On the Multivariate Analysis of the level of Use of Modern Methods of Family Planning between Northern and Southern Nigeria

Size: px
Start display at page:

Download "On the Multivariate Analysis of the level of Use of Modern Methods of Family Planning between Northern and Southern Nigeria"

Transcription

1 Iteratioal Joural of Sciece: Baic ad Applied Reearch (IJSBAR) ISSN (Prit & Olie) O the Multivariate Aalyi of the level of Ue of Moder Method of Family Plaig betwee Norther ad Souther Nigeria Athoy C. Akpata a*, Idika E. Okorie b a,b Abia State Uiverity Uturu Nigeria a ac_akpa@yahoo.com b i.okorie@yahoo.com Abtract The recetly publihed 03 Natioal Demographic ad Health Survey (NDHS) reult how a great apathy i the ue of moder family plaig method, depite the whoopig fiacial ad material reource beig pluged ito it. Worried by thi iigificat level of ue, the eed to dicer where more of the apathy i (North or South) become ievitable. Thi paper, therefore, tratified the NDHS data o the level of ue of the moder method of Family Plaig ito two, adopted the Hotellig T techique of the Multivariate Aalyi i determiig whether ay igificat differece exit betwee the level of ue i Norther ad Souther part of Nigeria. The reult obtaied how a igificat differece ad a t-tet of the differece of the mea of the two trata how that the level of ue of moder method of Family Plaig i more i the Souther part tha i the Norther part of the coutry. Keyword: Family Plaig; Moder Method; Multivariate Aalyi; Vector Mea; Variace- Covariace Matrix, Hotellig T * Correpodig author. addre: ac_akpa@yahoo.com. 49

2 Iteratioal Joural of Sciece: Baic ad Applied Reearch (IJSBAR)(05) Volume 4, No, pp Itroductio Geerally, accurate data i cetral to the maagemet of iue to improve the quality of ay give populatio. I Nigeria, the Natioal Populatio Commiio (NPC) i tatutorily empowered, through ceue ad urvey, to collate, aalyze ad diemiate demographic data ad co-ordiate ad moitor the atioal populatio policy for utaiable developmet. Stakeholder ad policy maker rely, to a great extet, o the Commiio for eceary atioal developmet data. Oe of uch urvey wa the 03 Nigeria Demographic ad Health Survey (NDHS) whoe reult were recetly releaed with valuable idicator o the health ad livig tadard of the Nigeria people. Exteive iformatio o the level of tred of fertility, family plaig, materal ad child health ad exually tramitted ifectio i the coutry were obtaied from the urvey. The digutig revelatio from the urvey reult with repect to the level of ue of family plaig method eceitated the coveig of The 3 rd Family Plaig Coferece with the theme Bridgig the Gap betwee Kowledge ad Practice of Family Plaig i Nigeria i Abua. Family plaig which ca be ee a a volutary tep take by idividual to prevet, delay or achieve a pregacy ha proved to be a powerful tool i combatig poverty ad reducig materal mortality, epecially if doe effectively. Stake holder i the health ector, epecially family plaig expert were at the Coferece to coider amog other thig the icredible diparity betwee kowledge of Family Plaig i Nigeria which i put at 85% ad the actual practice which i placed at oly 0% []. Although there are moder ad traditioal method of family plaig, we have oly retricted the work o the 8 moder method of family plaig captured i the urvey. They iclude: Pill, IUD, Iectible, Implat, Male codom, Lactatioal ameorrhea method (LAM), Stadard day method ad Other (which iclude Male terilizatio, Female codom ad Diaphragm). Data o the level of practice of thee method were obtaied from the urvey reult [], tratified ito two (North ad South) ad the ubected to further cietific aalyi with a view to fidig out the part of Nigeria ha bee repoible for thi low level of ue of family plaig method. Thi dicovery will, udoubtedly, help durig itervetio by relevat Agecie.. Method Whe we have a igle variable X with two radom ample of value from differet populatio ad let x i deote the value of X i the firt ample for i,,..., ad x i deote the value of X i the ecod ample for i,,...,, the the mea ad variace for the th ample are: x i i () x 50

3 Iteratioal Joural of Sciece: Baic ad Applied Reearch (IJSBAR)(05) Volume 4, No, pp ad ( xi x ) () Aumig ormality for X i both ample, a appropriate tet tatitic for tetig whether the two ample mea are igificatly differet from zero ivolve calculatig t (3) x x p + Where p i the pooled etimate of variace ad i give by p ( ) + ( ) + (4) The determiig whether (3) i igificatly differet from zero, it i compared with the t-ditributio with + degree of freedom. If we go o to ue the above method for each of the 8 moder method of family plaig uder dicuio to determie if ay of thee method appear to have had differet mea value for North ad South, the purpoe of the paper would be defeated. Our iteret i, ot to idividually look at them but, to kow whether the 8 variable (method) coidered together would give a igificat differece betwee North ad South. Coequetly, a Multivariate Aalyi become ievitable ad the Hotellig T tet readily come to mid. Geerally, give p variable X,...,, X X P ad ample with ad. Obtai ample mea vector X ad X ad ample covariace matricec ad C. A pooled etimate, C, of thee matrice i give by: C ( ) C + ( ) + C (5) The the Hotellig T tet-tatitic become: T ( X X ) C ( X X ) + (6) 5

