Matrices (Supplement A - augmented with SAS proc
More informationIntroduction and Preliminaries
Chapter 1 Introduction and Preliminaries This chapter serves two purposes. The first purpose is to prepare the readers for the more systematic development in later chapters of methods of real analysis
More informationProbability Lecture III (August, 2006)
robability Lecture III (August, 2006) 1 Some roperties of Random Vectors and Matrices We generalize univariate notions in this section. Definition 1 Let U = U ij k l, a matrix of random variables. Suppose
More informationLinear Algebra Review
January 29, 2013 Table of contents Metrics Metric Given a space X, then d : X X R + 0 and z in X if: d(x, y) = 0 is equivalent to x = y d(x, y) = d(y, x) d(x, y) d(x, z) + d(z, y) is a metric is for all
More informationSTT 843 Key to Homework 1 Spring 2018
STT 843 Key to Homework Spring 208 Due date: Feb 4, 208 42 (a Because σ = 2, σ 22 = and ρ 2 = 05, we have σ 2 = ρ 2 σ σ22 = 2/2 Then, the mean and covariance of the bivariate normal is µ = ( 0 2 and Σ
More informationMATH 54 - FINAL EXAM STUDY GUIDE
MATH 54 - FINAL EXAM STUDY GUIDE PEYAM RYAN TABRIZIAN This is the study guide for the final exam! It says what it does: to guide you with your studying for the exam! The terms in boldface are more important
More informationRandom Vectors 1. STA442/2101 Fall See last slide for copyright information. 1 / 30
Random Vectors 1 STA442/2101 Fall 2017 1 See last slide for copyright information. 1 / 30 Background Reading: Renscher and Schaalje s Linear models in statistics Chapter 3 on Random Vectors and Matrices
More informationLecture 3: Review of Linear Algebra
ECE 83 Fall 2 Statistical Signal Processing instructor: R Nowak Lecture 3: Review of Linear Algebra Very often in this course we will represent signals as vectors and operators (eg, filters, transforms,
More informationBusiness Statistics. Lecture 10: Correlation and Linear Regression
Business Statistics Lecture 10: Correlation and Linear Regression Scatterplot A scatterplot shows the relationship between two quantitative variables measured on the same individuals. It displays the Form
More informationLecture 3: Review of Linear Algebra
ECE 83 Fall 2 Statistical Signal Processing instructor: R Nowak, scribe: R Nowak Lecture 3: Review of Linear Algebra Very often in this course we will represent signals as vectors and operators (eg, filters,
More information