# Statistics AGAIN? Descriptives

Size: px
Start display at page:

Transcription

1 Cal State Northrdge Ψ427 Andrew Answorth PhD Statstcs AGAIN? What do we want to do wth statstcs? Organze and Descrbe patterns n data Takng ncomprehensble data and convertng t to: Tables that summarze the data Graphs Extract (.e. INFER) meanng from data Infer POPULATION values from SAMPLES Hypothess Testng Groups Hypothess Testng Relaton/Predcton Psy Cal State Northrdge 2 Descrptves Dsorganzed Data Comedy 7 Suspense 8 Comedy 7 Suspense 7 Drama 8 Horror 7 Drama 5 Comedy 6 Horror 8 Comedy 5 Drama 3 Drama 3 Suspense 7 Horror 8 Comedy 6 Suspense 6 Horror 8 Comedy 6 Drama 7 Horror 9 Drama 5 Horror 9 Drama 6 Suspense 4 Drama 5 Horror 7 Suspense 3 Suspense 4 Horror 7 Suspense 5 Horror 10 Suspense 5 Horror 9 Suspense 6 Comedy 6 Drama 8 Comedy 7 Comedy 5 Comedy 4 Drama 4 Psy Cal State Northrdge 3 1

2 Descrptves Reducng and Descrbng Data Genre Average Ratng Comedy 5.9 Drama 5.4 Horror 8.2 Suspense 5.5 Psy Cal State Northrdge 4 Descrptves Dsplayng Data Ratng of Move Genre Enjoyment Average Ratng Comedy Drama Horror Suspense Genre Psy Cal State Northrdge 5 Inferental Inferental statstcs: Is a set of procedures to nfer nformaton about a populaton based upon characterstcs from samples. Samples are taken from Populatons Sample Statstcs are used to nfer populaton parameters Psy Cal State Northrdge 6 2

3 Inferental Populatons the complete set of people, anmals, events or objects that share a common characterstc A samples some subset or subsets, selected from the populaton. representatve smple random sample. Psy Cal State Northrdge 7 Defnton Populaton Sample The group (people, thngs, A subset of the anmals, etc.) you are populaton; used as a ntendng to measure or representatve of the study; they share some populaton common characterstc Sze Large to Theoretcally Infnte Substantally Smaller than the populaton (e.g. 1 to (populaton - 1)) Descrptve Characterstcs Parameters Statstcs Symbols Greek Latn Mean µ X Standard Devaton σ s or SD Psy Cal State Northrdge 8 Inferental 3.7 Does the number of hours 3.6 students study per day 3.5 affect the grade they are 3.4 lkely to receve n statstcs (Ψ320)? 3.2 GPA hr per day (n=15) hrs 5 hrs per day per day (n=15) (n=15) hours of study per day Psy Cal State Northrdge 9 3

4 Inferental Sometmes manpulaton s not possble Is predcton possble? Can a relatonshp be establshed? E.g., number of cgarettes smoked by per and the lkelhood of gettng lung cancer, The level of chld abuse n the home and the severty of later psychatrc problems. Use of the death penalty and the level of crme. Psy Cal State Northrdge 10 Inferental Measured constructs can be assessed for co-relaton (where the coeffcent of correlaton vares between -1 to +1) Regresson analyss can be used to assess whether a measured construct predcts the values on another measured construct (or multple) (e.g., the level of crme gven the level of death penalty usage). Psy Cal State Northrdge 11 Measurement Statstcal analyses depend upon the measurement characterstcs of the data. Measurement s a process of assgnng numbers to constructs followng a set of rules. We normally measure varables nto one of four dfferent levels of measurement: Nomnal Ordnal Interval Rato Psy Cal State Northrdge 12 4

5 Ordnal Measurement Where Numbers Representatve Relatve Sze Only Contans 2 peces of nformaton B C D SIZE Psy Cal State Northrdge 13 Interval Measurement: Where Equal Dfferences Between Numbers Represent Equal Dfferences n Sze B C D Numbers representng Sze Dff n numbers 2-1=1 3-2=1 Dff n sze Sze C Sze B =Sze X Sze D Sze C = Sze X SIZE Psy Cal State Northrdge 14 Psy Cal State Northrdge 15 5

6 Psy Cal State Northrdge 16 Measurement Rato Scale Measurement In rato scale measurement there are four knds of nformaton conveyed by the numbers assgned to represent a varable: Everythng Interval Measurement Contans Plus A meanngful 0-pont and therefore meanngful ratos among measurements. Psy Cal State Northrdge 17 True Zero pont Psy Cal State Northrdge 18 6

7 Measurement Rato Scale Measurement If we have a true rato scale, where 0 represents an a complete absence of the varable n queston, then we form a meanngful rato among the scale values such as: 4 = 2 2 However, f 0 s not a true absence of the varable, then the rato 4/2 = 2 s not meanngful. Psy Cal State Northrdge 19 Percentles and Percentle Ranks A percentles the score at whch a specfed percentage of scores n a dstrbuton fall below To say a score 53 s n the 75th percentle s to say that 75% of all scores are less than 53 The percentle rankof a score ndcates the percentage of scores n the dstrbuton that fall at or below that score. Thus, for example, to say that the percentle rank of 53 s 75, s to say that 75% of the scores on the exam are less than 53. Psy Cal State Northrdge 20 Percentle Scores whch dvde dstrbutons nto specfc proportons Percentles = hundredths P1, P2, P3, P97, P98, P99 Quartles = quarters Q1, Q2, Q3 Decles = tenths D1, D2, D3, D4, D5, D6, D7, D8, D9 Percentles are the SCORES Psy Cal State Northrdge 21 7

8 Percentle Rank What percent of the scores fall below a partcular score? ( Rank.5) PR = 100 N Percentle Ranks are the Ranks not the scores Psy Cal State Northrdge 22 Example: Percentle Rank Rankng no tes just number them Score: Rank: Rankng wth tes -assgn mdpont to tes Score: Rank: Psy Cal State Northrdge 23 Step 1 Step 2 Step 3 Step 4 Assgn Mdpont to Tes Percentle Rank (Apply Formula) Data Order Number Steps to Calculatng Percentle Ranks Example: ( Rank.5) PR N (4.5) 100 = = 100 = Psy Cal State Northrdge 24 8

