f f... f 1 n n (ii) Median : It is the value of the middle-most observation(s).

Size: px
Start display at page:

Download "f f... f 1 n n (ii) Median : It is the value of the middle-most observation(s)."

Transcription

1 CHAPTER STATISTICS Pots to Remember :. Facts or fgures, collected wth a defte pupose, are called Data.. Statstcs s the area of study dealg wth the collecto, presetato, aalyss ad terpretato of data.. The data collected by the vestgator hmself wth a defte objectve md are kow as Prmary data.. The data collected by somoe else, other tha the vestgator, are kow as Secodary data.. Ay character whch s capable of takg reversal dfferet values s called a varable.. Each group to whch the raw data are codesed s kow as class-terval. Each class s bouded by two fgures kow as ts lmts. The fgure o the left s lower lmt ad fgure o the rght s upper lmt.. The dfferece betwee true upper lmt ad true lower lmt of a class s kow as ts class-sze.. Md-value of a class (or class mark) = upper lmt lower lmt. Class sze s the dfferece betwee ay two successve class marks (md-values).. The dfferece betwee the maxmum value ad the mmum value of the varable s kow as Rage.. The cout of umber of observatos a partcular class s kow as ts Frequecy.. The data ca be preseted graphcally the form of bar graphs, hstograms ad frequecy polygos.. The three measures of cetral tedecy for a ugrouped data are : () Mea : It s foud by addg all the values of the observatos ad dvdg t by the total umber of observatos. It s deoted by x. x So, x x... x x For a ugrouped frequecy dstrbuto,. fx f x... fx x f f... f fx f () Meda : It s the value of the mddle-most observato(s). If s a odd umber, the meda = value of the MATHEMATICS IX STATISTICS th observato. ad, f s a eve umber, the meda = mea of the values of ad () Mode : The mode s the most frequetly occurrg observato. Emprcal formula for calculatg mode s gve by, Mode = (Meda) (Mea) th th observatos.

2 ILLUSTRATIVE EXAMPLES Example. The relatve humdty ( %) of a certa cty for a moth of 0 days was as follows : () Costruct a grouped frequecy dstrbuto table wth classes -, - etc. () Whch moth or seaso do you thk ths data s about? () What s the rage of ths data? NCERT Soluto. () Frequecy dstrbuto table Relatve humdty ( %) () Moth-Jue or seaso-mosoo () Rage Tally Marks Frequecy STATISTICS MATHEMATICS IX = Maxmum observato mmum observato =.. =. Example. The value of upto 0 decmal places s gve below : Soluto..0 () Lst the dgts from 0 to ad make a frequecy dstrbuto of the dgts after the decmal pot. () What are the most ad the least frequecy occurrg dgts? NCERT () Frequecy dstrbuto table Dgt 0 Tally Marks Frequecy () Most frequecy occurg dgts are ad, ad least occurrg dgt s

3 Example. The followg table gves the lfe tmes of 00 eo lamps: Soluto. Example. Lfe tme ( hours) lamps () Represet the gve formato wth the help of a hstogram. () How may lamps have a lfe tme of more tha 00 hours? () () lamps havg lfe tme more tha 00 hours = + + = 0 NCERT The followg two tables gve the dstrbuto of studets of two sectos accordg to the marks obtaed by them : Secto A Secto B Marks Frequecy Marks Frequecy Represet the marks of the studets of both the sectos o the same graph by frequecy polygo. MATHEMATICS IX STATISTICS

4 Soluto. Requred frquecy polygo s as follows : Example. Draw hstogram of the weekly pocket expeses of studets of a school gve below : Soluto. Weekly pocket expeses ( Rs. ) studets Here, we observe that class tervals are uequal, so we wll frst adjust the frequeces of each class terval. Here, the mmum class sze s. We kow, Adjusted frequecy of a class terval Mmum class sze frequecy of the class classsze The adjusted frequecy of each class terval s gve below : Weekly pocket expeses Frequecy STATISTICS MATHEMATICS IX Adjusted frequecy

5 So, requred hstogram s gve below. Example. I a mathematcs test gve to studets, the followg marks (out of 0) are recorded :,,,,,,, 0,,,, 0,,, 0. Fd the mea, meda ad mode of the above marks. Soluto. () Mea ( x) x () Arragg the data the ascedg order : here,, 0, 0,,,,,,,,, 0,,, =, whch s odd. meda = value of th th observato observato = th observato = () Sce, occurs most frequetly.e. tmes, so mode s. MATHEMATICS IX STATISTICS

6 Example. Fd the mea salary of 0 workers of a factory from the followg table: NCERT Soluto. Example. Salary( Rs. ) x f x Mea ( x) f Salary ( Rs) workers f 0 STATISTICS MATHEMATICS IX No.of workers f 0000 Rs f x As f x 0000 The mea of umbers s. If oe umber s excluded, ther mea s. Fd the excluded umber. Soluto. Here, =, x. x Now, x x 0 So, total of umbers s 0. Let the excluded umber be a. The, total of umbers s 0 a. 0 a Mea of umbers 0 a 0 a = a = the excluded umber s. As. ( Gve, ew mea = )