4 Iteratioal Joural of Sciece: Baic ad Applied Reearch (IJSBAR)(05) Volume 4, No, pp Equatio 6 i amed after Harold Hotellig who developed it [3] a a geeralizatio of Studet t- tatitic of Equatio 3. Specifically, the Hotellig T ditributio arie i multivariate tatitic i udertakig tet of the differece betwee the (multivariate) mea of differet populatio, where tet for uivariate problem would make ue of a t-tet [4]. A igificatly large value for thi tatitic i evidet that the mea vector are differet for the ampled populatio. The igificace or iigificace of T i mot imply determied by uig the fact that i the ull hypothei cae of equal populatio mea the traformed tatitic F ( + p ) ( + T ) p (7) Follow a F ditributio with ad ( + p ) p degree of freedom, [5]. 3. Data Aalyi ad Reult The hypothei of iteret become: H : X South X North 0 H : X South X North R Statitical Software [6] ha bee ued to perform aalyi ad the followig reult obtaied: 5

5 Iteratioal Joural of Sciece: Baic ad Applied Reearch (IJSBAR)(05) Volume 4, No, pp

6 Iteratioal Joural of Sciece: Baic ad Applied Reearch (IJSBAR)(05) Volume 4, No, pp Deciio: H 0 i reected ice F calculated > F tabulated. Hece, we coclude that there i a igificat differece betwee the mea level of ue of moder Family Plaig Method i the Souther ad Norther Nigeria. To kow where the level of ue i more, aother imple tet (Welch Two Sample t-tet) i carried out. H 0 : X South X North H : X South < X North Table : Summary of Reult Mea South Mea North t Df p-value Statitic Deciio: The iformatio i Table how o ufficiet evidece agait the ull hypothei at 5% level of igificace. Thi i becaue of the large p-value of which i greater tha Cocluio The work coidered the recetly publihed 03 Natioal Demographic ad Health Survey (NDHS) data o the level of ue of moder family plaig method. Stratified the data ito two -Norther ad Souther Nigeria ad adopted the Hotellig T techique of the Multivariate Aalyi i determiig whether ay igificat differece exit betwee the level of ue i Norther ad Souther Nigeria. The reult obtaied how a igificat differece. Aother tet to how whether the mea level of ue of moder family plaig method i greater i the South tha the North wa carried out ad reult how i Table idicate that the ull hypothei caot be reected. Hece, we coclude that there i more level of ue of the method i the Souther tha i 54

7 Iteratioal Joural of Sciece: Baic ad Applied Reearch (IJSBAR)(05) Volume 4, No, pp the Norther Nigeria. I other word, the Norther part of Nigeria accout more for the geerally low level of ue of moder family plaig method. From the fidig, the paper recommed more itervetio ad advocacy o the level of ue of the moder family plaig method i the Norther part of the coutry. Referece [] Webite, lat date acceed 4\4\05, [] Natioal Populatio Commiio (NPC) Nigeria ad ICF Iteratioal. 04. Nigeria Demographic ad Health Survey, 03. Abua, Nigeria ad Rockville,Marylad, USA. NPC ad ICF Iteratioal. [3] H. Hotellig.93. The geeralizatio of Studet ratio Aal of Mathematical Statitic (3): [4] Webite, lat date acceed 4\4\05, http: //e.wikipedia.org/wiki/hotellig _T_quared_ditributio [5] C. Chartfield ad A.J. Colli Itroductio to Multivariate Aalyi. Chapma ad Hall, New York. [6] R Developmet Core Team R: A laguage ad eviromet for tatitical computig // R Foudatio for Statitical Computig. - Viea, Autria : [..],

Chapter 9. Key Ideas Hypothesis Test (Two Populations)

Chapter 9. Key Ideas Hypothesis Test (Two Populations) Chapter 9 Key Idea Hypothei Tet (Two Populatio) Sectio 9-: Overview I Chapter 8, dicuio cetered aroud hypothei tet for the proportio, mea, ad tadard deviatio/variace of a igle populatio. However, ofte

More information

TESTS OF SIGNIFICANCE

TESTS OF SIGNIFICANCE TESTS OF SIGNIFICANCE Seema Jaggi I.A.S.R.I., Library Aveue, New Delhi eema@iari.re.i I applied ivetigatio, oe i ofte itereted i comparig ome characteritic (uch a the mea, the variace or a meaure of aociatio

More information

Comments on Discussion Sheet 18 and Worksheet 18 ( ) An Introduction to Hypothesis Testing

Comments on Discussion Sheet 18 and Worksheet 18 ( ) An Introduction to Hypothesis Testing Commet o Dicuio Sheet 18 ad Workheet 18 ( 9.5-9.7) A Itroductio to Hypothei Tetig Dicuio Sheet 18 A Itroductio to Hypothei Tetig We have tudied cofidece iterval for a while ow. Thee are method that allow

More information

x z Increasing the size of the sample increases the power (reduces the probability of a Type II error) when the significance level remains fixed.

x z Increasing the size of the sample increases the power (reduces the probability of a Type II error) when the significance level remains fixed. ] z-tet for the mea, μ If the P-value i a mall or maller tha a pecified value, the data are tatitically igificat at igificace level. Sigificace tet for the hypothei H 0: = 0 cocerig the ukow mea of a populatio

More information

Statistical Inference Procedures

Statistical Inference Procedures Statitical Iferece Procedure Cofidece Iterval Hypothei Tet Statitical iferece produce awer to pecific quetio about the populatio of iteret baed o the iformatio i a ample. Iferece procedure mut iclude a

