Bringing the SAS Legacy into
the R Environment
A Brief Talk for SAS and non-SAS
Data Professionals
www.directeffects.net
Georgette Asherman, 201 673-4301
What is SAS?
The company
SAS, Inc. positions itself now as the leader in
business analytics softwar

C O M P I L E D B Y S H R AV A N V A S I S H T H
A D VA N C E D D ATA A N A LY S I S
F O R P S Y C H O L I N G U I S T I C S : B AY E S I A N M E T H O D S
VA S I S H T H L A B L E C T U R E N O T E S
Contents
1
Introduction
11
2
Probability theory and pr

Data Mining
Assignment #4
Due: Th, Apr 18th, 2013; Total points: 40
1. (2x3=6 pts) This problem explores scaling, clustering, and one method of evaluating the quality of a
clustering. You will also examine some of the issues in k-means clustering, and som

Weighted Least Square
Regression
Definition
Each term in the weighted least squares
criterion includes an additional weight, that
determines how much each observation in
the data set influences the final parameter
estimates and it can be used with functio

Calculus Notes
l'Hpital's rule: For functionsx) and g(x), if limf(x) = limg(x) = 0,01
limf(x) = :limg(x)
X_C I_C
must exist.
Lemma 2.3.14 Let (11,02, .
lim,H00 an 2 (1. Then
I!
i:
2.: 2
XC
_ . m)
m
=t00, then lim x)
XC g(x)
lim (1+an> 2e.
nboo 'n
(n+1)n

We; Z Swlobe
Theorem 2.1.5 Let X have pdf fx(m) and let Y = g(X), where g is a monotone
function. Let X and 3 be dened by (2.1 7). Suppose that fx($) is continuous on X
and that g_1(y) has a continuous derivative on )7. Then the pdf on is given by
(2110)

0);) g; 3
158 MULTlPLri RANDOM VARIABLES Section 43
Theorem 4.3.2 IfX ~ Poisson(6l and Y N Poisson(/\) and X and Y are indepen-
dent. then X + Y m Poisson(6 + A)
If (X7 Y) is a continuous random vector with joint pdf fX,y(x, y), then the joint pdf
of (U,

Rules for the exam:
Show your work and thoughts fully. Do not expect credit for answers only. If you quote a
theorem or lemma, make clear exactly which one you are using.
Do not tear any pages out of your composition book for any reason. The only material

CHOICE OF A PRIOR DENSITY
In Example 4, the prior distributions of
p1 and p2 were given for 17 different
values.
Note that 0 p1, p2 1 and that there are
infinitely many possible values between
0 and 1.
When practically possible, we give prior
and posteri

Journal of Information Technology & Politics
ISSN: 1933-1681 (Print) 1933-169X (Online) Journal homepage: http:/www.tandfonline.com/loi/witp20
Political Facebook use: Campaign strategies used
in 2008 and 2012 presidential elections
Porismita Borah
To cite

Web Data Extractors 2016
A White Paper Link Compilation
By
Marcus P. Zillman, M.S., A.M.H.A.
Executive Director Virtual Private Library
zillman@virtualprivatelibrary.com
Extracting data from the World Wide Web (WWW) has become an important issue in the
la

Using R to explore sampling distributions
This document describes some scripts I have prepared (for one-dimensional statistics) to help you start
that. Do some minimal editing to these scripts to change the population distribution or the statistic of
inte

University of Texas at Austin
Department of Educational Psychology
EDP 384Hierarchical Linear Modeling
EDP 384Hierarchical Linear Modeling
Fall 2014
Instructor: Keenan Pituch, Ph.D.
Meeting Times: T, TH: 12:30 2:00
Office:
SZB 538C
Meeting Rooms: SZB 432

INF385C : Human-Computer Interaction Instructor: Dr. Jacek Gwizdka
Course Schedule (subject to change) Fall 2014
# Date
Topic
Reading Assignment
In class activity
(before class on that
day, except %).
Introduct

Bayesian Statistical Methods
SSC384.7
Fall 2012
Instructor: Dr. Maggie Myers
myers@cs.utexas.edu
Office hours: : T 11-12, W 2-3, TH 3:30-4:30, extra hours and appointment
Office: Aces 2.112
Phone: 471-9533
TA: Joey Frazee
jfrazee@mail.utexas.edu
Text: Gel

Information
City U Summer School in Social Science Research:
Big(ger) Data Research
Schedule
Programme
Venue: M5055, 5/F, Run Run Shaw Creative Media Centre (CMC)
Registration on 5/F
15/6/2015 (Monday)
08:45-09:00
Registration
1. Welcoming Dinner Arrangem