9 Percentle X = ( p)( n + 1) P Where X P s the score at the desred percentle, p s the desred percentle (a number between 0 and 1) and n s the number of scores) If the number s an nteger, than the desred percentle s that number If the number s not an nteger than you can ether round or nterpolate; for ths class we ll just round (round up when p s below.50 and down when p s above.50) Psy Cal State Northrdge 25 Percentle Apply the formula X = ( p)( n + 1) P 1. You ll get a number lke 7.5 (thnk of t as place1.proporton) 2. Start wth the value ndcated by place1 (e.g. 7.5, start wth the valuen the 7 th place) 3. Fnd place2 whch s the next hghest placenumber (e.g. the 8 th place) and subtract the valuen place1 from the value n place2, ths dstance1 4. Multple the proporton number by the dstance1 value, ths s dstance2 5. Add dstance2 to the value n place1 and that s the nterpolated value Psy Cal State Northrdge 26 Example: Percentle Example 1: 25 th percentle: {1, 4, 9, 16, 25, 36, 49, 64, 81} X 25 = (.25)(9+1) = 2.5 place1= 2, proporton =.5 Value n place1= 4 Value n place2 = 9 dstance1 = 9 4 = 5 dstance2 = 5 *.5 = 2.5 Interpolated value = = s the 25 th percentle Psy Cal State Northrdge 27 9

10 Example: Percentle Example 2: 75 th percentle {1, 4, 9, 16, 25, 36, 49, 64, 81} X 75 = (.75)(9+1) = 7.5 place1= 7, proporton =.5 Value n place1= 49 Value n place2 = 64 dstance1 = = 15 dstance2 = 15 *.5 = 7.5 Interpolated value = = s the 75 th percentle Psy Cal State Northrdge 28 Quartles To calculate Quartles you smply fnd the scores the correspond to the 25, 50 and 75 percentles. Q 1 = P 25, Q 2 = P 50, Q 3 = P 75 Psy Cal State Northrdge 29 Reducng Dstrbutons Regardless of numbers of scores, dstrbutons can be descrbed wth three peces of nfo: Central Tendency Varablty Shape (Normal, Skewed, etc.) Psy Cal State Northrdge 30 10

11 Measures of Central Tendency Measure Defnton Mode Medan Mean Most frequent value Level of Measurement Dsadvantage nom., ord., nt./rat. Mddle value ord., nt./rat. Arthmetc average nt./rat. Crude Only two ponts contrbute Affected by skew Psy Cal State Northrdge 31 The Mean Only used for nterval & rato data. Mean M = X = = 1 = X Major advantages: The sample value s a very good estmate of the populaton value. n n X Psy Cal State Northrdge 32 Reducng Dstrbutons Regardless of numbers of scores, dstrbutons can be descrbed wth three peces of nfo: Central Tendency Varablty Shape (Normal, Skewed, etc.) Psy Cal State Northrdge 33 11

12 How do scores spread out? Varablty Tell us how far scores spread out Tells us how the degree to whch scores devate from the central tendency Psy Cal State Northrdge 34 How are these dfferent? Mean = 10 Mean = 10 Psy Cal State Northrdge 35 Measure of Varablty Measure Defnton Related to: Range Largest - Smallest Mode Interquartle Range X 75 - X 25 Sem-Interquartle Range (X 75 - X 25)/2 Medan Average Absolute Devaton X X N Varance N ( X ) 2 X = 1 N 1 Mean Standard Devaton ( X ) 2 X N = 1 N 1 Psy Cal State Northrdge 36 12

13 The Range The smplest measure of varablty Range (R) = X hghest X lowest Advantage Easy to Calculate Dsadvantages Lke Medan, only dependent on two scores unstable {0, 8, 9, 9, 11, 53} Range = 53 {0, 8, 9, 9, 11, 11} Range = 11 Does not reflect all scores Psy Cal State Northrdge 37 Varablty: IQR InterquartleRange = P 75 P 25 or Q 3 Q 1 Ths helps to get a range that s not nfluenced by the extreme hgh and low scores Where the range s the spread across 100% of the scores, the IQR s the spread across the mddle 50% Psy Cal State Northrdge 38 Varablty: SIQR Sem-nterquartle range =(P 75 P 25 )/2 or (Q 3 Q 1 )/2 IQR/2 Ths s the spread of the mddle 25% of the data The average dstance of Q1 and Q3 from the medan Better for skewed data Psy Cal State Northrdge 39 13

14 Varablty: SIQR Sem-Interquartle range Q 1 Q 2 Q 3 Q 1 Q 2 Q 3 Psy Cal State Northrdge 40 Varance The average squareddstance of each score from the mean Also known as the mean square Varance of a sample: s 2 Varance of a populaton: σ 2 Psy Cal State Northrdge 41 Varance When calculated for a sample s 2 = ( X ) 2 X N 1 When calculated for the entre populaton σ = 2 ( X X ) 2 N Psy Cal State Northrdge 42 14

15 Standard Devaton Varance s n squared unts What about regular old unts Standard Devaton = Square root of the varance s = ( X ) 2 X N 1 Psy Cal State Northrdge 43 Standard Devaton Uses measure of central tendency (.e. mean) Uses all data ponts Has a specal relatonshp wth the normal curve Can be used n further calculatons Standard Devaton of Sample = SDor s Standard Devaton of Populaton = σ Psy Cal State Northrdge 44 Why N-1? When usng a sample (whch we always do) we want a statstc that s the best estmate of the parameter ( X ) 2 X 2 ( X ) 2 X E = σ N 1 E = σ N 1 Psy Cal State Northrdge 45 15

16 Degrees of Freedom Usually referred to as df Number of observatons mnus the number of restrctons =10-4 free spaces =10-3 free spaces =10-2 free spaces =10 Last space s not free!! Only 3 dfs. Psy Cal State Northrdge 46 Reducng Dstrbutons Regardless of numbers of scores, dstrbutons can be descrbed wth three peces of nfo: Central Tendency Varablty Shape (Normal, Skewed, etc.) Psy Cal State Northrdge 47 T erm s tha t D escrb e D strbu to ns T e rm F eatu res E xa m p le left sde s m rror "S ym m etrc" m age of rght sde "P ostvely skew ed " rght tal s longer then the left "N egatvely skew ed " left tal s longer than the rght "U nm odal" one hghest po nt "B m odal" tw o hgh ponts "N orm al" unm odal, sym m etrc, asym p totc Psy Cal State Northrdge 48 16