7 Example. The meda of the observatos,,,, x +, x +, 0,,, arraged ascedg order s. Fd x. Soluto. Here, =. Sce s eve : th th observato observato Meda th observato th observato ( x ) ( x ) x x x = As. Example. Fd the mode for the followg data :,,,,,,,,,,,. Soluto. Arragg the gve data ascedg order:,,,,,,,,,,, Sce, occurs maxmum umber of tmes ( tmes), s the requred mode. PRACTICE EXERCISE. Costruct a frequecy table for the followg ages ( years) of 0 studets usg equal class tervals, oe of them beg -, where s ot cluded.,,,,,,,,,,,,,,,,,,,,,,,,,,,,,. The electrcty blls ( Rs.) of 0 houses a localty are gve below :,, 0,, 0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 0,,, Costruct a grouped frequecy table.. For the followg data of weekly wages ( Rs.) receved by 0 workers a factory, costruct a grouped frequecy dstrbuto table.,, 0, 00, 0,,,,,,,,, 0,, 0, 0,, 0,,,, 0,,,, 0,,, 00. Costruct a frequecy table, wth equal class-tervals from the followg data o the weekly wages ( Rs.) of labourers workg a factory, takg oe of the class-tervals as 0-00 (00 ot cluded). 0,,,, 0,,, 0, 0, 0,,, 00, 0,,,, 0,,,, 0,,, 0. Gve below are two cumulatve frequecy dstrbuto tables. Form a frequecy dstrbuto table for each of these. () () Ages ( years ) Below Below 0 Below 0 Below 0 Below 0 Below 0 persos 0 0 Marks obtaed More tha More tha 0 More tha 0 More tha 0 More tha 0 More tha 0 Studets MATHEMATICS IX STATISTICS 0 0

8 . O a certa day, the temperature a cty was recorded as uder : Tme a.m. a.m. a.m. p.m. p.m. Temperature ( C) 0 Draw a bar graph to represet the above data.. Read the bar graph gve below ad aswer the questos gve below : () What formato s gve by the bar graph? () I whch year was the producto maxmum? () After whch year was there a sudde fall the producto? (v) Fd the rato betwee the maxmum ad mmum producto durg the gve perod.. The table gve below shows the umber of blds a vllage : Age group blds 0 Draw a hstogram to represet the above data.. The followg table shows the average daly eargs of 0 geeral stores a market, durg a certa week. Daly earg ( Rs. ) stores STATISTICS MATHEMATICS IX 0

9 Draw a hstogram to represet the above data.. The followg table gves the heghts of 0 studets of a class. Draw a frequecy polygo to represet ths. Heght ( cm) Studets I a study of dabetc patets a vllage, the followg observatos were oted. Represet the gve data by a frequecy polygo Age ( Years) Patets. Draw a hstogram ad a frequecy polygo o the same graph to represet the followg data : Weght ( cm) Persos Draw a hstogram to represet the followg frequecy dstrbuto. Class Iterval Frequecy MATHEMATICS IX STATISTICS

10 . Draw a hstogram for the marks of studets gve below : Marks Studets. The rus scored by two teams A ad B o the frst 0 balls a crcket match are gve below : Number of balls STATISTICS MATHEMATICS IX Team A 0 0 Team B Represet the data of both the teams o the same graph wth the help of frequecy polygos.. Fd the mea for each of the followg sets of umbers : (),,,,, 0, ().,.,.,.,.,. (),,,,, (v),,,,. Calculate the mea ( x ) for each of the followg dstrbuto : () x f () x f. The followg table shows the umber of accdets met by 0 workers a factory durg a moth : accdets workers 0 Fd the average umber of accdets per workers.. The marks obtaed out of 0 by 0 studets a test are gve below : Marks Studets 0 Calculate the average marks. 0. If the mea of the followg data s., fd the value of p. f 0 0 x p 0 0 0

11 . The average of heght of 0 boys out of a class of 0, s 0 cm. If the average heght of the remag boys s cm, fd the average heght of the whole class.. The average of sx umbers s 0. If the average of frst four s ad that of last three s, fd the fourth umber.. The mea of 0 observatos was calculated as 0. It was foud later o that oe of the observatos was msread as stead of. Fd the corrected mea.. The mea of umbers s. If s subtracted from every umber, what wll be the ew mea?. If x s the mea of observatos x, x,..., x, the prove that ( x x) 0.e. the algebrac sum of devatos from mea s zero.. Fd the meda of followg data : (),,,,,,,, (),,,,,,,. The umbers,,,, x, x +,,,, 0 are arraged ascedg order. If ther meda s, fd the value of x.. Fd the meda of the followg observatos :,,,,,,,, 0,,. If s replaced by ad by the above data, fd the ew meda.. Fd out the mode of the followg data : (),,,,,,,,, ().,.,.,.,.,.,.,.,. 0. Gve below s the umber of pars of shoes of dfferet szes sold a day by the ower of the shop. What s the modal shoe sze? Sze of shoe pars sold MATHEMATICS IX STATISTICS PRACTICE TEST MM : Tme : hour Geeral Istructos : Each questo carry marks.. Three cos were tossed 0 tmes smultaeously. Each tme the umber of heads occurg was oted dow as follows: Prepare a frequecy dstrbuto table for ths data.. A radom survey of the umber of chldre of varous age groups playg a park was foud as follows: Age ( years ) chldre Draw a hstogram to represet the data above

12 . Fd mea ( x ) for the followg dstrbuto: x f 0 0. The followg observatos have bee arraged ascedg order. If the meda of the data s, fd the value of x.,,, 0, x, x +,,,,. The marks obtaed by 0 studets a test are gve. Fd the modal marks () No.of Marks Class Frequecy Class Frequecy studets Weekly Wages ( Rs.) workers Weekly Wages () ( Rs.) workers Age ( year) persos Studets 0 ANSWERS OF PRACTICE EXERCISE Marks obtaed STATISTICS MATHEMATICS IX

13 .. () The gve bar graph shows the producto ( mllo toes) of food gras durg the perod from 000 to 00. () 00 () 000 (v) :.... MATHEMATICS IX STATISTICS

14 STATISTICS MATHEMATICS IX

15 . (). (). (). (v). ().0 ()..... (approx) cm..... () () 0...,. () () ANSWERS OF PRACTICE TEST MATHEMATICS IX STATISTICS

MEASURES OF DISPERSION

MEASURES OF DISPERSION MEASURES OF DISPERSION Measure of Cetral Tedecy: Measures of Cetral Tedecy ad Dsperso ) Mathematcal Average: a) Arthmetc mea (A.M.) b) Geometrc mea (G.M.) c) Harmoc mea (H.M.) ) Averages of Posto: a) Meda

More information

Random Variables and Probability Distributions

Random Variables and Probability Distributions Radom Varables ad Probablty Dstrbutos * If X : S R s a dscrete radom varable wth rage {x, x, x 3,. } the r = P (X = xr ) = * Let X : S R be a dscrete radom varable wth rage {x, x, x 3,.}.If x r P(X = x

More information

CHAPTER VI Statistical Analysis of Experimental Data

CHAPTER VI Statistical Analysis of Experimental Data Chapter VI Statstcal Aalyss of Expermetal Data CHAPTER VI Statstcal Aalyss of Expermetal Data Measuremets do ot lead to a uque value. Ths s a result of the multtude of errors (maly radom errors) that ca

More information

Mean is only appropriate for interval or ratio scales, not ordinal or nominal.

