STATISTICS. , the mean deviation about their mean x is given by. x x M.D (M) =
|
|
- Homer Stanley
- 5 years ago
- Views:
Transcription
1 Chapter 5 STATISTICS 5. Overvew I earler classes, you have studed measures of cetral tedecy such as mea, mode, meda of ugrouped ad grouped data. I addto to these measures, we ofte eed to calculate a secod type of measure called a measure of dsperso whch measures the varato the observatos about the mddle value mea or meda etc. Ths chapter s cocered wth some mportat measures of dsperso such as mea devato, varace, stadard devato etc., ad fally aalyss of frequecy dstrbutos. 5.. Measures of dsperso (a) RageThe measure of dsperso whch s easest to uderstad ad easest to calculate s the rage. Rage s defed as: Rage Largest observato Smallest observato (b) Mea Devato () Mea devato for ugrouped data: For observato x, x,..., x, the mea devato about ther mea x s gve by M.D ( x ) x x Mea devato about ther meda M s gve by () M.D (M) x M () Mea devato for dscrete frequecy dstrbuto Let the gve data cosst of dscrete observatos x, x,..., x occurrg wth frequeces f, f,..., f, respectvely. I ths case ()
2 STATISTICS 7 M.D ( x ) f x x f x x f (3) M.D (M) f x M where f. () Mea devato for cotuous frequecy dstrbuto (Grouped data). M.D ( x ) f x x (4) (5) (c) (d) (e) (f) M.D (M) f x M where x are the mdpots of the classes, x ad M are, respectvely, the mea ad meda of the dstrbuto. Varace : Let x, x,..., x be observatos wth x as the mea. The varace, deoted by σ, s gve by σ ( x x) (7) Stadard Devato: If σ s the varace, the σ, s called the stadard devato, s gve by σ ( x x) Stadard devato for a dscrete frequecy dstrbuto s gve by σ where f s are the frequeces of x s ad (6) (8) f ( x x) (9) f. Stadard devato of a cotuous frequecy dstrbuto (grouped data) s gve by
3 7 EXEMPLAR PROBLEMS MATHEMATICS σ f ( x x) (0) where x are the mdpots of the classes ad f ther respectve frequeces. Formula (0) s same as (g) Aother formula for stadard devato : () σ ( ) σ x where h s the wdth of class tervals ad y mea. h f x f x ( f y ) () f y x A ad A s the assumed h 5.. Coeffcet of varato It s sometmes useful to descrbe varablty by expressg the stadard devato as a proporto of mea, usually a percetage. The formula for t as a percetage s 5. Solved Examples Coeffcet of varato Stadard devato 00 Mea Short Aswer Type Example Fd the mea devato about the mea of the followg data: Sze (x): Frequecy (f): Soluto Mea x f x f M.D. ( x ) f x x 3(7) + 3(5) + 4(3) + 4() + 7() + 4(3) + 3(5) + 4(7) f 4
4 STATISTICS Example Fd the varace ad stadard devato for the followg data: 57, 64, 43, 67, 49, 59, 44, 47, 6, 59 Soluto Mea ( x ) Varace (σ ) ( x x) Stadard devato (σ) σ Example 3 Show that the two formulae for the stadard devato of ugrouped data. ( x x) σ ad are equvalet. Soluto We have ( x x) x σ x + ( x xx x ) + + x xx x x x x + ( x ) ( ) + x x x x x x
5 74 EXEMPLAR PROBLEMS MATHEMATICS Dvdg both sdes by ad takg ther square root, we get σ σ. Example 4 Calculate varace of the followg data : Class terval Frequecy Mea ( x ) fx f Soluto Varace (σ ) f ( x x) 3( 7) + 6( 3) + 4() + 7(5) f Log Aswer Type Example 5 Calculate mea, varato ad stadard devato of the followg frequecy dstrbuto: Classes Frequecy
6 STATISTICS 75 Soluto Let A, the assumed mea, be 5.5. Here h 0 Classes x y x f f y fy x fy f Mea x ( 0) (0.4).5 h Varace (σ ) fy ( fy ) [70(4) ( 8) ] 70 (4) S.D. (σ) 6.7
7 76 EXEMPLAR PROBLEMS MATHEMATICS Example 6 Lfe of bulbs produced by two factores A ad B are gve below: Legth of lfe Factory A Factory B ( hours) (umber of bulbs) (umber of bulbs) The bulbs of whch factory are more cosstet from the pot of vew of legth of lfe? Soluto Here h 00, let A (assumed mea) 800. Legth of lfe Md values(x ) y x A 0 Factory A Factory B ( hour) f f y fy f f y fy For factory A Mea ( x ) hours 0 S.D (46)
8 STATISTICS 77 Therefore, Coeffcet of varato (C.V.) For factory B Mea S.D. S.D x (56) ( 36) 0 0 S.D. 0 Therefore, Coeffcet of varato Mea 770 Sce C.V. of factory B > C.V. of factory A Factory B has more varablty whch meas bulbs of factory A are more cosstet. Objectve Type Questos Choose the correct aswer out of the four optos gve agast each of the Examples 7 to 9 (M.C.Q.). Example 7 The mea devato of the data, 9, 9, 3, 6, 9, 4 from the mea s (A).3 (B).57 (C) 3.3 (D) 3.57 Soluto (B) s the correct aswer M.D. ( x ) x x Example 8 Varace of the data, 4, 5, 6, 8, 7 s The varace of 4, 8, 0,, 6, 34 wll be (A) 3.3 (B) 5.33 (C) (D) Soluto (C) s the correct aswer. Whe each observato s multpled by, the varace s also multpled by. Example 9 A set of values x, x,..., x has stadard devato 6. The stadard devato of values x + k, x + k,..., x + k wll be (A) σ (B) σ + k (C) σ k (D) kσ Soluto (A) s correct aswer. If each observato s creased by a costat k, the stadard devato s uchaged.