More information

COMPARISONS INVOLVING TWO SAMPLE MEANS. Two-tail tests have these types of hypotheses: H A : 1 2

COMPARISONS INVOLVING TWO SAMPLE MEANS. Two-tail tests have these types of hypotheses: H A : 1 2 Tetig Hypothee COMPARISONS INVOLVING TWO SAMPLE MEANS Two type of hypothee:. H o : Null Hypothei - hypothei of o differece. or 0. H A : Alterate Hypothei hypothei of differece. or 0 Two-tail v. Oe-tail

More information

Statistics and Chemical Measurements: Quantifying Uncertainty. Normal or Gaussian Distribution The Bell Curve

Statistics and Chemical Measurements: Quantifying Uncertainty. Normal or Gaussian Distribution The Bell Curve Statitic ad Chemical Meauremet: Quatifyig Ucertaity The bottom lie: Do we trut our reult? Should we (or ayoe ele)? Why? What i Quality Aurace? What i Quality Cotrol? Normal or Gauia Ditributio The Bell

More information

M227 Chapter 9 Section 1 Testing Two Parameters: Means, Variances, Proportions

M227 Chapter 9 Section 1 Testing Two Parameters: Means, Variances, Proportions M7 Chapter 9 Sectio 1 OBJECTIVES Tet two mea with idepedet ample whe populatio variace are kow. Tet two variace with idepedet ample. Tet two mea with idepedet ample whe populatio variace are equal Tet

More information

STUDENT S t-distribution AND CONFIDENCE INTERVALS OF THE MEAN ( )

STUDENT S t-distribution AND CONFIDENCE INTERVALS OF THE MEAN ( ) STUDENT S t-distribution AND CONFIDENCE INTERVALS OF THE MEAN Suppoe that we have a ample of meaured value x1, x, x3,, x of a igle uow quatity. Aumig that the meauremet are draw from a ormal ditributio

More information

REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION

REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION I liear regreio, we coider the frequecy ditributio of oe variable (Y) at each of everal level of a ecod variable (X). Y i kow a the depedet variable.

More information

Applied Mathematical Sciences, Vol. 9, 2015, no. 3, HIKARI Ltd,

Applied Mathematical Sciences, Vol. 9, 2015, no. 3, HIKARI Ltd, Applied Mathematical Sciece Vol 9 5 o 3 7 - HIKARI Ltd wwwm-hiaricom http://dxdoiorg/988/am54884 O Poitive Defiite Solutio of the Noliear Matrix Equatio * A A I Saa'a A Zarea* Mathematical Sciece Departmet

More information

LECTURE 13 SIMULTANEOUS EQUATIONS

LECTURE 13 SIMULTANEOUS EQUATIONS NOVEMBER 5, 26 Demad-upply ytem LETURE 3 SIMULTNEOUS EQUTIONS I thi lecture, we dicu edogeeity problem that arie due to imultaeity, i.e. the left-had ide variable ad ome of the right-had ide variable are

More information

Difference tests (1): parametric

Difference tests (1): parametric NST B Eperimetal Pychology Statitic practical Differece tet (): parametric Rudolf Cardial & Mike Aitke / 3 December 003; Departmet of Eperimetal Pychology Uiverity of Cambridge Hadout: Awer to Eample (from

More information

VIII. Interval Estimation A. A Few Important Definitions (Including Some Reminders)

VIII. Interval Estimation A. A Few Important Definitions (Including Some Reminders) VIII. Iterval Etimatio A. A Few Importat Defiitio (Icludig Some Remider) 1. Poit Etimate - a igle umerical value ued a a etimate of a parameter.. Poit Etimator - the ample tatitic that provide the poit

More information

Formula Sheet. December 8, 2011

Formula Sheet. December 8, 2011 Formula Sheet December 8, 2011 Abtract I type thi for your coveice. There may be error. Ue at your ow rik. It i your repoible to check it i correct or ot before uig it. 1 Decriptive Statitic 1.1 Cetral

More information

UNIVERSITY OF CALICUT

UNIVERSITY OF CALICUT Samplig Ditributio 1 UNIVERSITY OF CALICUT SCHOOL OF DISTANCE EDUCATION BSc. MATHEMATICS COMPLEMENTARY COURSE CUCBCSS 2014 Admiio oward III Semeter STATISTICAL INFERENCE Quetio Bak 1. The umber of poible

More information

Tools Hypothesis Tests

Tools Hypothesis Tests Tool Hypothei Tet The Tool meu provide acce to a Hypothei Tet procedure that calculate cofidece iterval ad perform hypothei tet for mea, variace, rate ad proportio. It i cotrolled by the dialog box how

More information

STA 4032 Final Exam Formula Sheet

STA 4032 Final Exam Formula Sheet Chapter 2. Probability STA 4032 Fial Eam Formula Sheet Some Baic Probability Formula: (1) P (A B) = P (A) + P (B) P (A B). (2) P (A ) = 1 P (A) ( A i the complemet of A). (3) If S i a fiite ample pace

More information

20. CONFIDENCE INTERVALS FOR THE MEAN, UNKNOWN VARIANCE

20. CONFIDENCE INTERVALS FOR THE MEAN, UNKNOWN VARIANCE 20. CONFIDENCE INTERVALS FOR THE MEAN, UNKNOWN VARIANCE If the populatio tadard deviatio σ i ukow, a it uually will be i practice, we will have to etimate it by the ample tadard deviatio. Sice σ i ukow,