Mean is only appropriate for interval or ratio scales, not ordinal or nominal. Mea Same as ordary average Sum all the data values ad dvde by the sample sze. x = ( x + x +... + x Usg summato otato, we wrte ths as x = x = x = = ) x Mea s oly approprate for terval or rato scales, ot

More information

Section l h l Stem=Tens. 8l Leaf=Ones. 8h l 03. 9h 58

Section l h l Stem=Tens. 8l Leaf=Ones. 8h l 03. 9h 58 Secto.. 6l 34 6h 667899 7l 44 7h Stem=Tes 8l 344 Leaf=Oes 8h 5557899 9l 3 9h 58 Ths dsplay brgs out the gap the data: There are o scores the hgh 7's. 6. a. beams cylders 9 5 8 88533 6 6 98877643 7 488

More information

2.28 The Wall Street Journal is probably referring to the average number of cubes used per glass measured for some population that they have chosen.

2.28 The Wall Street Journal is probably referring to the average number of cubes used per glass measured for some population that they have chosen. .5 x 54.5 a. x 7. 786 7 b. The raked observatos are: 7.4, 7.5, 7.7, 7.8, 7.9, 8.0, 8.. Sce the sample sze 7 s odd, the meda s the (+)/ 4 th raked observato, or meda 7.8 c. The cosumer would more lkely

More information

= 1. UCLA STAT 13 Introduction to Statistical Methods for the Life and Health Sciences. Parameters and Statistics. Measures of Centrality

= 1. UCLA STAT 13 Introduction to Statistical Methods for the Life and Health Sciences. Parameters and Statistics. Measures of Centrality UCLA STAT Itroducto to Statstcal Methods for the Lfe ad Health Sceces Istructor: Ivo Dov, Asst. Prof. of Statstcs ad Neurology Teachg Assstats: Fred Phoa, Krste Johso, Mg Zheg & Matlda Hseh Uversty of

More information

1. The weight of six Golden Retrievers is 66, 61, 70, 67, 92 and 66 pounds. The weight of six Labrador Retrievers is 54, 60, 72, 78, 84 and 67.

1. The weight of six Golden Retrievers is 66, 61, 70, 67, 92 and 66 pounds. The weight of six Labrador Retrievers is 54, 60, 72, 78, 84 and 67. Ecoomcs 3 Itroducto to Ecoometrcs Sprg 004 Professor Dobk Name Studet ID Frst Mdterm Exam You must aswer all the questos. The exam s closed book ad closed otes. You may use your calculators but please

More information

Measures of Central Tendency

Measures of Central Tendency Chapter 6 Measures of Cetral Tedecy Defto of a Summary Measure (page 185) A summary measure s a sgle value that we compute from a collecto of measuremets order to descrbe oe of the collecto s partcular

More information

Handout #1. Title: Foundations of Econometrics. POPULATION vs. SAMPLE

Handout #1. Title: Foundations of Econometrics. POPULATION vs. SAMPLE Hadout #1 Ttle: Foudatos of Ecoometrcs Course: Eco 367 Fall/015 Istructor: Dr. I-Mg Chu POPULATION vs. SAMPLE From the Bureau of Labor web ste (http://www.bls.gov), we ca fd the uemploymet rate for each

More information

Econometric Methods. Review of Estimation

Econometric Methods. Review of Estimation Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators

More information

Statistics Descriptive

Statistics Descriptive Statstcs Descrptve Ma aspects of descrbg a data set (a) Summarzazto ad descrpto of the data (1) Presetato of tables ad graphs (2) Scag the graphed data for ay uusual observatos wch seem to stck far out

More information

Descriptive Statistics

Descriptive Statistics Page Techcal Math II Descrptve Statstcs Descrptve Statstcs Descrptve statstcs s the body of methods used to represet ad summarze sets of data. A descrpto of how a set of measuremets (for eample, people

More information

Class 13,14 June 17, 19, 2015

Class 13,14 June 17, 19, 2015 Class 3,4 Jue 7, 9, 05 Pla for Class3,4:. Samplg dstrbuto of sample mea. The Cetral Lmt Theorem (CLT). Cofdece terval for ukow mea.. Samplg Dstrbuto for Sample mea. Methods used are based o CLT ( Cetral

More information

is the score of the 1 st student, x

is the score of the 1 st student, x 8 Chapter Collectg, Dsplayg, ad Aalyzg your Data. Descrptve Statstcs Sectos explaed how to choose a sample, how to collect ad orgaze data from the sample, ad how to dsplay your data. I ths secto, you wll

More information

Lecture Notes Types of economic variables

Lecture Notes Types of economic variables Lecture Notes 3 1. Types of ecoomc varables () Cotuous varable takes o a cotuum the sample space, such as all pots o a le or all real umbers Example: GDP, Polluto cocetrato, etc. () Dscrete varables fte

More information

Statistics Descriptive and Inferential Statistics. Instructor: Daisuke Nagakura

Statistics Descriptive and Inferential Statistics. Instructor: Daisuke Nagakura Statstcs Descrptve ad Iferetal Statstcs Istructor: Dasuke Nagakura (agakura@z7.keo.jp) 1 Today s topc Today, I talk about two categores of statstcal aalyses, descrptve statstcs ad feretal statstcs, ad