9 78 EXEMPLAR PROBLEMS MATHEMATICS 5.3 EXERCISE Short Aswer Type. Fd the mea devato about the mea of the dstrbuto: Sze Frequecy Fd the mea devato about the meda of the followg dstrbuto: Marks obtaed o. of studets Calculate the mea devato about the mea of the set of frst atural umbers whe s a odd umber. 4. Calculate the mea devato about the mea of the set of frst atural umbers whe s a eve umber. 5. Fd the stadard devato of the frst atural umbers. 6. The mea ad stadard devato of some data for the tme take to complete a test are calculated wth the followg results: umber of observatos 5, mea 8. secods, stadard devato 3.5 secods. Further, aother set of 5 observatos x, x,..., x 5, also secods, s ow avalable ad we have 5 x 79 ad dervato based o all 40 observatos. 5 x 554. Calculate the stadard 7. The mea ad stadard devato of a set of observatos are x ad s, respectvely whle the mea ad stadard devato of aother set of observatos are x ad s, respectvely. Show that the stadard devato of the combed set of ( + ) observatos s gve by S.D. ( ) + ( ) ( ) + + ( + ) s s x x
10 STATISTICS Two sets each of 0 observatos, have the same stadard dervato 5. The frst set has a mea 7 ad the secod a mea. Determe the stadard devato of the set obtaed by combg the gve two sets. 9. The frequecy dstrbuto: x A A 3A 4A 5A 6A f where A s a postve teger, has a varace of 60. Determe the value of A. 0. For the frequecy dstrbuto: x f Fd the stadard dstrbuto.. There are 60 studets a class. The followg s the frequecy dstrbuto of the marks obtaed by the studets a test: Marks Frequecy x x x (x + ) x x + where x s a postve teger. Determe the mea ad stadard devato of the marks.. The mea lfe of a sample of 60 bulbs was 650 hours ad the stadard devato was 8 hours. A secod sample of 80 bulbs has a mea lfe of 660 hours ad stadard devato 7 hours. Fd the overall stadard devato. 3. Mea ad stadard devato of 00 tems are 50 ad 4, respectvely. Fd the sum of all the tem ad the sum of the squares of the tems. ad the total umber of tem s 4. If for a dstrbuto ( x 5) 3, ( x 5) 43 8, fd the mea ad stadard devato. 5. Fd the mea ad varace of the frequecy dstrbuto gve below: x x < 3 3 x < 5 5 x < 7 7 x < 0 f 6 4 5
11 80 EXEMPLAR PROBLEMS MATHEMATICS Log Aswer Type 6. Calculate the mea devato about the mea for the followg frequecy dstrbuto: Class terval Frequecy Calculate the mea devato from the meda of the followg data: Class terval Frequecy Determe the mea ad stadard devato for the followg dstrbuto: Marks Frequecy The weghts of coffee 70 jars s show the followg table: Weght ( grams) Frequecy Determe varace ad stadard devato of the above dstrbuto. 0. Determe mea ad stadard devato of frst terms of a A.P. whose frst term s a ad commo dfferece s d.
12 STATISTICS 8. Followg are the marks obtaed, out of 00, by two studets Rav ad Hasha 0 tests. Rav Hasha Who s more tellget ad who s more cosstet?. Mea ad stadard devato of 00 observatos were foud to be 40 ad 0, respectvely. If at the tme of calculato two observatos were wrogly take as 30 ad 70 place of 3 ad 7 respectvely, fd the correct stadard devato. 3. Whle calculatg the mea ad varace of 0 readgs, a studet wrogly used the readg 5 for the correct readg 5. He obtaed the mea ad varace as 45 ad 6 respectvely. Fd the correct mea ad the varace. Objectve Type Questos Choose the correct aswer out of the gve four optos each of the Exercses 4 to 39 (M.C.Q.). 4. The mea devato of the data 3, 0, 0, 4, 7, 0, 5 from the mea s (A) (B).57 (C) 3 (D) Mea devato for observatos x, x,..., x from ther mea x s gve by (A) ( x x) (B) x x (C) ( x ) x (D) ( x x ) 6. Whe tested, the lves ( hours) of 5 bulbs were oted as follows: 357, 090, 666, 494, 63 The mea devatos ( hours) from ther mea s (A) 78 (B) 79 (C) 0 (D) Followg are the marks obtaed by 9 studets a mathematcs test: 50, 69, 0, 33, 53, 39, 40, 65, 59 The mea devato from the meda s: (A) 9 (B) 0.5 (C).67 (D) 4.76
13 8 EXEMPLAR PROBLEMS MATHEMATICS 8. The stadard devato of the data 6, 5, 9, 3,, 8, 0 s (A) 5 7 (B) 5 7 (C) 6 (D) 6 9. Let x, x,..., x be observatos ad x be ther arthmetc mea. The formula for the stadard devato s gve by (A) ( x x) (B) ( x x) (C) ( x x) (D) x + x 30. The mea of 00 observatos s 50 ad ther stadard devato s 5. The sum of all squares of all the observatos s (A) (B) (C) 5500 (D) Let a, b, c, d, e be the observatos wth mea m ad stadard devato s. The stadard devato of the observatos a + k, b + k, c + k, d + k, e + k s (A) s (B) ks (C) s + k (D) 3. Let x, x, x 3, x 4, x 5 be the observatos wth mea m ad stadard devato s. The stadard devato of the observatos kx, kx, kx 3, kx 4, kx 5 s (A) k + s (B) s k (C) ks (D) s 33. Let x, x,... x be observatos. Let w lx + k for,,..., where l ad k are costats. If the mea of x s s 48 ad ther stadard devato s, the mea of w s s 55 ad stadard devato of w s s 5, the values of l ad k should be (A) l.5, k 5 (B) l.5, k 5 (C) l.5, k 5 (D) l.5, k Stadard devatos for frst 0 atural umbers s (A) 5.5 (B) 3.87 (C).97 (D) Cosder the umbers,, 3, 4, 5, 6, 7, 8, 9, 0. If s added to each umber, the varace of the umbers so obtaed s s k
14 STATISTICS 83 (A) 6.5 (B).87 (C) 3.87 (D) Cosder the frst 0 postve tegers. If we multply each umber by ad the add to each umber, the varace of the umbers so obtaed s (A) 8.5 (B) 6.5 (C) 3.87 (D) The followg formato relates to a sample of sze 60: x 8000, x 960 The varace s (A) 6.63 (B) 6 (C) (D) Coeffcet of varato of two dstrbutos are 50 ad 60, ad ther arthmetc meas are 30 ad 5 respectvely. Dfferece of ther stadard devato s (A) 0 (B) (C).5 (D) The stadard devato of some temperature data C s 5. If the data were coverted to ºF, the varace would be (A) 8 (B) 57 (C) 36 (D) 5 Fll the blaks Exercses from 40 to Coeffcet of varato... Mea If x s the mea of values of x, the ( x x) s always equal to. If a has ay value other tha x, the ( x x) s tha ( x a) 4. If the varace of a data s, the the stadard devato of the data s. 43. The stadard devato of a data s of ay chage org, but s o the chage of scale. 44. The sum of the squares of the devatos of the values of the varable s whe take about ther arthmetc mea. 45. The mea devato of the data s whe measured from the meda. 46. The stadard devato s to the mea devato take from the arthmetc mea.