17 Psy Cal State Northrdge 49 Normal Dstrbuton f(x) Example: The Mean = 100 and the Standard Devaton = 20 Psy Cal State Northrdge 50 Normal Dstrbuton (Characterstcs) Horzontal Axs = possble X values Vertcal Axs = densty (.e. f(x)related to probablty or proporton) Defned as ( X µ ) 2σ f ( X ) = ( e) σ 2π 1 f ( X ) = *( ) ( s) 2*( ) 2 2 ( X X ) 2s The dstrbuton reles on only the meanand s Psy Cal State Northrdge 51 17

18 Normal Dstrbuton (Characterstcs) Bell shaped, symmetrcal, unmodal Mean, medan, mode all equal No real dstrbuton s perfectly normal But, many dstrbutons are approxmately normal, so normal curve statstcs apply Normal curve statstcs underle procedures n most nferental statstcs. Psy Cal State Northrdge 52 Normal Dstrbuton f(x) µ 4sd µ 3sd µ 2sd µ 1sd µ µ + 1sd µ + 2sd µ + 3sd µ + 4sd Psy Cal State Northrdge 53 The standard normal dstrbuton A normal dstrbuton wth the added propertes that the mean = 0 and the s = 1 Convertng a dstrbuton nto a standard normal means convertng raw scores nto Z-scores Psy Cal State Northrdge 54 18

19 Z-Score Formula Raw score Z-score X X score - mean Z = = s standard devaton Z-score Raw score X = Z ( s) + X Psy Cal State Northrdge 55 Propertes of Z-Scores Z-score ndcates how many SD s a score falls above or below the mean. Postve z-scores are above the mean. Negatve z-scores are below the mean. Area under curve probablty Z s contnuous so can only compute probablty for range of values Psy Cal State Northrdge 56 Propertes of Z-Scores Most z-scores fall between -3 and +3 because scores beyond 3sd from the mean Z-scores are standardzed scores allows for easy comparson of dstrbutons Psy Cal State Northrdge 57 19

20 The standard normal dstrbuton Rough estmates of the SND (.e. Z-scores): Psy Cal State Northrdge 58 Have Need Chart When rough estmatng sn t enough X X Z = s Z-Table Raw Score Z-score Area under Dstrbuton X = Z ( s) + X Z-table Psy Cal State Northrdge 59 What about negatve Z values? Snce the normal curve s symmetrc, areas beyond, between, and below postve z scores are dentcal to areas beyond, between, and below negatve z scores. There s no such thng as negatve area! Psy Cal State Northrdge 60 20

21 Norms and Norm-Referenced Tests Norm -statstcal representatons of a populaton (e.g. mean, medan). Norm-referenced test (NRT) Compares an ndvdual's results on the test wth the preestablshed norm Made to compare test-takers to each other I.E. -The Normal Curve Psy Cal State Northrdge 61 Norms and Norm-Referenced Tests Normally rather than testng an entre populaton, the norms are nferred from a representatve sample or group (nferental stats revsted). Norms allow for a better understandng of how an ndvdual's scores compare wth the group wth whch they are beng compared Examples: WAIS, SAT, MMPI, Graduate Record Examnaton (GRE) Psy Cal State Northrdge 62 Crteron-Referenced Tests Crteron-referenced tests (CRTs) - ntended to measure how well a person has mastered a specfc knowledge set or skll Cutscore pont at whch an examnee passes f ther score exceeds that pont; can be decded by a panel or by a sngle nstructor Crteron the doman n whch the test s desgned to assess Psy Cal State Northrdge 63 21

### Following a musical performance from a partially specified score.

Followng a muscal performance from a partally specfed score. Bryan Pardo and Wllam P. Brmngham Artfcal Intellgence Laboratory Electrcal Engneerng and Computer Scence Dept. and School of Musc The Unversty

### RIAM Local Centre Woodwind, Brass & Percussion Syllabus

8 RIAM Local Centre Woodwnd, Brass & Percusson Syllabus 2015-2018 AURAL REQUIREMENTS AND THEORETICAL QUESTIONS REVISED FOR ALL PRACTICAL SUBJECTS AURAL TESTS From Elementary to Grade V ths area s worth

### tj tj D... '4,... ::=~--lj c;;j _ ASPA: Automatic speech-pause analyzer* t> ,. "",. : : :::: :1'NTmAC' I

ASPA: Automatc speech-pause analyzer* D. GERVERt and G. DNELEY Unversty of Durham, Durham, England ASPA: The Programs Snce the actual detals of nterface samplng, dsk storage routnes, etc., wll depend upon

### System of Automatic Chinese Webpage Summarization Based on The Random Walk Algorithm of Dynamic Programming

Send Orders for Reprnts to reprnts@benthamscence.ae The Open Cybernetcs & Systemcs Journal, 205, 9, 35-322 35 Open Access System of Automatc Chnese Webpage Summarzaton Based on The Random Walk Algorthm

### Small Area Co-Modeling of Point Estimates and Their Variances for Domains in the Current Employment Statistics Survey

Small Area Co-Modelng of Pont Estmates and Ther Varances for Domans n the Current Employment Statstcs Survey Jule Gershunskaya, Terrance D. Savtsky U.S. Bureau of Labor Statstcs FCSM, March 2018 Dsclamer:

### A Comparative Analysis of Disk Scheduling Policies

A Comparatve Analyss of Dsk Schedulng Polces Toby J. Teorey and Tad B. Pnkerton Unversty of Wsconsn* Fve well-known schedulng polces for movable head dsks are compared usng the performance crtera of expected

### Novel Quantization Strategies for Linear Prediction with Guarantees

Smon S. Du* Ychong Xu* Yuan L Hongyang Zhang Aart Sngh Pulkt Grover Carnege Mellon Unversty, Pttsburgh, PA 15213, USA *: Contrbute equally. SSDU@CS.CMU.EDU YICHONGX@CS.CMU.EDU LIYUANCHRISTY@GMAIL.COM HONGYANZ@CS.CMU.EDU