More information

Continuous Distributions

Continuous Distributions 7//3 Cotuous Dstrbutos Radom Varables of the Cotuous Type Desty Curve Percet Desty fucto, f (x) A smooth curve that ft the dstrbuto 3 4 5 6 7 8 9 Test scores Desty Curve Percet Probablty Desty Fucto, f

More information

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best Error Aalyss Preamble Wheever a measuremet s made, the result followg from that measuremet s always subject to ucertaty The ucertaty ca be reduced by makg several measuremets of the same quatty or by mprovg

More information

hp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations

hp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations HP 30S Statstcs Averages ad Stadard Devatos Average ad Stadard Devato Practce Fdg Averages ad Stadard Devatos HP 30S Statstcs Averages ad Stadard Devatos Average ad stadard devato The HP 30S provdes several

More information

Median as a Weighted Arithmetic Mean of All Sample Observations

Median as a Weighted Arithmetic Mean of All Sample Observations Meda as a Weghted Arthmetc Mea of All Sample Observatos SK Mshra Dept. of Ecoomcs NEHU, Shllog (Ida). Itroducto: Iumerably may textbooks Statstcs explctly meto that oe of the weakesses (or propertes) of

More information

Laboratory I.10 It All Adds Up

Laboratory I.10 It All Adds Up Laboratory I. It All Adds Up Goals The studet wll work wth Rema sums ad evaluate them usg Derve. The studet wll see applcatos of tegrals as accumulatos of chages. The studet wll revew curve fttg sklls.

More information

Summary of the lecture in Biostatistics

Summary of the lecture in Biostatistics Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the

More information

THE ROYAL STATISTICAL SOCIETY HIGHER CERTIFICATE

THE ROYAL STATISTICAL SOCIETY HIGHER CERTIFICATE THE ROYAL STATISTICAL SOCIETY 00 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE PAPER I STATISTICAL THEORY The Socety provdes these solutos to assst caddates preparg for the examatos future years ad for the

More information

Simulation Output Analysis

Simulation Output Analysis Smulato Output Aalyss Summary Examples Parameter Estmato Sample Mea ad Varace Pot ad Iterval Estmato ermatg ad o-ermatg Smulato Mea Square Errors Example: Sgle Server Queueg System x(t) S 4 S 4 S 3 S 5

More information

Lecture 3. Sampling, sampling distributions, and parameter estimation

Lecture 3. Sampling, sampling distributions, and parameter estimation Lecture 3 Samplg, samplg dstrbutos, ad parameter estmato Samplg Defto Populato s defed as the collecto of all the possble observatos of terest. The collecto of observatos we take from the populato s called

More information

Third handout: On the Gini Index

Third handout: On the Gini Index Thrd hadout: O the dex Corrado, a tala statstca, proposed (, 9, 96) to measure absolute equalt va the mea dfferece whch s defed as ( / ) where refers to the total umber of dvduals socet. Assume that. The

More information

Module 7: Probability and Statistics

Module 7: Probability and Statistics Lecture 4: Goodess of ft tests. Itroducto Module 7: Probablty ad Statstcs I the prevous two lectures, the cocepts, steps ad applcatos of Hypotheses testg were dscussed. Hypotheses testg may be used to

More information

Measures of Dispersion

Measures of Dispersion Chapter 8 Measures of Dsperso Defto of Measures of Dsperso (page 31) A measure of dsperso s a descrptve summary measure that helps us characterze the data set terms of how vared the observatos are from

More information

UNIT 1 MEASURES OF CENTRAL TENDENCY

UNIT 1 MEASURES OF CENTRAL TENDENCY UIT MEASURES OF CETRAL TEDECY Measures o Cetral Tedecy Structure Itroducto Objectves Measures o Cetral Tedecy 3 Armetc Mea 4 Weghted Mea 5 Meda 6 Mode 7 Geometrc Mea 8 Harmoc Mea 9 Partto Values Quartles

More information

NATIONAL SENIOR CERTIFICATE GRADE 11

NATIONAL SENIOR CERTIFICATE GRADE 11 NATIONAL SENIOR CERTIFICATE GRADE MATHEMATICS P EXEMPLAR 007 MARKS: 50 TIME: 3 hours Ths questo paper cossts of 9 pages, a sheet of graph paper ad a -page formula sheet. Mathematcs/P INSTRUCTIONS AND INFORMATION

More information

Lesson 3. Group and individual indexes. Design and Data Analysis in Psychology I English group (A) School of Psychology Dpt. Experimental Psychology

Lesson 3. Group and individual indexes. Design and Data Analysis in Psychology I English group (A) School of Psychology Dpt. Experimental Psychology 17/03/015 School of Psychology Dpt. Expermetal Psychology Desg ad Data Aalyss Psychology I Eglsh group (A) Salvador Chacó Moscoso Susaa Saduvete Chaves Mlagrosa Sáchez Martí Lesso 3 Group ad dvdual dexes

More information

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Postpoed exam: ECON430 Statstcs Date of exam: Jauary 0, 0 Tme for exam: 09:00 a.m. :00 oo The problem set covers 5 pages Resources allowed: All wrtte ad prted

More information

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The

More information

Bounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy

Bounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy Bouds o the expected etropy ad KL-dvergece of sampled multomal dstrbutos Brado C. Roy bcroy@meda.mt.edu Orgal: May 18, 2011 Revsed: Jue 6, 2011 Abstract Iformato theoretc quattes calculated from a sampled

More information

PTAS for Bin-Packing

PTAS for Bin-Packing CS 663: Patter Matchg Algorthms Scrbe: Che Jag /9/00. Itroducto PTAS for B-Packg The B-Packg problem s NP-hard. If we use approxmato algorthms, the B-Packg problem could be solved polyomal tme. For example,

More information

UNIVERSITY OF EAST ANGLIA. Main Series UG Examination

UNIVERSITY OF EAST ANGLIA. Main Series UG Examination UNIVERSITY OF EAST ANGLIA School of Ecoomcs Ma Seres UG Examato 03-4 INTRODUCTORY MATHEMATICS AND STATISTICS FOR ECONOMISTS ECO-400Y Tme allowed: 3 hours Aswer ALL questos from both Sectos. Aswer EACH

More information

Midterm Exam 1, section 2 (Solution) Thursday, February hour, 15 minutes

Midterm Exam 1, section 2 (Solution) Thursday, February hour, 15 minutes coometrcs, CON Sa Fracsco State Uverst Mchael Bar Sprg 5 Mdterm xam, secto Soluto Thursda, Februar 6 hour, 5 mutes Name: Istructos. Ths s closed book, closed otes exam.. No calculators of a kd are allowed..