(4) n + 1. n+1. (1) 2n 1 (2) 2 (3) n 1 2 (1) 1 (2) 3 (1) 23 (2) 25 (3) 27 (4) 30
CHCK YOUR GRASP STATISTICS XRCIS-I Arthmetc mea, weghted mea, Combed mea. Mea of the frst terms of the A.P. a, (a + d), (a + d),... s- a d () ( )d a a + ( ) d a + d. The A.M. of frst eve atural umber s
More informationLecture 4: Distribution of the Mean of Random Variables
Experece has show that a certa le detector wll show a postve readg (so you are lyg) 0% of the tme whe a perso s tellg the truth ad 95% of the tme whe a perso s actually lyg. Suppose 0 suspects are subjected
More informationx in place of µ in formulas.
Algebra Notes SOL A.9 Statstcal Varato Mrs. Greser Name: Date: Block: Statstcal Varato Notato/Term Descrpto Example/Notes populato A etre set of data about whch we wsh to ga formato. The heght of every
More informationMeasures of Central Tendency - the Mean
Measures of Cetral Tedecy - the Mea Dr Tom Ilveto Departmet of Food ad Resource Ecoomcs Overvew We wll beg lookg at varous measures of the ceter of the data - thk of t as a typcal value We wll start wth
More informationSo... we make an error when we estimate
8. Samplg Dstrbuto of the Mea Pg 6/Ex 7. A populato of 7 studets has ages 9 0 8 9 5 The populato mea ( ) 0.7 Estmate the populato mea by takg a radom sample of studets 9 5 Fd the sample mea... 9 + + 5.67
More informationTwo Data sets. Variability. Data Example with the range. Issues with the range. Central Tendency tells part of the story
Cetral Tedecy tell part of the tory Numercal Decrptve Meaure for Quattatve data II Dr. Tom Ilveto FREC 408 Image two data et Data et ha a mea, meda, ad mode of 5 Data et ha a mea, meda, ad mode of 5 Two
More informationEDEXCEL NATIONAL CERTIFICATE UNIT 28 FURTHER MATHEMATICS FOR TECHNICIANS OUTCOME 1- ALGEBRAIC TECHNIQUES TUTORIAL 3 - STATISTICAL TECHNIQUES
EDEXCEL NATIONAL CERTIFICATE UNIT 8 FURTHER MATHEMATICS FOR TECHNICIANS OUTCOME 1- ALGEBRAIC TECHNIQUES TUTORIAL 3 - STATISTICAL TECHNIQUES CONTENTS Be able to apply algebraic techiques Arithmetic progressio
More informationChapter 8 Descriptive Statistics
8.1 Uivariate aalysis ivolves a sigle variable, for examples, the weight of all the studets i your class. Comparig two thigs, like height ad weight, is bivariate aalysis. (Which we will look at later)
More informationAero-Material Consumption Prediction Based on Linear Regression Model
Avalable ole at www.scecedrect.com SceceDrect Proceda Computer Scece 3 () 5 3 th Iteratoal Cogress of Iformato ad Commucato Techology (ICICT-) Aero-Materal Cosumpto Predcto Based o Lear Regresso Model
More informationMeasures of Spread: Standard Deviation
Measures of Spread: Stadard Deviatio So far i our study of umerical measures used to describe data sets, we have focused o the mea ad the media. These measures of ceter tell us the most typical value of
More informationSINGLE SIMULATION CONFIDENCE INTERVALS USING THE DELTA METHOD
Roser Chrstoph ad Masaru Nakao. Sgle Smulato Cofdece Itervals Usg the Delta Method. I Iteratoal Smposum o Schedulg edted b H. Fumoto ad M. Kuroda 63 66. Hamamatsu Japa 00. SINGLE SIMULATION CONFIDENCE
More informationThe Mean Area Measurement Method to. Multiple Attribute Decision Making within. Triangular Fuzzy Numbers 1
Apped Mathematca Sceces, Vo. 9, 015, o. 8, 393-397 HIKARI Ltd, www.m-hkar.com http://dx.do.org/10.1988/ams.015.1196 The Mea Area Measuremet Method to Mutpe Attrbute Decso Makg wth Traguar Fuzzy Numbers
More informationJUST THE MATHS UNIT NUMBER STATISTICS 3 (Measures of dispersion (or scatter)) A.J.Hobson
JUST THE MATHS UNIT NUMBER 8.3 STATISTICS 3 (Measures of dispersio (or scatter)) by A.J.Hobso 8.3. Itroductio 8.3.2 The mea deviatio 8.3.3 Practica cacuatio of the mea deviatio 8.3.4 The root mea square
More informationStatistics 11 Lecture 18 Sampling Distributions (Chapter 6-2, 6-3) 1. Definitions again
Statistics Lecture 8 Samplig Distributios (Chapter 6-, 6-3). Defiitios agai Review the defiitios of POPULATION, SAMPLE, PARAMETER ad STATISTIC. STATISTICAL INFERENCE: a situatio where the populatio parameters
More informationContents. Random Variables
Rado Sgal Processg Chapter Rado Varables Chapter Rado Varables Cotets Rado Varables.... Deto o a Rado Varable..... Cuulatve Dstrbuto Fucto CDF..... Probablt Dest Fucto PDF..... partal characteratos...7..