### Technical Information

CHEMCUT Techncal Informaton CORPORATION Introducton The Chemcut CC8000 etcher has many new features desgned to reduce the cost of manufacturng and, just as mportantly, the cost of ownershp. Keepng the

### What is Statistics? 13.1 What is Statistics? Statistics

13.1 What is Statistics? What is Statistics? The collection of all outcomes, responses, measurements, or counts that are of interest. A portion or subset of the population. Statistics Is the science of

### Optimized PMU placement by combining topological approach and system dynamics aspects

Optmzed PU placement by combnng topologcal approach and system dynamcs aspects Jonas Prommetta, Jakob Schndler, Johann Jaeger Insttute of Electrcal Energy Systems Fredrch-Alexander-Unversty Erlangen-Nuremberg

### Decision Support by Interval SMART/SWING Incorporating. Imprecision into SMART and SWING Methods

Decson Support by Interval SMART/SWING Incorporatng Imprecson nto SMART and SWING Methods Abstract: Interval judgments are a way of handlng preferental and nformatonal mprecson n multcrtera decson analyss.

Provded by the author(s) and Unversty College Dubln Lbrary n accordance wth publsher polces., Please cte the publshed verson when avalable. tle Dynamc Complexty Scalng for Real-me H.264/AVC Vdeo Encodng

### SONG STRUCTURE IDENTIFICATION OF JAVANESE GAMELAN MUSIC BASED ON ANALYSIS OF PERIODICITY DISTRIBUTION

SOG STRUCTURE IDETIFICATIO OF JAVAESE GAMELA MUSIC BASED O AALYSIS OF PERIODICITY DISTRIBUTIO D. P. WULADARI, Y. K. SUPRAPTO, 3 M. H. PUROMO,,3 Insttut Teknolog Sepuluh opember, Department of Electrcal

### Lost on the Web: Does Web Distribution Stimulate or Depress Television Viewing?

Lost on the Web: Does Web Dstrbuton Stmulate or Depress Televson Vewng? Joel Waldfogel The Wharton School Unversty of Pennsylvana August 10, 2007 Prelmnary comments welcome Abstract In the past few years,

### Environmental Reviews. Cause-effect analysis for sustainable development policy

Envronmental Revews Cause-effect analyss for sustanable development polcy Journal: Envronmental Revews Manuscrpt ID er-2016-0109.r2 Manuscrpt Type: Revew Date Submtted by the Author: 24-Feb-2017 Complete

### Instructions for Contributors to the International Journal of Microwave and Wireless Technologies

Instructons for Contrbutors to the Internatonal Journal of Mcrowave and Wreless Technologes Frst A. Author 1, Second Author 1,2, Thrd Author 2 1 Cambrdge Unversty Press, Ednburgh Buldng, Shaftesbury Road,

### Modeling Form for On-line Following of Musical Performances

Modelng Form for On-lne Followng of Muscal Performances Bryan Pardo 1 and Wllam Brmngham 2 1 Computer Scence Department, Northwestern Unversty, 1890 Maple Ave, Evanston, IL 60201 2 Department of Math and

### QUICK START GUIDE v0.98

QUICK START GUIDE v0.98 QUICK HELP Q A 1 STEP BY STEP 3 GLOSSARY 2 A B C 1 INSTALLATION 1. Make sure that the hardware nstallaton s performed by a certfed vendor 2. Install OTOTRAK app from Apple s App

### Craig Webre, Sheriff Personnel Division/Law Enforcement Complex 1300 Lynn Street Thibodaux, Louisiana 70301

DATE OF APPLCATON: Craig Webre, Sheriff Personnel Division/Law Enforcement Complex 1300 Lynn Street Thibodaux, Louisiana 70301 N GENERAL EMAL ADDRESS: For Local Calls - (985) 532-4380 (985) 446-2255 (985)

### Why Take Notes? Use the Whiteboard Capture System

Why Take Notes? Use the Whteboard Capture System L-we He Zhengyou Zhang and Zcheng Lu September, 2002 Techncal Report MSR-TR-2002-89 Mcrosoft Research Mcrosoft Corporaton One Mcrosoft Way Redmond, WA 98052

### Failure Rate Analysis of Power Circuit Breaker in High Voltage Substation

T. Suwanasr, M. T. Hlang and C. Suwanasr / GMSAR Internatonal Journal 8 (2014) 1-6 Falure Rate Analyss of Power Crcut Breaker n Hgh Voltage Substaton Thanapong Suwanasr, May Thandar Hlang and Cattareeya

### THE IMPORTANCE OF ARM-SWING DURING FORWARD DIVE AND REVERSE DIVE ON SPRINGBOARD

THE MPORTANCE OF ARM-SWNG DURNG FORWARD DVE AND REVERSE DVE ON SPRNGBOARD Ken Yokoyama Laboratory of Bomechancs Faculty ofeducaton Kanazawa Unversty Kanazawa, Japan J unjro Nagano Department of Physcal

### arxiv: v1 [cs.cl] 12 Sep 2018

Powered by TCPDF (www.tcpdf.org) Neural Melody Composton from Lyrcs Hangbo Bao, Shaohan Huang 2, Furu We 2, Le Cu 2, Yu Wu 3, Chuanq Tan 3, Songhao Pao, Mng Zhou 2 School of Computer Scence, Harbn Insttute

### Analysis of Subscription Demand for Pay-TV

Analyss of Subscrpton Demand for Pay-TV Manabu Shshkura Researcher Insttute for Informaton and Communcatons Polcy 2-1-2 Kasumgasek, Chyoda-ku Tokyo 110-8926 Japan m-shshkura@soumu.go.jp Tel: 03-5253-5496

### Simple VBR Harmonic Broadcasting (SVHB)

mple VBR Harmonc Broadcastng (VHB) Hsang-Fu Yu ab, Hung-hang Yang a, Y-Mng hen c, -Mng Tseng a, and hen-y Kuo a a Dep. of omputer cence & Informaton Engneerng, atonal entral Unversty, Tawan b omputer enter,