More information

18.05 Problem Set 9, Spring 2014 Solutions

18.05 Problem Set 9, Spring 2014 Solutions 18.05 Problem Set 9, Sprig 2014 Solutio Problem 1. (10 pt.) (a) We have x biomial(, θ), o E(X) =θ ad Var(X) = θ(1 θ). The rule-of-thumb variace i jut 4. So the ditributio beig plotted are biomial(250,

More information

Chapter 9: Hypothesis Testing

Chapter 9: Hypothesis Testing Chapter 9: Hypothei Tetig Chapter 5 dicued the cocept of amplig ditributio ad Chapter 8 dicued how populatio parameter ca be etimated from a ample. 9. Baic cocept Hypothei Tetig We begi by makig a tatemet,

More information

Statistics Parameters

Statistics Parameters Saplig Ditributio & Cofidece Iterval Etiator Statitical Iferece Etiatio Tetig Hypothei Statitic Ued to Etiate Populatio Paraeter Statitic Saple Mea, Saple Variace, Saple Proportio, Paraeter populatio ea

More information

Generalized Likelihood Functions and Random Measures

Generalized Likelihood Functions and Random Measures Pure Mathematical Sciece, Vol. 3, 2014, o. 2, 87-95 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/pm.2014.437 Geeralized Likelihood Fuctio ad Radom Meaure Chrito E. Koutzaki Departmet of Mathematic

More information

SOLUTION: The 95% confidence interval for the population mean µ is x ± t 0.025; 49

SOLUTION: The 95% confidence interval for the population mean µ is x ± t 0.025; 49 C22.0103 Sprig 2011 Homework 7 olutio 1. Baed o a ample of 50 x-value havig mea 35.36 ad tadard deviatio 4.26, fid a 95% cofidece iterval for the populatio mea. SOLUTION: The 95% cofidece iterval for the

More information

ON THE SCALE PARAMETER OF EXPONENTIAL DISTRIBUTION

ON THE SCALE PARAMETER OF EXPONENTIAL DISTRIBUTION Review of the Air Force Academy No. (34)/7 ON THE SCALE PARAMETER OF EXPONENTIAL DISTRIBUTION Aca Ileaa LUPAŞ Military Techical Academy, Bucharet, Romaia (lua_a@yahoo.com) DOI:.96/84-938.7.5..6 Abtract:

More information

IntroEcono. Discrete RV. Continuous RV s

IntroEcono. Discrete RV. Continuous RV s ItroEcoo Aoc. Prof. Poga Porchaiwiekul, Ph.D... ก ก e-mail: Poga.P@chula.ac.th Homepage: http://pioeer.chula.ac.th/~ppoga (c) Poga Porchaiwiekul, Chulalogkor Uiverity Quatitative, e.g., icome, raifall

More information

Statistical Inference for Two Samples. Applied Statistics and Probability for Engineers. Chapter 10 Statistical Inference for Two Samples

Statistical Inference for Two Samples. Applied Statistics and Probability for Engineers. Chapter 10 Statistical Inference for Two Samples 4/3/6 Applied Statitic ad Probability for Egieer Sixth Editio Dougla C. Motgomery George C. Ruger Chapter Statitical Iferece for Two Sample Copyright 4 Joh Wiley & So, Ic. All right reerved. CHAPTER OUTLINE

More information

STRONG DEVIATION THEOREMS FOR THE SEQUENCE OF CONTINUOUS RANDOM VARIABLES AND THE APPROACH OF LAPLACE TRANSFORM

STRONG DEVIATION THEOREMS FOR THE SEQUENCE OF CONTINUOUS RANDOM VARIABLES AND THE APPROACH OF LAPLACE TRANSFORM Joural of Statitic: Advace i Theory ad Applicatio Volume, Number, 9, Page 35-47 STRONG DEVIATION THEORES FOR THE SEQUENCE OF CONTINUOUS RANDO VARIABLES AND THE APPROACH OF LAPLACE TRANSFOR School of athematic

More information

MATHEMATICS LW Quantitative Methods II Martin Huard Friday April 26, 2013 TEST # 4 SOLUTIONS

MATHEMATICS LW Quantitative Methods II Martin Huard Friday April 26, 2013 TEST # 4 SOLUTIONS ATHATICS 360-55-L Quatitative ethod II arti Huard Friday April 6, 013 TST # 4 SOLUTIONS Name: Awer all quetio ad how all your work. Quetio 1 (10 poit) To oberve the effect drikig a Red Bull ha o cocetratio,

More information

Chapter 13, Part A Analysis of Variance and Experimental Design

Chapter 13, Part A Analysis of Variance and Experimental Design Slides Prepared by JOHN S. LOUCKS St. Edward s Uiversity Slide 1 Chapter 13, Part A Aalysis of Variace ad Eperimetal Desig Itroductio to Aalysis of Variace Aalysis of Variace: Testig for the Equality of

More information

m = Statistical Inference Estimators Sampling Distribution of Mean (Parameters) Sampling Distribution s = Sampling Distribution & Confidence Interval

m = Statistical Inference Estimators Sampling Distribution of Mean (Parameters) Sampling Distribution s = Sampling Distribution & Confidence Interval Saplig Ditributio & Cofidece Iterval Uivariate Aalyi for a Nueric Variable (or a Nueric Populatio) Statitical Iferece Etiatio Tetig Hypothei Weight N ( =?, =?) 1 Uivariate Aalyi for a Categorical Variable