More informationGOALS. Describing Data: Numerical Measures. Why a Numeric Approach? Concepts & Goals. Characteristics of the Mean. Graphic of the Arithmetic Mean
GOALS Describig Data: umerical Measures Chapter 3 Dr. Richard Jerz Calculate the arithmetic mea, weighted mea, media, ad mode Explai the characteristics, uses, advatages, ad disadvatages of each measure
More informationSec 7.6 Inferences & Conclusions From Data Central Limit Theorem
Sec 7. Ifereces & Coclusios From Data Cetral Limit Theorem Name: The Cetral Limit Theorem offers us the opportuity to make substatial statistical predictios about the populatio based o the sample. To better
More informationObjectives. Sampling Distributions. Overview. Learning Objectives. Statistical Inference. Distribution of Sample Mean. Central Limit Theorem
Objectives Samplig Distributios Cetral Limit Theorem Ivestigate the variability i sample statistics from sample to sample Fid measures of cetral tedecy for distributio of sample statistics Fid measures
More informationStandard deviation The formula for the best estimate of the population standard deviation from a sample is:
Geder differeces Are there sigificat differeces betwee body measuremets take from male ad female childre? Do differeces emerge at particular ages? I this activity you will use athropometric data to carry
More informationEfficiency of Modified Lord s Test in Testing Equality of Means: An Empirical Approach through Simulation with Theoretical Proof!
Proceedg of the Secod Aa-Pacfc Coferece o Global Bue, Ecoomc, Face ad Socal Scece AP5Vetam Coferece ISBN: 978--6345-833-6 Daag-Vetam, 0- July, 05 Paper ID: V54 Effcecy of Modfed Lord Tet Tetg Equalty of
More informationA Novel Global Measure Approach based on Ontology Spectrum to Evaluate Ontology Enrichment
A Novel Global Measure Approach based o Otology Spectrum to Evaluate Otology Erchmet Karm Kamou Faculty of Sceces of Tus, Uversty of Tus El Maar, Tusa ABSTRACT I the cotext of otology evoluto real world
More informationTesting Methods of Minimum Distance. Controlled Tabular Adjustment
Testg Methods of Mmum Dstace Cotrolled Tabular Adjustmet Jord Castro, Uversty of Cataluya Departmet of Statstcs ad Operatos Research Sarah GIESSING, Federal Statstcal Offce of Germay Dvso Mathematcal Statstcal
More informationEfficiency of Modified Lord s Test in Testing Equality of Means: An Empirical Approach through Simulation with Theoretical Proof
Iteratoal Revew of Reearch Emergg Market ad the Global Ecoomy IRREM A Ole Iteratoal Reearch Joural ISSN: 3-300 05 Vol: Iue 4 Effcecy of Modfed Lord Tet Tetg Equalty of Mea: A Emprcal Approach through Smulato
More informationApply Gaussian Distribution and RBF Neural Network to Diagnosis and Prescription High Blood Pressure Disease in Oriental Medicine
Apply Gaussa Dstrbuto ad BF Neural Network to Dagoss ad Prescrpto Hgh Blood Pressure Dsease retal Medce Cao Thag Graduate chool of cece ad Egeerg, tsumeka Uversty, Japa thagc@spcecrtsumeacp Yukobu Hosho
More informationStatistics Lecture 13 Sampling Distributions (Chapter 18) fe1. Definitions again
fe1. Defiitios agai Review the defiitios of POPULATIO, SAMPLE, PARAMETER ad STATISTIC. STATISTICAL IFERECE: a situatio where the populatio parameters are ukow, ad we draw coclusios from sample outcomes
More informationHow is the President Doing? Sampling Distribution for the Mean. Now we move toward inference. Bush Approval Ratings, Week of July 7, 2003
Samplig Distributio for the Mea Dr Tom Ilveto FREC 408 90 80 70 60 50 How is the Presidet Doig? 2/1/2001 4/1/2001 Presidet Bush Approval Ratigs February 1, 2001 through October 6, 2003 6/1/2001 8/1/2001
More informationVirus dynamics. How fast does HIV reproduce in vivo? Collaborators. HIV is a retrovirus. Treatment leads to a rapid decline in virus load
Vrus dyamcs Mart Nowak Isttute for Advaced Study Prceto Collaborators Robert May (Oxford) Sebasta Bohoeffer (Zurch) Domk Wodarz (Seattle) Marc Lpstch (Harvard) Alu Lloyd (Prceto) George Shaw (Brmgham,
More informationVARIANCE INFLATION FACTORS IN REGRESSION MODELS WITH DUMMY VARIABLES
Kasas State Uversty Lbrares Coferece o Appled Statstcs Agrculture - 4th Aual Coferece Proceedgs VARIANCE INFLATION FACTORS IN REGREION MODELS WITH DUMMY VARIABLES Legh Murray He Nguye Yu-Feg Lee Marta
More informationSummary. * EAA Central Facility, The University of Manchester, Faculty of Life Sciences, Manchester, UK; ** Health
Screeg Computer-Asssted Dosage Programs for atcoagulato wth warfar ad other vtam K-atagosts: mmum safety requremets for dvdual programs O behalf of the Subcommttee o Cotrol of Atcoagulato of the Scetfc
More informationConcepts Module 7: Comparing Datasets and Comparing a Dataset with a Standard
Cocepts Module 7: Comparig Datasets ad Comparig a Dataset with a Stadard Idepedece of each data poit Test statistics Cetral Limit Theorem Stadard error of the mea Cofidece iterval for a mea Sigificace