More information

Application of Calibration Approach for Regression Coefficient Estimation under Two-stage Sampling Design

Application of Calibration Approach for Regression Coefficient Estimation under Two-stage Sampling Design Authors: Pradp Basak, Kaustav Adtya, Hukum Chadra ad U.C. Sud Applcato of Calbrato Approach for Regresso Coeffcet Estmato uder Two-stage Samplg Desg Pradp Basak, Kaustav Adtya, Hukum Chadra ad U.C. Sud

More information

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines It J Cotemp Math Sceces, Vol 5, 2010, o 19, 921-929 Solvg Costraed Flow-Shop Schedulg Problems wth Three Maches P Pada ad P Rajedra Departmet of Mathematcs, School of Advaced Sceces, VIT Uversty, Vellore-632

More information

Lecture 1 Review of Fundamental Statistical Concepts

Lecture 1 Review of Fundamental Statistical Concepts Lecture Revew of Fudametal Statstcal Cocepts Measures of Cetral Tedecy ad Dsperso A word about otato for ths class: Idvduals a populato are desgated, where the dex rages from to N, ad N s the total umber

More information

( ) 2 2. Multi-Layer Refraction Problem Rafael Espericueta, Bakersfield College, November, 2006

( ) 2 2. Multi-Layer Refraction Problem Rafael Espericueta, Bakersfield College, November, 2006 Mult-Layer Refracto Problem Rafael Espercueta, Bakersfeld College, November, 006 Lght travels at dfferet speeds through dfferet meda, but refracts at layer boudares order to traverse the least-tme path.

More information

STA 105-M BASIC STATISTICS (This is a multiple choice paper.)

STA 105-M BASIC STATISTICS (This is a multiple choice paper.) DCDM BUSINESS SCHOOL September Mock Eamatos STA 0-M BASIC STATISTICS (Ths s a multple choce paper.) Tme: hours 0 mutes INSTRUCTIONS TO CANDIDATES Do ot ope ths questo paper utl you have bee told to do

More information

GOALS The Samples Why Sample the Population? What is a Probability Sample? Four Most Commonly Used Probability Sampling Methods

GOALS The Samples Why Sample the Population? What is a Probability Sample? Four Most Commonly Used Probability Sampling Methods GOLS. Epla why a sample s the oly feasble way to lear about a populato.. Descrbe methods to select a sample. 3. Defe ad costruct a samplg dstrbuto of the sample mea. 4. Epla the cetral lmt theorem. 5.

More information

F. Inequalities. HKAL Pure Mathematics. 進佳數學團隊 Dr. Herbert Lam 林康榮博士. [Solution] Example Basic properties

F. Inequalities. HKAL Pure Mathematics. 進佳數學團隊 Dr. Herbert Lam 林康榮博士. [Solution] Example Basic properties 進佳數學團隊 Dr. Herbert Lam 林康榮博士 HKAL Pure Mathematcs F. Ieualtes. Basc propertes Theorem Let a, b, c be real umbers. () If a b ad b c, the a c. () If a b ad c 0, the ac bc, but f a b ad c 0, the ac bc. Theorem

More information

1 Onto functions and bijections Applications to Counting

1 Onto functions and bijections Applications to Counting 1 Oto fuctos ad bectos Applcatos to Coutg Now we move o to a ew topc. Defto 1.1 (Surecto. A fucto f : A B s sad to be surectve or oto f for each b B there s some a A so that f(a B. What are examples of

More information

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b CS 70 Dscrete Mathematcs ad Probablty Theory Fall 206 Sesha ad Walrad DIS 0b. Wll I Get My Package? Seaky delvery guy of some compay s out delverg packages to customers. Not oly does he had a radom package

More information

Chapter 14 Logistic Regression Models

Chapter 14 Logistic Regression Models Chapter 4 Logstc Regresso Models I the lear regresso model X β + ε, there are two types of varables explaatory varables X, X,, X k ad study varable y These varables ca be measured o a cotuous scale as

More information

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution: Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed

More information

A Study of the Reproducibility of Measurements with HUR Leg Extension/Curl Research Line

A Study of the Reproducibility of Measurements with HUR Leg Extension/Curl Research Line HUR Techcal Report 000--9 verso.05 / Frak Borg (borgbros@ett.f) A Study of the Reproducblty of Measuremets wth HUR Leg Eteso/Curl Research Le A mportat property of measuremets s that the results should

More information

Quantitative analysis requires : sound knowledge of chemistry : possibility of interferences WHY do we need to use STATISTICS in Anal. Chem.?