More information

Statistical treatment of test results

Statistical treatment of test results SCAN-G :07 Revied 007 Pulp, paper ad board Statitical treatmet of tet reult 0 Itroductio The value of tatitical method lie i the fact that they make it poible to iterpret tet reult accordig to trictly

More information

Investigating the Significance of a Correlation Coefficient using Jackknife Estimates

Investigating the Significance of a Correlation Coefficient using Jackknife Estimates Iteratioal Joural of Scieces: Basic ad Applied Research (IJSBAR) ISSN 2307-4531 (Prit & Olie) http://gssrr.org/idex.php?joural=jouralofbasicadapplied ---------------------------------------------------------------------------------------------------------------------------

More information

McNemar s Test and Introduction to ANOVA

McNemar s Test and Introduction to ANOVA McNemar Tet ad Itroductio to ANOVA Recall from the lat lecture o oparametric method we ued the eample of reductio of forced vital capacity. FVC Reduc (ml) Subj Placebo Drug 4 3 80 95 3 75 33 4 54 440 5

More information

Elementary Statistics

Elementary Statistics Two Samle Mea Cha08 Dr. Ghamary Page Elemetary Statitic M. Ghamary, Ph.D. Chater 8 Tet of Hyothei a Cofiece Iterval for Two Samle Two Samle Mea Cha08 Dr. Ghamary Page Tet of Hyothei for Two amle: A Statitical

More information

Chapter 1 ASPECTS OF MUTIVARIATE ANALYSIS

Chapter 1 ASPECTS OF MUTIVARIATE ANALYSIS Chapter ASPECTS OF MUTIVARIATE ANALYSIS. Itroductio Defiitio Wiipedia: Multivariate aalyi MVA i baed o the tatitical priciple of multivariate tatitic which ivolve obervatio ad aalyi of more tha oe tatitical

More information

CHAPTER 6. Confidence Intervals. 6.1 (a) y = 1269; s = 145; n = 8. The standard error of the mean is = s n = = 51.3 ng/gm.

CHAPTER 6. Confidence Intervals. 6.1 (a) y = 1269; s = 145; n = 8. The standard error of the mean is = s n = = 51.3 ng/gm. } CHAPTER 6 Cofidece Iterval 6.1 (a) y = 1269; = 145; = 8. The tadard error of the mea i SE ȳ = = 145 8 = 51.3 g/gm. (b) y = 1269; = 145; = 30. The tadard error of the mea i ȳ = 145 = 26.5 g/gm. 30 6.2

More information

Chapter 8.2. Interval Estimation

Chapter 8.2. Interval Estimation Chapter 8.2. Iterval Etimatio Baic of Cofidece Iterval ad Large Sample Cofidece Iterval 1 Baic Propertie of Cofidece Iterval Aumptio: X 1, X 2,, X are from Normal ditributio with a mea of µ ad tadard deviatio.

More information

Tables and Formulas for Sullivan, Fundamentals of Statistics, 2e Pearson Education, Inc.

Tables and Formulas for Sullivan, Fundamentals of Statistics, 2e Pearson Education, Inc. Table ad Formula for Sulliva, Fudametal of Statitic, e. 008 Pearo Educatio, Ic. CHAPTER Orgaizig ad Summarizig Data Relative frequecy frequecy um of all frequecie Cla midpoit: The um of coecutive lower

More information

11/19/ Chapter 10 Overview. Chapter 10: Two-Sample Inference. + The Big Picture : Inference for Mean Difference Dependent Samples

11/19/ Chapter 10 Overview. Chapter 10: Two-Sample Inference. + The Big Picture : Inference for Mean Difference Dependent Samples /9/0 + + Chapter 0 Overview Dicoverig Statitic Eitio Daiel T. Laroe Chapter 0: Two-Sample Iferece 0. Iferece for Mea Differece Depeet Sample 0. Iferece for Two Iepeet Mea 0.3 Iferece for Two Iepeet Proportio

More information

On the Signed Domination Number of the Cartesian Product of Two Directed Cycles

On the Signed Domination Number of the Cartesian Product of Two Directed Cycles Ope Joural of Dicrete Mathematic, 205, 5, 54-64 Publihed Olie July 205 i SciRe http://wwwcirporg/oural/odm http://dxdoiorg/0426/odm2055005 O the Siged Domiatio Number of the Carteia Product of Two Directed

More information

Confidence Intervals. Confidence Intervals

Confidence Intervals. Confidence Intervals A overview Mot probability ditributio are idexed by oe me parameter. F example, N(µ,σ 2 ) B(, p). I igificace tet, we have ued poit etimat f parameter. F example, f iid Y 1,Y 2,...,Y N(µ,σ 2 ), Ȳ i a poit

More information

Estimation Theory. goavendaño. Estimation Theory

Estimation Theory. goavendaño. Estimation Theory Etimatio Theory Statitical Iferece method by which geeralizatio are made about a populatio Two Major Area of Statitical Iferece. Etimatio a parameter i etablihed baed o the amplig ditributio of a proportio,