More informationTHE NORMAL DISTRIBUTION AND Z-SCORES COMMON CORE ALGEBRA II
Name: Date: THE NORMAL DISTRIBUTION AND Z-SCORES COMMON CORE ALGEBRA II The normal dstrbuton can be used n ncrements other than half-standard devatons. In fact, we can use ether our calculators or tables
More informationAppendix C: Concepts in Statistics
Appedi C. Measures of Cetral Tedecy ad Dispersio A8 Appedi C: Cocepts i Statistics C. Measures of Cetral Tedecy ad Dispersio Mea, Media, ad Mode I may real-life situatios, it is helpful to describe data
More informationNumerical Solutions of Fredholm Integral Equation of Second Kind Using Piecewise Bernoulli Polynomials
Nuercl Solutos of Fredhol Itegrl Equto of Secod Kd Usg Pecewse eroull Polyols Afroz Shr, Md. Shfqul Isl* Isttute of Nturl Sceces, Uted Itertol Uversty, Dh-9, gldesh. El: froz_shr77@yhoo.co. Deprtet of
More informationReview for Chapter 9
Review for Chapter 9 1. For which of the followig ca you use a ormal approximatio? a) = 100, p =.02 b) = 60, p =.4 c) = 20, p =.6 d) = 15, p = 2/3 e) = 10, p =.7 2. What is the probability of a sample
More informationEstimation of voltage sags patterns with k-means algorithm and clustering of fault zones in high and medium voltage grids
REVISTA INGENIERÍA E INVESTIGACIÓN Vol. 3 Suplemeto No. (SICEL 0) OCTUBRE DE 0 (338) Estmato of voltage sags patters wth kmeas algorthm ad clusterg of fault zoes hgh ad medum voltage grds Estmacó de patroes
More informationEstimation and Confidence Intervals
Estimatio ad Cofidece Itervals Chapter 9 McGraw-Hill/Irwi Copyright 2010 by The McGraw-Hill Compaies, Ic. All rights reserved. GOALS 1. Defie a poit estimate. 2. Defie level of cofidece. 3. Costruct a
More information5/7/2014. Standard Error. The Sampling Distribution of the Sample Mean. Example: How Much Do Mean Sales Vary From Week to Week?
Samplig Distributio Meas Lear. To aalyze how likely it is that sample results will be close to populatio values How probability provides the basis for makig statistical ifereces The Samplig Distributio
More informationCHAPTER 8 ANSWERS. Copyright 2012 Pearson Education, Inc. Publishing as Addison-Wesley
CHAPTER 8 ANSWERS Sectio 8.1 Statistical Literacy ad Critical Thikig 1 The distributio of radomly selected digits from to 9 is uiform. The distributio of sample meas of 5 such digits is approximately ormal.
More informationChapter 21. Recall from previous chapters: Statistical Thinking. Chapter What Is a Confidence Interval? Review: empirical rule
Chapter 21 What Is a Cofidece Iterval? Chapter 21 1 Review: empirical rule Chapter 21 5 Recall from previous chapters: Parameter fixed, ukow umber that describes the populatio Statistic kow value calculated
More informationMeasuring Dispersion
05-Sirki-4731.qxd 6/9/005 6:40 PM Page 17 CHAPTER 5 Measurig Dispersio PROLOGUE Comparig two groups by a measure of cetral tedecy may ru the risk for each group of failig to reveal valuable iformatio.
More informationOn The Use of Coefficient of Variation and 1, 2
Samlg Stratege for Fte Poulato Ug Auxlary Iformato O e Ue of oeffet of arato ad, Etmatg Mea of a Fte Poulato B. B. Kare, P. S. Ja ad U. Srvatava Deartmet of Statt, B.H.U, araa-005 Statt Seto, MM, B.H.U,
More information23.3 Sampling Distributions
COMMON CORE Locker LESSON Commo Core Math Stadards The studet is expected to: COMMON CORE S-IC.B.4 Use data from a sample survey to estimate a populatio mea or proportio; develop a margi of error through
More informationStatistical Analysis and Graphing
BIOL 202 LAB 4 Statistical Aalysis ad Graphig Aalyzig data objectively to determie if sets of data differ ad the to preset data to a audiece succictly ad clearly is a major focus of sciece. We eed a way
More informationAutism Awareness Education. April 2018
Autism Awareess Educatio April 2018 What is Autism Autism is a wide-spectrum metal disorder that is talked about every day i health circles, but few really kow all the facts about it. Research cotiues
More informationC have deleterious effects on renal function [l]. This
Seral Chages Real Fucto Cardac Surgcal Patets Gerald S. Weste, MD, Param S. Rao, PhD, George Vretaks, MD, ad Des H. yras, MD Dvso of Cardothoracc Surgery, he Heart Isttute, og Islad Jewsh Medcal Ceter,
More informationChapter 23 Summary Inferences about Means
U i t 6 E x t e d i g I f e r e c e Chapter 23 Summary Iferece about Mea What have we leared? Statitical iferece for mea relie o the ame cocept a for proportio oly the mechaic ad the model have chaged.
More informationRADIESSE Dermal Filler for the Correction of Moderate to Severe Facial Wrinkles and Folds, Such As Nasolabial Folds
A PATIENT S GUIDE RADIESSE Dermal Filler for the Correctio of Moderate to Severe Facial Wrikles ad Folds, Such As Nasolabial Folds Read all the iformatio before you are treated with Radiesse dermal filler.