### LOW-COMPLEXITY VIDEO ENCODER FOR SMART EYES BASED ON UNDERDETERMINED BLIND SIGNAL SEPARATION

LOW-COMPLEXITY VIDEO ENCODER FOR SMART EYES BASED ON UNDERDETERMINED BLIND SIGNAL SEPARATION Jng Lu, Fe Qao *, Zhjan Ou and Huazhong Yang Department of Electronc Engneerng, Tsnghua Unversty ABSTRACT Ths

### current activity shows on the top right corner in green. The steps appear in yellow

Browzwear Tutorals Tutoral ntroducton Ths tutoral leads you through the best practces of color ways operatons usng an llustrated step by step approach. Each slde shows the actual applcaton at the stage

### 9! VERY LARGE IN THEIR CONCERNS. AND THEREFORE, UH, i

340 WELL, alack PAJAMAS WAS A SOMEWHAT METAPHORCAL 2 TERM. MANY VETNAMESE PEASANTS TENDED TO WEAR 3 BLACK PAJAMAS, BUT WHAT AM REFERRNG TO S THAT 4 OUTSDE OF THE NORTH VETNAMESE UNTS AND ~OME OF 5 THE

### Hybrid Transcoding for QoS Adaptive Video-on-Demand Services

732 IEEE Transactons on Consumer Electroncs, Vol. 50, No. 2, MAY 2004 Hybrd Transcodng for QoS Adaptve Vdeo-on-Demand Servces Ilhoon Shn and Kern Koh Abstract Transcodng s a core technque that s used n

### Detecting Errors in Blood-Gas Measurement by Analysiswith Two Instruments

CLIN. CHEM. 33/4, 512-517 (1987) Detectng Errors n Blood-Gas Measurement by Analysswth Two Instruments LouIs F. Metzger, Wllam B. Stauffer, Ann V. Kruplnskl, Rchard P. MIIlman,3 George S. Cembrowskl,2

### Bachelor s Degree Programme (BDP)

EEG-01/ BEGE-101 Bachelor s Degree Programme (BDP) ASSIGNMENT (for July 2018 and January 2019 Sessons) EEG-01/BEGE-101 ELECTIVE COURSE IN ENGLISH School of Humantes Indra Gandh Natonal Open Unersty Madan

### Simple Solution for Designing the Piecewise Linear Scalar Companding Quantizer for Gaussian Source

94 J. NIKOIĆ, Z. PERIĆ,. VEIMIROVIĆ, SIMPE SOUTION FOR DESIGNING THE PIECEWISE INEAR SCAAR Smle Soluton for Desgnng the Pecewse near Scalar Comandng Quantzer for Gaussan Source Jelena NIKOIĆ, Zoran PERIĆ,

### COMP Test on Psychology 320 Check on Mastery of Prerequisites

COMP Test on Psychology 320 Check on Mastery of Prerequisites This test is designed to provide you and your instructor with information on your mastery of the basic content of Psychology 320. The results

### Q. YOU SAY IN PARAGRAPH 3 OF THlf PAPER THAT YOU'VE

"t... _. ------- -~---------.--~-.-...-------.."-.-"---.~,-~.-".--.---..-..-.~-.--~.~-------"..---+-...---" --_... l... l.... BY MR. MURRY: 0. Q. BUT YOU DON'T REMEMBER THE ST~TSTCAL DFFERENCE STTNG HERE

### Handout #5. Introduction to the Design of Experiments (DOX) (Reading: FCDAE, Chapter 1~3)

Hadout #5 Ttle: FAE Course: Eco 368/01 Spr/015 Istructor: Dr. I-M Chu Itroducto to the Des of Expermets (DOX) (Read: FCDAE, Chapter 1~3) I hadout oe, we leared that data ca be ether observatoal or expermetal.

### Distribution of Data and the Empirical Rule

302360_File_B.qxd 7/7/03 7:18 AM Page 1 Distribution of Data and the Empirical Rule 1 Distribution of Data and the Empirical Rule Stem-and-Leaf Diagrams Frequency Distributions and Histograms Normal Distributions

### CONNECTIONS GUIDE. To Find Your Hook.up Turn To Page 1

CONNECTIONS GUIDE To Fnd Your Hook.up Turn To Page 1 Connectng TV to Antenna (or Cable Wthout Cable Box) and No VCR (Hook-up 1A)... 2 Monaural VCR (Hook-up 1B)... 3 StereoVCR (Hook-up 1C)... 4 Cable Wth

### Anchor Box Optimization for Object Detection

Anchor Box Optmzaton for Object Detecton Yuany Zhong 1, Janfeng Wang 2, Jan Peng 1, and Le Zhang 2 1 Unversty of Illnos at Urbana-Champagn 2 Mcrosoft Research 1 {yuanyz2, janpeng}@llnos.edu, 2 {janfw,

### User s manual. Digital control relay SVA

User s manual Dgtal control relay DISIBEINT ELECTRONIC S.L, has been present n the feld of the manufacture of components for the ndustral automaton for more than years, and mantans n constant evoluton

### STAT 113: Statistics and Society Ellen Gundlach, Purdue University. (Chapters refer to Moore and Notz, Statistics: Concepts and Controversies, 8e)

STAT 113: Statistics and Society Ellen Gundlach, Purdue University (Chapters refer to Moore and Notz, Statistics: Concepts and Controversies, 8e) Learning Objectives for Exam 1: Unit 1, Part 1: Population

### Step 3: Select a Class

R Step 1: Roll Ablty Scores a. ndcate dce-rollng method (p. 13):. Roll 3d6 sx tmes, n order. 11. Roll 3d6 twce per ablty, select ether. 111. Roll 3d6 sx tmes and assgn to abltes as desred. V. Roll 3d6

### AIAA Optimal Sampling Techniques for Zone- Based Probabilistic Fatigue Life Prediction

AIAA 2002-383 Optmal Samplng Technques or Zone- Based Probablstc Fatgue Le Predcton M. P. Enrght Southwest Research Insttute San Antono, TX H. R. Mllwater Unversty o Texas at San Antono San Antono, TX

### A STUDY OF TRUMPET ENVELOPES

A STUDY OF TRUMPET ENVELOPES Roger B. Dannenberg, Hank Pellern, and Istvan Dereny School of Computer Scence, Carnege Mellon Unversty Pttsburgh, PA 15213 USA rbd@cs.cmu.edu, hank.pellern@andrew.cmu.edu,