More information

Confidence Intervals: Three Views Class 23, Jeremy Orloff and Jonathan Bloom

Confidence Intervals: Three Views Class 23, Jeremy Orloff and Jonathan Bloom Cofidece Iterval: Three View Cla 23, 18.05 Jeremy Orloff ad Joatha Bloom 1 Learig Goal 1. Be able to produce z, t ad χ 2 cofidece iterval baed o the correpodig tadardized tatitic. 2. Be able to ue a hypothei

More information

BIOS 4110: Introduction to Biostatistics. Breheny. Lab #9

BIOS 4110: Introduction to Biostatistics. Breheny. Lab #9 BIOS 4110: Itroductio to Biostatistics Brehey Lab #9 The Cetral Limit Theorem is very importat i the realm of statistics, ad today's lab will explore the applicatio of it i both categorical ad cotiuous

More information

100(1 α)% confidence interval: ( x z ( sample size needed to construct a 100(1 α)% confidence interval with a margin of error of w:

100(1 α)% confidence interval: ( x z ( sample size needed to construct a 100(1 α)% confidence interval with a margin of error of w: Stat 400, ectio 7. Large Sample Cofidece Iterval ote by Tim Pilachowki a Large-Sample Two-ided Cofidece Iterval for a Populatio Mea ectio 7.1 redux The poit etimate for a populatio mea µ will be a ample

More information

Generalized Fibonacci Like Sequence Associated with Fibonacci and Lucas Sequences

Generalized Fibonacci Like Sequence Associated with Fibonacci and Lucas Sequences Turkih Joural of Aalyi ad Number Theory, 4, Vol., No. 6, 33-38 Available olie at http://pub.ciepub.com/tjat//6/9 Sciece ad Educatio Publihig DOI:.69/tjat--6-9 Geeralized Fiboacci Like Sequece Aociated

More information

Chapter 1 Econometrics

Chapter 1 Econometrics Chapter Ecoometric There are o exercie or applicatio i Chapter. 0 Pearo Educatio, Ic. Publihig a Pretice Hall Chapter The Liear Regreio Model There are o exercie or applicatio i Chapter. 0 Pearo Educatio,

More information

Chapter 8 Part 2. Unpaired t-test With Equal Variances With Unequal Variances

Chapter 8 Part 2. Unpaired t-test With Equal Variances With Unequal Variances Chapter 8 Part Upaired t-tet With Equal Variace With Uequal Variace December, 008 Goal: To eplai that the choice of the two ample t-tet deped o whether the ample are depedet or idepedet ad for the idepedet

More information

Erick L. Oberstar Fall 2001 Project: Sidelobe Canceller & GSC 1. Advanced Digital Signal Processing Sidelobe Canceller (Beam Former)

Erick L. Oberstar Fall 2001 Project: Sidelobe Canceller & GSC 1. Advanced Digital Signal Processing Sidelobe Canceller (Beam Former) Erick L. Obertar Fall 001 Project: Sidelobe Caceller & GSC 1 Advaced Digital Sigal Proceig Sidelobe Caceller (Beam Former) Erick L. Obertar 001 Erick L. Obertar Fall 001 Project: Sidelobe Caceller & GSC

More information

Fractional parts and their relations to the values of the Riemann zeta function

Fractional parts and their relations to the values of the Riemann zeta function Arab. J. Math. (08) 7: 8 http://doi.org/0.007/40065-07-084- Arabia Joural of Mathematic Ibrahim M. Alabdulmohi Fractioal part ad their relatio to the value of the Riema zeta fuctio Received: 4 Jauary 07

More information

Lecture 5: Parametric Hypothesis Testing: Comparing Means. GENOME 560, Spring 2016 Doug Fowler, GS

Lecture 5: Parametric Hypothesis Testing: Comparing Means. GENOME 560, Spring 2016 Doug Fowler, GS Lecture 5: Parametric Hypothesis Testig: Comparig Meas GENOME 560, Sprig 2016 Doug Fowler, GS (dfowler@uw.edu) 1 Review from last week What is a cofidece iterval? 2 Review from last week What is a cofidece

More information

Because it tests for differences between multiple pairs of means in one test, it is called an omnibus test.

Because it tests for differences between multiple pairs of means in one test, it is called an omnibus test. Math 308 Sprig 018 Classes 19 ad 0: Aalysis of Variace (ANOVA) Page 1 of 6 Itroductio ANOVA is a statistical procedure for determiig whether three or more sample meas were draw from populatios with equal

More information

On the 2-Domination Number of Complete Grid Graphs

On the 2-Domination Number of Complete Grid Graphs Ope Joural of Dicrete Mathematic, 0,, -0 http://wwwcirporg/oural/odm ISSN Olie: - ISSN Prit: - O the -Domiatio Number of Complete Grid Graph Ramy Shahee, Suhail Mahfud, Khame Almaea Departmet of Mathematic,

More information

CE3502 Environmental Monitoring, Measurements, and Data Analysis (EMMA) Spring 2008 Final Review

CE3502 Environmental Monitoring, Measurements, and Data Analysis (EMMA) Spring 2008 Final Review CE35 Evirometal Moitorig, Meauremet, ad Data Aalyi (EMMA) Sprig 8 Fial Review I. Topic:. Decriptive tatitic: a. Mea, Stadard Deviatio, COV b. Bia (accuracy), preciio, Radom v. ytematic error c. Populatio

More information

Overview. p 2. Chapter 9. Pooled Estimate of. q = 1 p. Notation for Two Proportions. Inferences about Two Proportions. Assumptions