More informationCaribbean Examinations Council Secondary Education Certificate School Based Assessment Additional Math Project
Caribbea Examiatios Coucil Secodary Educatio Certificate School Based Assessmet Additioal Math Project Does good physical health ad fitess, as idicated by Body Mass Idex, affect the academic performace
More informationPattern Identification of Subthalamic Local Field Potentials in Parkinson's Disease
ITM Web of Cofereces, 4 (7) DOI:.5/ tmcof/74 Patter Idetfcato of Subthalamc Local Feld Potetals Parkso's Dsease Ku Zhag,, B Feg,, Yu-Peg Zhag, Yog-Zh Huag,3 ad Shou-Ya Wag,4, * Suzhou Isttute of Bomedcal
More informationDraft Guidance on Methylphenidate Hydrochloride
Cota Nobdg ecommedato raft Gudace o Methylphedate ydrochlorde h draft gudace, oce falzed, wll repreet the Food ad rug Admtrato' (FA') curret thkg o th topc. t doe ot create or cofer ay rght for or o ay
More informationPERCEPTION OF CRISIS MANAGEMENT: A COMPARATIVE ANALYSIS OF GUATEMALAN AND U.S. SMALL BUSINESSES
PERCEPTION OF CRISIS MANAGEMENT: A COMPARATIVE ANALYSIS OF GUATEMALAN AND U.S. SMALL BUSINESSES Joh E. Splla, Ph.D. Assocate Professor of Bess Pe State Uversty Dubos Dubos, PA 15801 (814 375-4803 jes40@psu.edu
More informationA Supplement to Improved Likelihood Inferences for Weibull Regression Model by Yan Shen and Zhenlin Yang
A Supplemet to Improved Likelihood Ifereces for Weibull Regressio Model by Ya She ad Zheli Yag More simulatio experimets were carried out to ivestigate the effect of differet cesorig percetages o the performace
More informationSampling Distributions and Confidence Intervals
1 6 Samplig Distributios ad Cofidece Itervals Iferetial statistics to make coclusios about a large set of data called the populatio, based o a subset of the data, called the sample. 6.1 Samplig Distributios
More informationA New Proposed Statistically-Derived Compromise Cut-off CD4 Count Value for HIV Patients to Start using ARVs
Amerca Joural of Appled Sceces Orgal Research Paper A New Proposed Statstcally-Derved Compromse Cut-off CD4 Cout Value for HIV Patets to Start usg ARVs Mara Mokgad Lekgayae ad Solly Matshosa Seeletse Departmet
More informationA New Method To Improve Movement Tracking Of Human Sperms
IAENG Iteratoal Joural of Computer Scece, 45:4, IJCS_45_4_05 A New Method To Improve Movemet Trackg Of Huma Sperms I Gede Susrama Masdyasa, Member, IAENG, I Ketut Eddy Purama, Maurdh Hery Puromo Abstract
More informationPitch Estimation Enhancement Employing Neural Network- Based Music Prediction
Ptch Estmato Ehacemet Employg eural etwor- Based Musc Predcto Mare Szczerba & Adrze Czyżews Soud & Vso Egeerg Departmet Techcal Uversty of Gdańs ul. arutowcza /, PL-895 Gdańs, Polad tel. +48 (58) 347 3
More informationST Variability Analysis using Triangular Method, Linear Regression and SVM. S. Thulasi Prasad, Dr. S. Varadarajan
Iteratoal Joural of Scetfc & Egeerg Research, Volume 6, Issue, October-25 ST Varablty Aalyss usg Tragular Method, Lear Regresso ad SVM S. Thulas Prasad, Dr. S. Varadaraja Abstract The Cardo vascular dseases
More informationMOVIFIT MOVIFIT MC for controlling MOVIMOT drives
MC for cotrollg MOVIMOT drves P f Hz MOVIFIT. MOVIFIT MC for cotrollg MOVIMOT drves The followg fgure shows a MOVIFIT MC ut wth three coected MOVIMOT helcal gearmotors: 0 AXX.. Features of MOVIFIT MC Up
More informationTechnical Assistance Document Algebra I Standard of Learning A.9
Techical Assistace Documet 2009 Algebra I Stadard of Learig A.9 Ackowledgemets The Virgiia Departmet of Educatio wishes to express sicere thaks to J. Patrick Liter, Doa Meeks, Dr. Marcia Perry, Amy Siepka,
More informationINCOME, EDUCATION AND AGE EFFECTS ON MEAT AND FISH DEMAND IN TUNISIA
Iteratoal Joural of FoodadAgrculturalEcoomcs ISSN 2147-8988 Vol. 1 No. 2 pp. 1-12 INCOME, EDUCATION AND AGE EFFECTS ON MEAT AND FISH DEMAND IN TUNISIA Mohamed Zed Dhraef Laboratory of Agrcultural Ecoomcs,
More informationChapter 8 Student Lecture Notes 8-1
Chapter 8 tudet Lecture Notes 8-1 Basic Busiess tatistics (9 th Editio) Chapter 8 Cofidece Iterval Estimatio 004 Pretice-Hall, Ic. Chap 8-1 Chapter Topics Estimatio Process Poit Estimates Iterval Estimates
More informationLecture Outline. BIOST 514/517 Biostatistics I / Applied Biostatistics I. Paradigm of Statistics. Inferential Statistic.