Quantitative analysis requires : sound knowledge of chemistry : possibility of interferences WHY do we need to use STATISTICS in Anal. Chem.? Ch 4. Statstcs 4.1 Quattatve aalyss requres : soud kowledge of chemstry : possblty of terfereces WHY do we eed to use STATISTICS Aal. Chem.? ucertaty ests. wll we accept ucertaty always? f ot, from how

More information

STA302/1001-Fall 2008 Midterm Test October 21, 2008

STA302/1001-Fall 2008 Midterm Test October 21, 2008 STA3/-Fall 8 Mdterm Test October, 8 Last Name: Frst Name: Studet Number: Erolled (Crcle oe) STA3 STA INSTRUCTIONS Tme allowed: hour 45 mutes Ads allowed: A o-programmable calculator A table of values from

More information

Summary tables and charts

Summary tables and charts Data Aalyss Summary tables ad charts. Orgazg umercal data: Hstograms ad frequecy tables I ths lecture, we wll study descrptve statstcs. By descrptve statstcs, we refer to methods volvg the collecto, presetato,

More information

Parameter, Statistic and Random Samples

Parameter, Statistic and Random Samples Parameter, Statstc ad Radom Samples A parameter s a umber that descrbes the populato. It s a fxed umber, but practce we do ot kow ts value. A statstc s a fucto of the sample data,.e., t s a quatty whose

More information

Multiple Choice Test. Chapter Adequacy of Models for Regression

Multiple Choice Test. Chapter Adequacy of Models for Regression Multple Choce Test Chapter 06.0 Adequac of Models for Regresso. For a lear regresso model to be cosdered adequate, the percetage of scaled resduals that eed to be the rage [-,] s greater tha or equal to

More information

Chapter 5 Properties of a Random Sample

Chapter 5 Properties of a Random Sample Lecture 6 o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for the prevous lecture Cocepts: t-dstrbuto, F-dstrbuto Theorems: Dstrbutos of sample mea ad sample varace, relatoshp betwee sample mea ad sample

More information

CHAPTER 3 POSTERIOR DISTRIBUTIONS

CHAPTER 3 POSTERIOR DISTRIBUTIONS CHAPTER 3 POSTERIOR DISTRIBUTIONS If scece caot measure the degree of probablt volved, so much the worse for scece. The practcal ma wll stck to hs apprecatve methods utl t does, or wll accept the results

More information

Special Instructions / Useful Data

Special Instructions / Useful Data JAM 6 Set of all real umbers P A..d. B, p Posso Specal Istructos / Useful Data x,, :,,, x x Probablty of a evet A Idepedetly ad detcally dstrbuted Bomal dstrbuto wth parameters ad p Posso dstrbuto wth

More information

Outline. Point Pattern Analysis Part I. Revisit IRP/CSR

Outline. Point Pattern Analysis Part I. Revisit IRP/CSR Pot Patter Aalyss Part I Outle Revst IRP/CSR, frst- ad secod order effects What s pot patter aalyss (PPA)? Desty-based pot patter measures Dstace-based pot patter measures Revst IRP/CSR Equal probablty:

More information

LECTURE - 4 SIMPLE RANDOM SAMPLING DR. SHALABH DEPARTMENT OF MATHEMATICS AND STATISTICS INDIAN INSTITUTE OF TECHNOLOGY KANPUR

LECTURE - 4 SIMPLE RANDOM SAMPLING DR. SHALABH DEPARTMENT OF MATHEMATICS AND STATISTICS INDIAN INSTITUTE OF TECHNOLOGY KANPUR amplg Theory MODULE II LECTURE - 4 IMPLE RADOM AMPLIG DR. HALABH DEPARTMET OF MATHEMATIC AD TATITIC IDIA ITITUTE OF TECHOLOGY KAPUR Estmato of populato mea ad populato varace Oe of the ma objectves after

More information

Point Estimation: definition of estimators

Point Estimation: definition of estimators Pot Estmato: defto of estmators Pot estmator: ay fucto W (X,..., X ) of a data sample. The exercse of pot estmato s to use partcular fuctos of the data order to estmate certa ukow populato parameters.

More information

STATISTICAL INFERENCE

STATISTICAL INFERENCE (STATISTICS) STATISTICAL INFERENCE COMPLEMENTARY COURSE B.Sc. MATHEMATICS III SEMESTER ( Admsso) UNIVERSITY OF CALICUT SCHOOL OF DISTANCE EDUCATION CALICUT UNIVERSITY P.O., MALAPPURAM, KERALA, INDIA -

More information

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971))

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971)) art 4b Asymptotc Results for MRR usg RESS Recall that the RESS statstc s a specal type of cross valdato procedure (see Alle (97)) partcular to the regresso problem ad volves fdg Y $,, the estmate at the

More information

Can we take the Mysticism Out of the Pearson Coefficient of Linear Correlation?

Can we take the Mysticism Out of the Pearson Coefficient of Linear Correlation? Ca we tae the Mstcsm Out of the Pearso Coeffcet of Lear Correlato? Itroducto As the ttle of ths tutoral dcates, our purpose s to egeder a clear uderstadg of the Pearso coeffcet of lear correlato studets

More information

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Exam: ECON430 Statstcs Date of exam: Frday, December 8, 07 Grades are gve: Jauary 4, 08 Tme for exam: 0900 am 00 oo The problem set covers 5 pages Resources allowed:

More information

GENERALIZATIONS OF CEVA S THEOREM AND APPLICATIONS

GENERALIZATIONS OF CEVA S THEOREM AND APPLICATIONS GENERLIZTIONS OF CEV S THEOREM ND PPLICTIONS Floret Smaradache Uversty of New Mexco 200 College Road Gallup, NM 87301, US E-mal: smarad@um.edu I these paragraphs oe presets three geeralzatos of the famous

More information

BAL-001-AB-0a Real Power Balancing Control Performance

BAL-001-AB-0a Real Power Balancing Control Performance Alberta Relablty Stadards Resource ad Demad Balacg BAL-00-AB-0a. Purpose BAL-00-AB-0a Real Power Balacg Cotrol Performace The purpose of ths relablty stadard s to mata WECC steady-state frequecy wth defed

More information

DATE: 21 September, 1999 TO: Jim Russell FROM: Peter Tkacik RE: Analysis of wide ply tube winding as compared to Konva Kore CC: Larry McMillan

DATE: 21 September, 1999 TO: Jim Russell FROM: Peter Tkacik RE: Analysis of wide ply tube winding as compared to Konva Kore CC: Larry McMillan M E M O R A N D U M DATE: 1 September, 1999 TO: Jm Russell FROM: Peter Tkack RE: Aalyss of wde ply tube wdg as compared to Kova Kore CC: Larry McMlla The goal of ths report s to aalyze the spral tube wdg

More information

Midterm Exam 1, section 1 (Solution) Thursday, February hour, 15 minutes

Midterm Exam 1, section 1 (Solution) Thursday, February hour, 15 minutes coometrcs, CON Sa Fracsco State Uversty Mchael Bar Sprg 5 Mdterm am, secto Soluto Thursday, February 6 hour, 5 mutes Name: Istructos. Ths s closed book, closed otes eam.. No calculators of ay kd are allowed..