Overview. p 2. Chapter 9. Pooled Estimate of. q = 1 p. Notation for Two Proportions. Inferences about Two Proportions. Assumptions Chapter 9 Slide Ifereces from Two Samples 9- Overview 9- Ifereces about Two Proportios 9- Ifereces about Two Meas: Idepedet Samples 9-4 Ifereces about Matched Pairs 9-5 Comparig Variatio i Two Samples

More information

S T A T R a c h e l L. W e b b, P o r t l a n d S t a t e U n i v e r s i t y P a g e 1. = Population Variance

S T A T R a c h e l L. W e b b, P o r t l a n d S t a t e U n i v e r s i t y P a g e 1. = Population Variance S T A T 4 - R a c h e l L. W e b b, P o r t l a d S t a t e U i v e r i t y P a g e Commo Symbol = Sample Size x = Sample Mea = Sample Stadard Deviatio = Sample Variace pˆ = Sample Proportio r = Sample

More information

Chapter 10: H at alpha of.05. Hypothesis Testing: Additional Topics

Chapter 10: H at alpha of.05. Hypothesis Testing: Additional Topics Chapter 10: pothei Tetig: Additioal Topic 10.1 = 5 paired obervatio with ample mea of 50 ad 60 for populatio 1 ad. Ca ou reject the ull hpothei at a alpha of.05 if a. d = 0, : 0 1 0; : 1 1 0; 10 0 t =

More information

EULER-MACLAURIN SUM FORMULA AND ITS GENERALIZATIONS AND APPLICATIONS

EULER-MACLAURIN SUM FORMULA AND ITS GENERALIZATIONS AND APPLICATIONS EULER-MACLAURI SUM FORMULA AD ITS GEERALIZATIOS AD APPLICATIOS Joe Javier Garcia Moreta Graduate tudet of Phyic at the UPV/EHU (Uiverity of Baque coutry) I Solid State Phyic Addre: Practicate Ada y Grijalba

More information

Common Large/Small Sample Tests 1/55

Common Large/Small Sample Tests 1/55 Commo Large/Small Sample Tests 1/55 Test of Hypothesis for the Mea (σ Kow) Covert sample result ( x) to a z value Hypothesis Tests for µ Cosider the test H :μ = μ H 1 :μ > μ σ Kow (Assume the populatio

More information

Properties and Hypothesis Testing

Properties and Hypothesis Testing Chapter 3 Properties ad Hypothesis Testig 3.1 Types of data The regressio techiques developed i previous chapters ca be applied to three differet kids of data. 1. Cross-sectioal data. 2. Time series data.

More information

2 1. The r.s., of size n2, from population 2 will be. 2 and 2. 2) The two populations are independent. This implies that all of the n1 n2

2 1. The r.s., of size n2, from population 2 will be. 2 and 2. 2) The two populations are independent. This implies that all of the n1 n2 Chapter 8 Comparig Two Treatmets Iferece about Two Populatio Meas We wat to compare the meas of two populatios to see whether they differ. There are two situatios to cosider, as show i the followig examples:

More information

Chapter 8: Estimating with Confidence

Chapter 8: Estimating with Confidence Chapter 8: Estimatig with Cofidece Sectio 8.2 The Practice of Statistics, 4 th editio For AP* STARNES, YATES, MOORE Chapter 8 Estimatig with Cofidece 8.1 Cofidece Itervals: The Basics 8.2 8.3 Estimatig

More information

Soo King Lim Figure 1: Figure 2: Figure 3: Figure 4: Figure 5: Figure 6: Figure 7:

Soo King Lim Figure 1: Figure 2: Figure 3: Figure 4: Figure 5: Figure 6: Figure 7: 0 Multivariate Cotrol Chart 3 Multivariate Normal Distributio 5 Estimatio of the Mea ad Covariace Matrix 6 Hotellig s Cotrol Chart 6 Hotellig s Square 8 Average Value of k Subgroups 0 Example 3 3 Value

More information

Statistics Problem Set - modified July 25, _. d Q w. i n

Statistics Problem Set - modified July 25, _. d Q w. i n Statitic Problem Set - modified July 5, 04 x i x i i x i _ x x _ t d Q w F x x t pooled calculated pooled. f d x x t calculated / /.. f d Kow cocept of Gauia Curve Sytematic Error Idetermiate Error t-tet

More information

ME 410 MECHANICAL ENGINEERING SYSTEMS LABORATORY REGRESSION ANALYSIS

ME 410 MECHANICAL ENGINEERING SYSTEMS LABORATORY REGRESSION ANALYSIS ME 40 MECHANICAL ENGINEERING REGRESSION ANALYSIS Regreio problem deal with the relatiohip betwee the frequec ditributio of oe (depedet) variable ad aother (idepedet) variable() which i (are) held fied

More information

Data Analysis and Statistical Methods Statistics 651

Data Analysis and Statistical Methods Statistics 651 Data Aalysis ad Statistical Methods Statistics 651 http://www.stat.tamu.edu/~suhasii/teachig.html Suhasii Subba Rao Review of testig: Example The admistrator of a ursig home wats to do a time ad motio

More information

Statistical Equations

Statistical Equations Statitical Equatio You are permitted to ue the iformatio o thee page durig your eam. Thee page are ot guarateed to cotai all the iformatio you will eed. If you fid iformatio which you believe hould be