BIOST 514/517 Biostatistics I / Applied Biostatistics I Kathlee Kerr, Ph.D. Associate Professor of Biostatistics iversity of Washigto Lecture 11: Properties of Estimates; Cofidece Itervals; Stadard Errors;
More informationPDF hosted at the Radboud Repository of the Radboud University Nijmegen
PDF hosted at the Radboud Repostory of the Radboud Uversty Njmege The followg full text s a publsher's verso. For addtoal formato about ths publcato clck ths lk. http://hdl.hadle.et/066/449 Please be advsed
More informationSample Size Determination
Distributio of differece betwee sample meas Vijar Føebø Distributio of differece betwee two sample meas. Your variable is: ( x x ) Differece betwee sample meas The statistical test to be used would be:
More informationOntology Mapping: An Information Retrieval and Interactive Activation Network Based Approach
Otology Mappg: A Iformato Retreval ad Iteractve Actvato Network Based Approach Mg Mao School of Iformato Sceces Uversty of Pttsburgh mgmao@mal.ss.ptt.edu Abstract. Otology mappg s to fd sematc correspodeces
More informationE. Sforza and E. Lugaresi. Sleep Center, Institute of Neurology, University of Bologna, Bologna, Italy
leep, 18(3): 195-2 I 1995 Aerca leep Dsorders Assocato ad leep Research ocety Dayte leepess ad Nasal Cotuous Postve Arway Pressure herapy Obstructve leep Apea ydroe Patets: Effects of Chroc reatet ad I-Nght
More informationChapter 18 - Inference about Means
Chapter 18 - Iferece about Mea December 1, 2014 I Chapter 16-17, we leared how to do cofidece iterval ad tet hypothei for proportio. I thi chapter we will do the ame for mea. 18.1 The Cetral Limit Theorem
More informationNote: This copy is for your personal, non-commercial use only. To order presentation-ready copies for distribution to your colleagues or clients, cont
Note: Ths copy s for your persoal, o-commercal use oly. To order presetato-ready copes for dstrbuto to your colleagues or clets, cotact us at www.rsa.org/rsarghts. Thorste Persgehl, MD Alexader Wall, MD
More informationUsing the Perpendicular Distance to the Nearest Fracture as a Proxy for Conventional Fracture Spacing Measures
Usng the Perpendcular Dstance to the Nearest Fracture as a Proxy for Conventonal Fracture Spacng Measures Erc B. Nven and Clayton V. Deutsch Dscrete fracture network smulaton ams to reproduce dstrbutons
More informationAn assessment of heavy metals in red sand dunes near Bhimili of Visakhapatnam
Avalable ole at www.scholarsresearchlbrary.com Archves of Appled Scece Research, 2013, 5 (1):251-258 (http://scholarsresearchlbrary.com/archve.html) ISSN 0975-508X CODEN (USA) AASRC9 A assessmet of heavy
More informationInternational Journal of Environmental Science and Development, Vol. 4, No. 4, August assessment,
Iteratoal Joural of Evrometal Scece ad Developmet, Vol. 4, No. 4, August 01 The Use of Epdemologcal Studes for Health Rsk Assessmet Kev Fog-Rey Lu, We-Ru Che, Yu-Che Yeh, L-We Chag, ad Yug-Shue She estmate
More informationIntro to Scientific Analysis (BIO 100) THE t-test. Plant Height (m)
THE t-test Let Start With a Example Whe coductig experimet, we would like to kow whether a experimetal treatmet had a effect o ome variable. A a imple but itructive example, uppoe we wat to kow whether
More informationCompetition, extinction, and the sexuality of species
A. Zool. Fec 38: 315 330 ISSN 0003-455X Helsk 2001 Fsh Zoologcal ad Botacal Publshg Board 2001 Dedcated to the memory of W. D. Hamlto Competto, extcto, ad the sexualty of speces Waye M. Getz Departmet
More informationStatistics for Managers Using Microsoft Excel Chapter 7 Confidence Interval Estimation
Statistics for Maagers Usig Microsoft Excel Chapter 7 Cofidece Iterval Estimatio 1999 Pretice-Hall, Ic. Chap. 7-1 Chapter Topics Cofidece Iterval Estimatio for the Mea (s Kow) Cofidece Iterval Estimatio
More informationDEA with Missing Data: An Interval Data Assignment Approach
Joual of Optmzato dustal Egeeg 17 (2015) 31-36 wth Mssg Data: A teval Data Assgmet Appoach Reza Kazem Mat a,*, Roza Azz b a Assocate Pofesso, Depatmet of Mathematcs, slamc Azad Uvesty, Kaa Bach, Kaa, a
More informationEstimating Means with Confidence
Today: Chapter, cofidece iterval for mea Aoucemet Ueful ummary table: Samplig ditributio: p. 353 Cofidece iterval: p. 439 Hypothei tet: p. 534 Homework aiged today ad Wed, due Friday. Fial exam eat aigmet
More informationSexuality and chronic kidney disease
Sexuality ad chroic kidey disease T H E K I D N E Y F O U N D A T I O N O F C A N A D A 1 Sexuality ad chroic kidey disease Let s talk about it Sexuality is a vital part of us all. It icludes may aspects
More informationNoise Reduction of Steel Cord Conveyor Belt Defect Electromagnetic Signal by Combined Use of Improved Wavelet and EMD
algorthms Artcle Noe Reducto Steel Cord Coveyor Belt Defect Electromagetc Sgal by Combed Use Improved Wavelet Hog-We Ma, Hog-We Fa *, Qg-Hua Mao *, Xu-Hu Zhag Wag Xg School Mechacal Egeerg, X a Uversty
More informationPlasma Fibrinopeptide A, /?-Thromboglobulin, and Platelet Factor 4 in Diabetic Retinopathy
Ivestgatve Ophthalmology & Vsual Scece, Vol., No., Jue 1988 Copyrght Assocato for Research Vso ad Ophthalmology Plasma Fbropeptde A, /?-Thromboglobul, ad Platelet Factor Dabetc Retopathy Moque 5. Roy,*
More informationShould We Care How Long to Publish? Investigating the Correlation between Publishing Delay and Journal Impact Factor 1
Should We Care How Log to Publish? Ivestigatig the Correlatio betwee Publishig Delay ad Joural Impact Factor 1 Jie Xu 1, Jiayu Wag 1, Yuaxiag Zeg 2 1 School of Iformatio Maagemet, Wuha Uiversity, Hubei,
More informationAverages and Variation
Chapter 3 Averages and Variation Name Section 3.1 Measures of Central Tendency: Mode, Median, and Mean Objective: In this lesson you learned how to compute, interpret, and explain mean, median, and mode.