More information

X X X E[ ] E X E X. is the ()m n where the ( i,)th. j element is the mean of the ( i,)th., then

X X X E[ ] E X E X. is the ()m n where the ( i,)th. j element is the mean of the ( i,)th., then Secto 5 Vectors of Radom Varables Whe workg wth several radom varables,,..., to arrage them vector form x, t s ofte coveet We ca the make use of matrx algebra to help us orgaze ad mapulate large umbers

More information

12.2 Estimating Model parameters Assumptions: ox and y are related according to the simple linear regression model

12.2 Estimating Model parameters Assumptions: ox and y are related according to the simple linear regression model 1. Estmatg Model parameters Assumptos: ox ad y are related accordg to the smple lear regresso model (The lear regresso model s the model that says that x ad y are related a lear fasho, but the observed

More information

St John s College. Preliminary Examinations July 2014 Mathematics Paper 1. Examiner: G Evans Time: 3 hrs Moderator: D Grigoratos Marks: 150

St John s College. Preliminary Examinations July 2014 Mathematics Paper 1. Examiner: G Evans Time: 3 hrs Moderator: D Grigoratos Marks: 150 St Joh s College Prelmar Eamatos Jul 04 Mathematcs Paper Eamer: G Evas Tme: 3 hrs Moderator: D Grgoratos Marks: 50 PLEASE READ THE FOLLOWING INSTRUCTIONS CAREFULLY. Ths questo paper cossts of pages, cludg

More information

ESS Line Fitting

ESS Line Fitting ESS 5 014 17. Le Fttg A very commo problem data aalyss s lookg for relatoshpetwee dfferet parameters ad fttg les or surfaces to data. The smplest example s fttg a straght le ad we wll dscuss that here

More information

Ideal multigrades with trigonometric coefficients

Ideal multigrades with trigonometric coefficients Ideal multgrades wth trgoometrc coeffcets Zarathustra Brady December 13, 010 1 The problem A (, k) multgrade s defed as a par of dstct sets of tegers such that (a 1,..., a ; b 1,..., b ) a j = =1 for all

More information

Maximum Likelihood Estimation

Maximum Likelihood Estimation Marquette Uverst Maxmum Lkelhood Estmato Dael B. Rowe, Ph.D. Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Coprght 08 b Marquette Uverst Maxmum Lkelhood Estmato We have bee sag that ~

More information

THE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA

THE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA THE ROYAL STATISTICAL SOCIETY 3 EXAMINATIONS SOLUTIONS GRADUATE DIPLOMA PAPER I STATISTICAL THEORY & METHODS The Socety provdes these solutos to assst caddates preparg for the examatos future years ad

More information

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions Iteratoal Joural of Computatoal Egeerg Research Vol, 0 Issue, Estmato of Stress- Stregth Relablty model usg fte mxture of expoetal dstrbutos K.Sadhya, T.S.Umamaheswar Departmet of Mathematcs, Lal Bhadur

More information

The Selection Problem - Variable Size Decrease/Conquer (Practice with algorithm analysis)

The Selection Problem - Variable Size Decrease/Conquer (Practice with algorithm analysis) We have covered: Selecto, Iserto, Mergesort, Bubblesort, Heapsort Next: Selecto the Qucksort The Selecto Problem - Varable Sze Decrease/Coquer (Practce wth algorthm aalyss) Cosder the problem of fdg the

More information

Bayes Estimator for Exponential Distribution with Extension of Jeffery Prior Information

Bayes Estimator for Exponential Distribution with Extension of Jeffery Prior Information Malaysa Joural of Mathematcal Sceces (): 97- (9) Bayes Estmator for Expoetal Dstrbuto wth Exteso of Jeffery Pror Iformato Hadeel Salm Al-Kutub ad Noor Akma Ibrahm Isttute for Mathematcal Research, Uverst

More information

Chapter -2 Simple Random Sampling

Chapter -2 Simple Random Sampling Chapter - Smple Radom Samplg Smple radom samplg (SRS) s a method of selecto of a sample comprsg of umber of samplg uts out of the populato havg umber of samplg uts such that every samplg ut has a equal

More information

Chapter 3 Sampling For Proportions and Percentages

Chapter 3 Sampling For Proportions and Percentages Chapter 3 Samplg For Proportos ad Percetages I may stuatos, the characterstc uder study o whch the observatos are collected are qualtatve ature For example, the resposes of customers may marketg surveys

More information

Chapter 8: Statistical Analysis of Simulated Data

Chapter 8: Statistical Analysis of Simulated Data Marquette Uversty MSCS600 Chapter 8: Statstcal Aalyss of Smulated Data Dael B. Rowe, Ph.D. Departmet of Mathematcs, Statstcs, ad Computer Scece Copyrght 08 by Marquette Uversty MSCS600 Ageda 8. The Sample

More information

Mu Sequences/Series Solutions National Convention 2014

Mu Sequences/Series Solutions National Convention 2014 Mu Sequeces/Seres Solutos Natoal Coveto 04 C 6 E A 6C A 6 B B 7 A D 7 D C 7 A B 8 A B 8 A C 8 E 4 B 9 B 4 E 9 B 4 C 9 E C 0 A A 0 D B 0 C C Usg basc propertes of arthmetc sequeces, we fd a ad bm m We eed

More information

Example: Multiple linear regression. Least squares regression. Repetition: Simple linear regression. Tron Anders Moger

Example: Multiple linear regression. Least squares regression. Repetition: Simple linear regression. Tron Anders Moger Example: Multple lear regresso 5000,00 4000,00 Tro Aders Moger 0.0.007 brthweght 3000,00 000,00 000,00 0,00 50,00 00,00 50,00 00,00 50,00 weght pouds Repetto: Smple lear regresso We defe a model Y = β0

More information

SPECIAL CONSIDERATIONS FOR VOLUMETRIC Z-TEST FOR PROPORTIONS

SPECIAL CONSIDERATIONS FOR VOLUMETRIC Z-TEST FOR PROPORTIONS SPECIAL CONSIDERAIONS FOR VOLUMERIC Z-ES FOR PROPORIONS Oe s stctve reacto to the questo of whether two percetages are sgfcatly dfferet from each other s to treat them as f they were proportos whch the

More information

For combinatorial problems we might need to generate all permutations, combinations, or subsets of a set.