### CASH TRANSFER PROGRAMS WITH INCOME MULTIPLIERS: PROCAMPO IN MEXICO

FCND DP No. 99 FCND DISCUSSION PAPER NO. 99 CASH TRANSFER PROGRAMS WITH INCOME MULTIPLIERS: PROCAMPO IN MEXICO Elsabeth Sadoulet, Alan de Janvry, and Benjamn Davs Food Consumpton and Nutrton Dvson Internatonal

### Production of Natural Penicillins by Strains of Penicillium chrysogenutn

Producton of Natural Pencllns by Strans of Pencllum chrysogenutn a J. FUSK and ЬЕ. WELWRDOVÁ ^Department of Mcrobology and Bochemstry, Slovak Techncal Unversty, Bratslava b Botka, Slovenská Ľupča Receved

### CONNECTIONS GUIDE. To Find Your Hook.up Turn To Page 1

CONNECTIONS GUIDE To Fnd Your Hook.up Turn To Page 1 Connectng TV to Antenna (or Cable Wthout Cable Box) and No VCR (Hook-up 1A)...2 Monaural VCR (Hook-up 1B)...3 Stereo VCR (Hook-up 1C)... 4 Cable Wth

### Product Information. Manual change system HWS

Product Informaton HWS HWS Flexble. Compact. Productve. HWS manual change system Manual tool change system wth ntegrated ar feed-through and optonal electrc feed-through Feld of applcaton Excellently sutable

### Product Information. Manual change system HWS

Product Informaton HWS HWS Flexble. Compact. Productve. HWS manual change system Manual tool change system wth ntegrated ar feed-through and optonal electrc feed-through Feld of applcaton Excellently sutable

### Simon Sheu Computer Science National Tsing Hua Universtity Taiwan, ROC

Mounr A. Tantaou School of Electrcal Engneerng and Computer Scence Unversty of Central Florda Orlando, FL 3286-407-823-393 tantaou@cs.ucf.edu Interacton wth Broadcast Vdeo Ken A. Hua School of Electrcal

### Discussion Paper Series

Doshsha Unversty Center for the Study of the Creatve Economy Dscusson Paper Seres No. 2013-04 Nonlnear Effects of Superstar Collaboraton: Why the Beatles Succeeded but Broke Up Tadash Yag Dscusson Paper

### Accepted Manuscript. An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time

Accepted Manuscrpt An mproved artfcal bee colony algorthm for flexble ob-shop schedulng problem wth fuzzy processng tme Ka Zhou Gao, Ponnuthura Nagaratnam Suganthan, Quan Ke Pan, Tay Jn Chua, Chn Soon

### Algebra I Module 2 Lessons 1 19

Eureka Math 2015 2016 Algebra I Module 2 Lessons 1 19 Eureka Math, Published by the non-profit Great Minds. Copyright 2015 Great Minds. No part of this work may be reproduced, distributed, modified, sold,

### Study on the location of building evacuation indicators based on eye tracking

Study on the locaton of buldng evacuaton ndcators based on eye trackng Yue L Tsnghua Unversty yue-l5@malstsnghuaeducn Png hang Tsnghua Unversty zhangp@malstsnghuaeducn Hu hang Tsnghua Unversty, zhhu@tsnghuaeducn

### Error Concealment Aware Rate Shaping for Wireless Video Transport 1

Error Concealment Aware Rate Shapng for Wreless Vdeo Transport 1 Trsta Pe-chun Chen and Tsuhan Chen 2 Abstract Streamng of vdeo, whch s both source- and channel- coded, over wreless networks faces the

### Cost-Aware Fronthaul Rate Allocation to Maximize Benefit of Multi-User Reception in C-RAN

Cost-Aware Fronthaul Rate Allocaton to Maxmze Beneft of Mult-User Recepton n C-RAN Dora Bovz, Chung Shue Chen, Sheng Yang To cte ths verson: Dora Bovz, Chung Shue Chen, Sheng Yang. Cost-Aware Fronthaul

### Product Information. Universal swivel units SRU-plus

Product Informaton Unversal swvel unts SRU-plus SRU-plus Unversal swvel unts Robust. Fast. Hgh Performance. SRU-plus unversal rotary actuator Unversal unt for pneumatc swvel and turnng movements. Feld

### Quantization of Three-Bit Logic for LDPC Decoding

Proceedngs of the World Congress on Engneerng and Computer Scence 2011 Vol II, October 19-21, 2011, San Francsco, USA Quantzaton of Three-Bt Logc for LDPC Decodng Raymond Moberly and Mchael E. O'Sullvan

### Reduce Distillation Column Cost by Hybrid Particle Swarm and Ant

From the SelectedWorks of Dr. Sandp Kumar Lahr Summer July 20, 2016 Reduce Dstllaton Column Cost by Hybrd Partcle Swarm and Ant Dr. Sandp k lahr chnmaya lenka Avalable at: https://works.bepress.com/sandp_lahr/33/

### Turn it on. Your guide to getting the best out of BT Vision

Avalable n Bralle, large prnt and audo CD. Please call FREE on 8 8 15 for your copy. Turn t on Your gude to gettng the best out of www.bt.com/btvson V.2 28656 Enchantng flms to entertan all the famly Flms

### Correcting Image Placement Errors Using Registration Control (RegC ) Technology In The Photomask Periphery

Correctng Image Placement Errors Usng Regstraton Control (RegC ) Technology In The Photomask Perphery Av Cohen 1, Falk Lange 2 Guy Ben-Zv 1, Erez Gratzer 1, Dmtrev Vladmr 1 1. Carl Zess SMS Ltd., Karmel

### Fast Intra-Prediction Mode Decision in H.264/AVC Based on Macroblock Properties

Fast Intra-Predcton Mode Decson n H.264/AVC Based on Macroblock Propertes Abstract Intra-predcton s a wdely used tecnque n ntra codng. H.264/AVC adopts rate-dstorton optmzaton (RDO) tecnque to obtan te

### AN INTERACTIVE APPROACH FOR MULTI-CRITERIA SORTING PROBLEMS

AN INTERACTIVE APPROACH FOR MULTI-CRITERIA SORTING PROBLEMS A THESIS SUBMITTED TO THE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES OF MIDDLE EAST TECHNICAL UNIVERSITY BY BURAK KESER IN PARTIAL FULFILLMENT