More information5.1 Description of characteristics of population Bivariate analysis Stratified analysis
Chapter 5 Results Page umbers 5.1 Descriptio of characteristics of populatio 121-123 5.2 Bivariate aalysis 123-131 5.3 Stratified aalysis 131-133 5.4 Multivariate aalysis 134-135 5.5 Estimatio of Attributable
More informationInvestigation of non-covalent complexes of glutathione with common amino acids by electrospray ionization mass spectrometry 1
Acta harmacol S 8 Ju; 9 6: 759 77 Full-legth artcle Ivestgato of o-covalet complexes of glutathoe wth commo amo acds by electrospray ozato mass spectrometry Zhao-yu DAI,4, Ya-qu CHU,4, Bo WU, ag WU, Chua-fa
More informationINTERNATIONAL STUDENT TIMETABLE SYDNEY CAMPUS
INTERNATIONAL STUDENT TIMETABLE KEY TERM DATES Term Iductio Day Term Dates Public Holidays Studet Fees 2013/14 Holiday Periods* Commece Util* Commece Util ALL studets 1, 2014 Fri 24 Ja Fri 24 Ja Su 6 Apr
More informationA New Multi-objective Optimization Model for Diet Planning of Diabetes Patients under Uncertainty
Health Educato ad Health Promoto (HEHP) (7) Vol. () A New Mult-obectve Optmzato Model for Det Plag of Dabetes Patets uder Ucertaty Maryam Eghbal-arch, Reza Tavakkol-Moghaddam, Fatemeh Esfahaa, Amr Azaro
More informationl A data structure representing a list l A series of dynamically allocated nodes l A separate pointer (the head) points to the first
Liked Lists Week 8 Gaddis: Chater 17 CS 5301 Srig 2018 Itroductio to Liked Lists l A data structure reresetig a list l A series of dyamically allocated odes chaied together i sequece - Each ode oits to
More informationVariability. After reading this chapter, you should be able to do the following:
LEARIG OBJECTIVES C H A P T E R 3 Variability After reading this chapter, you should be able to do the following: Explain what the standard deviation measures Compute the variance and the standard deviation
More informationAn Improved Double Sampling Regression Type Estimator of Population Mean Using Auxiliary Information
ITRATIOAL RARH JOURAL OF MULTIDIIPLIAR TUDI Vol., Issu 7, Jul, 7 I (Ol): 5-899 Impact Factor:.599(GIF),.679(IIF) A Improvd Doubl amplg Rgrsso Tp stmator of Populato Ma Usg Auxlar Iformato Push Msra Dpartmt
More informationSTATISTICAL ANALYSIS & ASTHMATIC PATIENTS IN SULAIMANIYAH GOVERNORATE IN THE TUBER-CLOSES CENTER
March 3. Vol., No. ISSN 37-3 IJRSS & K.A.J. All rights reserved STATISTICAL ANALYSIS & ASTHMATIC PATIENTS IN SULAIMANIYAH GOVERNORATE IN THE TUBER-CLOSES CENTER Dr. Mohammad M. Faqe Hussai (), Asst. Lecturer
More informationComparison of speed and accuracy between manual and computer-aided measurements of dental arch and jaw arch lengths in study model casts
Compariso of speed ad accuracy betwee maual ad computeraided measuremets (Diah Wibisoo, et.al.) Compariso of speed ad accuracy betwee maual ad computeraided measuremets of detal arch ad jaw arch legths
More informationChem 135: First Midterm
Chem 135: First Midterm September 30 th, 2013 Please provide all aswers i the spaces provided. You are ot allowed to use a calculator for this exam, but you may use (previously disassembled) molecular
More informationGamma-Normal-Gamma Mixture Model for Detecting Differentially Methylated Loci in Three Breast Cancer Cell Lines
Gamma-Normal-Gamma Mxture Model for Detectg Dfferetally Methylated Loc Three Breast Cacer Cell Les ORIGINAL RESEARCH Abbas Khall, Dust Potter 2,5, Pearlly Ya 2, Lag L 3, Joe Gray 4, Tm Huag 2 ad Shl L,5
More information! A data structure representing a list. ! A series of dynamically allocated nodes. ! A separate pointer (the head) points to the first
Liked Lists Week 8 Gaddis: Chater 17 CS 5301 Fall 2015 Itroductio to Liked Lists! A data structure reresetig a list! A series of dyamically allocated odes chaied together i sequece - Each ode oits to oe
More informationParameter Estimates of a Random Regression Test Day Model for First Three Lactation Somatic Cell Scores
Parameter Estmates of a Random Regresson Test Day Model for Frst Three actaton Somatc Cell Scores Z. u, F. Renhardt and R. Reents Unted Datasystems for Anmal Producton (VIT), Hedeweg 1, D-27280 Verden,
More informationObjectives. Types of Statistical Inference. Statistical Inference. Chapter 19 Confidence intervals: Estimating with confidence
Types of Statistical Iferece Chapter 19 Cofidece itervals: The basics Cofidece itervals for estiatig the value of a populatio paraeter Tests of sigificace assesses the evidece for a clai about a populatio.
More informationEconomic Analysis of On-line Music: Choice between On-line and Traditional Music Shops* TOSHIAKI TAKITA
Coferece Proceedgs, the 46 th Cogress of the Europea Regoal Scece Assocato, Volos, Greece, August 30 September 3, 2006 Ecoomc Aalyss of O-le Musc: Choce betwee O-le ad Tradtoal Musc Shops* TOSHIAKI TAKITA
More informationPeople love and sports tourism industry development strategy research
Peope ove ad sports toursm dustry deveopmet strategy research Ya Yu * Isttute of Physca Educato, Xayag Norma Uversty, Xayag, cha Abstract: Sports toursm s a toursm, ot oy combes the eemets of toursm, aso
More informationM e sotheliom a. a UK nursing and inform ation project. Mavis Robinson Project Manager
M e sotheliom a a UK ursig ad iform atio project Mavis Robiso Project Maager Mesotheliom a Mesothelioma is a cacer. It most commoly affects the outer liig of the lugs (the pleura). I over 70% of cases
More informationYour health matters. Practical tips and sources of support
Your health matters Practical tips ad sources of support Your health matters Medicie is a challegig ad stressful professio ad doctors are at particular risk of certai health problems as a result. This
More informationDISTRIBUTION AND PROPERTIES OF SPERMATOZOA IN DIFFERENT FRACTIONS OF SPLIT EJACULATES*
FERTILITY AND STERILITY Copyright 1972 by The Williams & Wilkis Co. Vol. 23, No.4, April 1972 Prited i U.S.A. DISTRIBUTION AND PROPERTIES OF SPERMATOZOA IN DIFFERENT FRACTIONS OF SPLIT EJACULATES* R. ELIASSON,
More information