For combinatorial problems we might need to generate all permutations, combinations, or subsets of a set. Addtoal Decrease ad Coquer Algorthms For combatoral problems we mght eed to geerate all permutatos, combatos, or subsets of a set. Geeratg Permutatos If we have a set f elemets: { a 1, a 2, a 3, a } the

More information

Prof. YoginderVerma. Prof. Pankaj Madan Dean- FMS Gurukul Kangri Vishwavidyalaya, Haridwar

Prof. YoginderVerma. Prof. Pankaj Madan Dean- FMS Gurukul Kangri Vishwavidyalaya, Haridwar Paper:5, Quattatve Techques or aagemet Decsos odule:5 easures o Cetral Tedecy: athematcal Averages (A, G, H) Prcpal Ivestgator Co-Prcpal Ivestgator Paper Coordator Cotet Wrter Pro. S P Basal Vce Chacellor

More information

THE ROYAL STATISTICAL SOCIETY 2016 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5

THE ROYAL STATISTICAL SOCIETY 2016 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5 THE ROYAL STATISTICAL SOCIETY 06 EAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5 The Socety s provdg these solutos to assst cadtes preparg for the examatos 07. The solutos are teded as learg ads ad should

More information

THE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA

THE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA THE ROYAL STATISTICAL SOCIETY EXAMINATIONS SOLUTIONS GRADUATE DIPLOMA PAPER II STATISTICAL THEORY & METHODS The Socety provdes these solutos to assst caddates preparg for the examatos future years ad for

More information

Lecture 9: Tolerant Testing

Lecture 9: Tolerant Testing Lecture 9: Tolerat Testg Dael Kae Scrbe: Sakeerth Rao Aprl 4, 07 Abstract I ths lecture we prove a quas lear lower boud o the umber of samples eeded to do tolerat testg for L dstace. Tolerat Testg We have

More information

The internal structure of natural numbers, one method for the definition of large prime numbers, and a factorization test

The internal structure of natural numbers, one method for the definition of large prime numbers, and a factorization test Fal verso The teral structure of atural umbers oe method for the defto of large prme umbers ad a factorzato test Emmaul Maousos APM Isttute for the Advacemet of Physcs ad Mathematcs 3 Poulou str. 53 Athes

More information

b. There appears to be a positive relationship between X and Y; that is, as X increases, so does Y.

b. There appears to be a positive relationship between X and Y; that is, as X increases, so does Y. .46. a. The frst varable (X) s the frst umber the par ad s plotted o the horzotal axs, whle the secod varable (Y) s the secod umber the par ad s plotted o the vertcal axs. The scatterplot s show the fgure

More information

Department of Agricultural Economics. PhD Qualifier Examination. August 2011

Department of Agricultural Economics. PhD Qualifier Examination. August 2011 Departmet of Agrcultural Ecoomcs PhD Qualfer Examato August 0 Istructos: The exam cossts of sx questos You must aswer all questos If you eed a assumpto to complete a questo, state the assumpto clearly

More information

Functions of Random Variables

Functions of Random Variables Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,

More information

PROPERTIES OF GOOD ESTIMATORS

PROPERTIES OF GOOD ESTIMATORS ESTIMATION INTRODUCTION Estmato s the statstcal process of fdg a appromate value for a populato parameter. A populato parameter s a characterstc of the dstrbuto of a populato such as the populato mea,

More information

ENGI 4421 Propagation of Error Page 8-01

ENGI 4421 Propagation of Error Page 8-01 ENGI 441 Propagato of Error Page 8-01 Propagato of Error [Navd Chapter 3; ot Devore] Ay realstc measuremet procedure cotas error. Ay calculatos based o that measuremet wll therefore also cota a error.

More information

(b) By independence, the probability that the string 1011 is received correctly is

(b) By independence, the probability that the string 1011 is received correctly is Soluto to Problem 1.31. (a) Let A be the evet that a 0 s trasmtted. Usg the total probablty theorem, the desred probablty s P(A)(1 ɛ ( 0)+ 1 P(A) ) (1 ɛ 1)=p(1 ɛ 0)+(1 p)(1 ɛ 1). (b) By depedece, the probablty

More information

UNIT 4 SOME OTHER SAMPLING SCHEMES

UNIT 4 SOME OTHER SAMPLING SCHEMES UIT 4 SOE OTHER SAPLIG SCHEES Some Other Samplg Schemes Structure 4. Itroducto Objectves 4. Itroducto to Systematc Samplg 4.3 ethods of Systematc Samplg Lear Systematc Samplg Crcular Systematc Samplg Advatages

More information

Chapter -2 Simple Random Sampling

Chapter -2 Simple Random Sampling Chapter - Smple Radom Samplg Smple radom samplg (SRS) s a method of selecto of a sample comprsg of umber of samplg uts out of the populato havg umber of samplg uts such that every samplg ut has a equal

More information

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions.

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions. Ordary Least Squares egresso. Smple egresso. Algebra ad Assumptos. I ths part of the course we are gog to study a techque for aalysg the lear relatoshp betwee two varables Y ad X. We have pars of observatos

More information