### What can you tell about these films from this box plot? Could you work out the genre of these films?

FILM A FILM B FILM C Age of film viewer What can you tell about these films from this box plot? Could you work out the genre of these films? Compare the box plots and write down anything you notice FILM

### A Scalable HDD Video Recording Solution Using A Real-time File System

H. L et al.: A Scalable HDD Vdeo Recordng Soluton Usng A Real-tme Fle System A Scalable HDD Vdeo Recordng Soluton Usng A Real-tme Fle System Hong L, Stephen R. Cumpson Member, IEEE, Robert Jochemsen, Jan

### Scalable QoS-Aware Disk-Scheduling

Scalable QoS-Aware Dsk-Schedulng Wald G. Aref Khaled El-Bassyoun Ibrahm Kamel Mohamed F. Mokbel Department of Computer Scences, urdue Unversty, West Lafayette, IN 47907-1398 anasonc Informaton and Networkng

### Chapter 1 Midterm Review

Name: Class: Date: Chapter 1 Midterm Review Multiple Choice Identify the choice that best completes the statement or answers the question. 1. A survey typically records many variables of interest to the

### SKEW DETECTION AND COMPENSATION FOR INTERNET AUDIO APPLICATIONS. Orion Hodson, Colin Perkins, and Vicky Hardman

SKEW DETECTION AND COMPENSATION FOR INTERNET AUDIO APPLICATIONS Oron Hodson, Coln Perkns, and Vcky Hardman Department of Computer Scence Unversty College London Gower Street, London, WC1E 6BT, UK. ABSTRACT

### AMP-LATCH* Ultra Novo mm [.025 in.] Ribbon Cable 02 MAR 12 Rev C

AMP-LATCH* Ultra Novo Applcaton Specfcaton Receptacle Connectors for 114-40056 0.64 mm [.025 n.] Rbbon Cable 02 MAR 12 All numercal values are n metrc unts [wth U.S. customary unts n brackets]. Dmensons

### GENERAL AGREEMENT ON MMra

RESTRICTED GENERAL AGREEMENT ON MMra TARIFFS AND TRADE Speeal Dstrbuton Agrculture Commttee A. Remarks IMPORT MEASURES Varable Leves and Other Specal Charges Addendum SWITZERLAND Imports of the products

### Integration of Internet of Thing Technology in Digital Energy Network with Dispersed Generation

Amercan Scentfc Research Journal for Engneerng, Technology, and Scences (ASRJETS) ISS (Prnt) 2313-4410, ISS (Onlne) 2313-4402 Global Socety of Scentfc Research and Researchers http://asrjetsjournal.org/

### Bootstrap Methods in Regression Questions Have you had a chance to try any of this? Any of the review questions?

ICPSR Blalock Lectures, 2003 Bootstrap Resampling Robert Stine Lecture 3 Bootstrap Methods in Regression Questions Have you had a chance to try any of this? Any of the review questions? Getting class notes

### INIHODU~IION AND NOI[~ OJ KJUN~ HO rahk

HAROLD COURLANO[R, C[N[RAL [OITOR INIHODU~IION AND NOI[~ OJ KJUN~ HO rahk Covel desgon by Ronald Clyne , ~ C» ==-== :' C ::::J FOLKWA YS RECORDS FE 4424 ~, C C.., -- s;::: :- C I:) : C I:) ~..,.,---, a,

### ,~ COUNTY OF C 0'0 K ) 2 FATHER EDWARD SCHMIDT 1 IN THE CIRCUIT COURT OF COOK COUNTY, ILLINOIS. 8 The discovery deposition of FATHER EDWARD 9 EXHIBITS

. STATE OF LLNOS ) NDEX ) SS: WTNESS EXAMNATON COUNTY OF C 0'0 K ) FATHER EDWARD SCHMDT N THE CRCUT COURT OF COOK COUNTY LLNOS BY MR. PEARLMAN t' COUNTY OEPARTMENT. LAW DVSON 'j JOHN DOE # ) ' Plantff

### Critical Path Reduction of Distributed Arithmetic Based FIR Filter

Crtcal Path Reducton of strbuted rthmetc Based FIR Flter Sunta Badave epartment of Electrcal and Electroncs Engneerng.I.T, urangabad aharashtra, Inda njal Bhalchandra epartment of Electroncs and Telecommuncaton

### Chapter 27. Inferences for Regression. Remembering Regression. An Example: Body Fat and Waist Size. Remembering Regression (cont.)

Chapter 27 Inferences for Regression Copyright 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 27-1 Copyright 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley An

### Multi-Line Acquisition With Minimum Variance Beamforming in Medical Ultrasound Imaging

IEEE Transactons on Ultrasoncs, Ferroelectrcs, and Frequency Control, vol. 60, no. 12, Decemer 2013 2521 Mult-Lne Acquston Wth Mnmum Varance Beamformng n Medcal Ultrasound Imagng Ad Ranovch, Zv Fredman,

### Measuring Variability for Skewed Distributions

Measuring Variability for Skewed Distributions Skewed Data and its Measure of Center Consider the following scenario. A television game show, Fact or Fiction, was canceled after nine shows. Many people

### Lesson 7: Measuring Variability for Skewed Distributions (Interquartile Range)

: Measuring Variability for Skewed Distributions (Interquartile Range) Exploratory Challenge 1: Skewed Data and its Measure of Center Consider the following scenario. A television game show, Fact or Fiction,

### Chapter 6. Normal Distributions

Chapter 6 Normal Distributions Understandable Statistics Ninth Edition By Brase and Brase Prepared by Yixun Shi Bloomsburg University of Pennsylvania Edited by José Neville Díaz Caraballo University of

### MODELING AND ANALYZING THE VOCAL TRACT UNDER NORMAL AND STRESSFUL TALKING CONDITIONS

MODELING AND ANALYZING THE VOCAL TRACT UNDER NORMAL AND STRESSFUL TALING CONDITIONS Ismal Shahn and Naeh Botros 2 Electrcal/Electroncs and Comuter Engneerng Deartment Unversty of Sharjah, P. O. Box 27272,

### Product Information. Universal swivel units SRU-plus 25

Product Informaton SRU-plus Robust. Fast. Hgh Performance. SRU-plus unversal rotary actuator Unversal unt for pneumatc swvel and turnng movements. Feld of applcaton Can be used n ether clean or contamnated

### Product Information. Miniature rotary unit ERD

Product Informaton ERD ERD Fast. Compact. Flexble. ERD torque motor Powerful torque motor wth absolute encoder and electrc and pneumatc rotary feed-through Feld of applcaton For all applcatons wth exceptonal

### A Quantization-Friendly Separable Convolution for MobileNets

arxv:1803.08607v1 [cs.cv] 22 Mar 2018 A Quantzaton-Frendly Separable for MobleNets Abstract Tao Sheng tsheng@qt.qualcomm.com Xaopeng Zhang parker.zhang@gmal.com As deep learnng (DL) s beng rapdly pushed

### GENERAL WILLIAM C. WESTMORELAND.} } PLAINTIFF. } VS. } DEFENDANTS. )

N THE UNTED STATES DSTRCT COURT SOUTHERN DSTRCT OF NEW YORK ACTON NO. 32 ev 7913 (PNL) r. ~ ' \...'. /~ GENERAL WLLAM C. WESTMORELAND.} } PLANTFF. } } VS. } } CBS NC. ET AL.. } ) DEFENDANTS. ) DEPOSTON

### FPGA Implementation of Cellular Automata Based Stream Cipher: YUGAM-128

ISSN (Prnt) : 2320 3765 ISSN (Onlne): 2278 8875 Internatonal Journal of Advanced Research n Electrcal, Electroncs and Instrumentaton Engneerng An ISO 3297: 2007 Certfed Organzaton Vol. 3, Specal Issue

### Box Plots. So that I can: look at large amount of data in condensed form.

LESSON 5 Box Plots LEARNING OBJECTIVES Today I am: creating box plots. So that I can: look at large amount of data in condensed form. I ll know I have it when I can: make observations about the data based

### User Manual. AV Router. High quality VGA RGBHV matrix that distributes signals directly. Controlled via computer.

User Manual AV Router Hgh qualty VGA RGBHV matrx that dstrbutes sgnals drectly. Controlled va computer. Notce: : The nmaton contaned n ths document s subject to change wthout notce. SmartAVI makes no warranty

### ) No. 07 L 6781 ) ) )

c '"r 0 STATE OF LLNOS ) ) ss: COUNTY OF C 0 O.K ) N THE CRCUT COURT OF COOK COUNTY LLNOS COUNTY OEPARTMENT. LAW OVSON JOHN DOE # ) Pantff ) VS. THE CHCAGO PROVNCE OF THE SOCETY OF JESUS. Defendant. )

### MATH 214 (NOTES) Math 214 Al Nosedal. Department of Mathematics Indiana University of Pennsylvania. MATH 214 (NOTES) p. 1/11

MATH 214 (NOTES) Math 214 Al Nosedal Department of Mathematics Indiana University of Pennsylvania MATH 214 (NOTES) p. 1/11 CHAPTER 6 CONTINUOUS PROBABILITY DISTRIBUTIONS MATH 214 (NOTES) p. 2/11 Simple

### Modular Plug Connectors (Standard and Small Conductor)

Modular Plug Connectors (Standard and Small Conductor) Applcaton Specfcaton 114-6016 04 APR 11 All numercal values are n metrc unts [wth U.S. customary unts n brackets]. Dmensons are n mllmeters [and nches].

### IN DESCRIBING the tape transport of

Apparatus For Magnetc Storage on Three-Inch Wde Tapes R. B. LAWRANCE R. E. WILKINS R. A. PENDLETON IN DESCRIBING the tape transport of the DATAmatc 1, t s perhaps well to begn by revewng the nfluental

### Estimation of inter-rater reliability

Estimation of inter-rater reliability January 2013 Note: This report is best printed in colour so that the graphs are clear. Vikas Dhawan & Tom Bramley ARD Research Division Cambridge Assessment Ofqual/13/5260

### Set-Top-Box Pilot and Market Assessment

Final Report Set-Top-Box Pilot and Market Assessment April 30, 2015 Final Report Set-Top-Box Pilot and Market Assessment April 30, 2015 Funded By: Prepared By: Alexandra Dunn, Ph.D. Mersiha McClaren,

### Color Monitor. L200p. English. User s Guide

Color Montor L200p User s Gude Englsh Frst Edton (February / 2003) Note : For mportant nformaton, refer to the Montor Safety and Warranty manual that comes wth ths montor. Contents ENGLISH Safety (Read

### AGAINST ALL ODDS EPISODE 22 SAMPLING DISTRIBUTIONS TRANSCRIPT

AGAINST ALL ODDS EPISODE 22 SAMPLING DISTRIBUTIONS TRANSCRIPT 1 FUNDER CREDITS Funding for this program is provided by Annenberg Learner. 2 INTRO Pardis Sabeti Hi, I m Pardis Sabeti and this is Against

### MATH 214 (NOTES) Math 214 Al Nosedal. Department of Mathematics Indiana University of Pennsylvania. MATH 214 (NOTES) p. 1/3

MATH 214 (NOTES) Math 214 Al Nosedal Department of Mathematics Indiana University of Pennsylvania MATH 214 (NOTES) p. 1/3 CHAPTER 1 DATA AND STATISTICS MATH 214 (NOTES) p. 2/3 Definitions. Statistics is

### ! I I.! rrhe LOGIC OF TEE CONCEP'r Ob' ART

!.! rrhe LOGC OF TEE CONCEP'r Ob' ART THE WGC OF THE CmWW1' OF ART By! PATRCK PAUL HCLAUGHLH. B.A. A Thess Submhed to the Sc:ho01 of Graduate Studes ~n :'1rt~al 1'"U1llmel1't 02 "tne t.equremen:;s for

### Automated composer recognition for multi-voice piano compositions using rhythmic features, n-grams and modified cortical algorithms

Complex Intell. Syst. (2018) 4:55 65 https://do.org/10.1007/s40747-017-0052-x ORIGINAL ARTICLE Automated composer recognton for mult-voce pano compostons usng rhythmc features, n-grams and modfed cortcal