More information

Chapter 13: Tests of Hypothesis Section 13.1 Introduction

Chapter 13: Tests of Hypothesis Section 13.1 Introduction Chapter 13: Tests of Hypothesis Sectio 13.1 Itroductio RECAP: Chapter 1 discussed the Likelihood Ratio Method as a geeral approach to fid good test procedures. Testig for the Normal Mea Example, discussed

More information

Explicit scheme. Fully implicit scheme Notes. Fully implicit scheme Notes. Fully implicit scheme Notes. Notes

Explicit scheme. Fully implicit scheme Notes. Fully implicit scheme Notes. Fully implicit scheme Notes. Notes Explicit cheme So far coidered a fully explicit cheme to umerically olve the diffuio equatio: T + = ( )T + (T+ + T ) () with = κ ( x) Oly table for < / Thi cheme i ometime referred to a FTCS (forward time

More information

TI-83/84 Calculator Instructions for Math Elementary Statistics

TI-83/84 Calculator Instructions for Math Elementary Statistics TI-83/84 Calculator Itructio for Math 34- Elemetary Statitic. Eterig Data: Data oit are tored i Lit o the TI-83/84. If you have't ued the calculator before, you may wat to erae everythig that wa there.

More information

Mathacle PSet Stats, Confidence Intervals and Estimation Level Number Name: Date: Unbiased Estimators So we don t have favorite.

Mathacle PSet Stats, Confidence Intervals and Estimation Level Number Name: Date: Unbiased Estimators So we don t have favorite. PSet ----- Stat, Cofidece Iterval ad Etimatio Ubiaed Etimator So we do t have favorite. IV. CONFIDENCE INTERVAL AND ESTIMATION 4.1. Sigificat Level ad Critical Value z ad The igificat level, ofte deoted

More information

Performance-Based Plastic Design (PBPD) Procedure

Performance-Based Plastic Design (PBPD) Procedure Performace-Baed Platic Deig (PBPD) Procedure 3. Geeral A outlie of the tep-by-tep, Performace-Baed Platic Deig (PBPD) procedure follow, with detail to be dicued i ubequet ectio i thi chapter ad theoretical

More information

MTH 212 Formulas page 1 out of 7. Sample variance: s = Sample standard deviation: s = s

MTH 212 Formulas page 1 out of 7. Sample variance: s = Sample standard deviation: s = s MTH Formula age out of 7 DESCRIPTIVE TOOLS Poulatio ize = N Samle ize = x x+ x +... + x x Poulatio mea: µ = Samle mea: x = = N ( µ ) ( x x) Poulatio variace: = Samle variace: = N Poulatio tadard deviatio:

More information

Analysis of Analytical and Numerical Methods of Epidemic Models

Analysis of Analytical and Numerical Methods of Epidemic Models Iteratioal Joural of Egieerig Reearc ad Geeral Sciece Volue, Iue, Noveber-Deceber, 05 ISSN 09-70 Aalyi of Aalytical ad Nuerical Metod of Epideic Model Pooa Kuari Aitat Profeor, Departet of Mateatic Magad

More information

This is an introductory course in Analysis of Variance and Design of Experiments.

This is an introductory course in Analysis of Variance and Design of Experiments. 1 Notes for M 384E, Wedesday, Jauary 21, 2009 (Please ote: I will ot pass out hard-copy class otes i future classes. If there are writte class otes, they will be posted o the web by the ight before class

More information

The Hong Kong University of Science & Technology ISOM551 Introductory Statistics for Business Assignment 3 Suggested Solution

The Hong Kong University of Science & Technology ISOM551 Introductory Statistics for Business Assignment 3 Suggested Solution The Hog Kog Uiversity of ciece & Techology IOM55 Itroductory tatistics for Busiess Assigmet 3 uggested olutio Note All values of statistics i Q ad Q4 are obtaied by Excel. Qa. Let be the robability that

More information

Stat 3411 Spring 2011 Assignment 6 Answers

Stat 3411 Spring 2011 Assignment 6 Answers Stat 3411 Sprig 2011 Aigmet 6 Awer (A) Awer are give i 10 3 cycle (a) 149.1 to 187.5 Sice 150 i i the 90% 2-ided cofidece iterval, we do ot reject H 0 : µ 150 v i favor of the 2-ided alterative H a : µ

More information

10-716: Advanced Machine Learning Spring Lecture 13: March 5

10-716: Advanced Machine Learning Spring Lecture 13: March 5 10-716: Advaced Machie Learig Sprig 019 Lecture 13: March 5 Lecturer: Pradeep Ravikumar Scribe: Charvi Ratogi, Hele Zhou, Nicholay opi Note: Lae template courtey of UC Berkeley EECS dept. Diclaimer: hee

More information

A tail bound for sums of independent random variables : application to the symmetric Pareto distribution

A tail bound for sums of independent random variables : application to the symmetric Pareto distribution A tail boud for um of idepedet radom variable : applicatio to the ymmetric Pareto ditributio Chritophe Cheeau To cite thi verio: Chritophe Cheeau. A tail boud for um of idepedet radom variable : applicatio

More information

Confidence Interval for Standard Deviation of Normal Distribution with Known Coefficients of Variation

Confidence Interval for Standard Deviation of Normal Distribution with Known Coefficients of Variation Cofidece Iterval for tadard Deviatio of Normal Distributio with Kow Coefficiets of Variatio uparat Niwitpog Departmet of Applied tatistics, Faculty of Applied ciece Kig Mogkut s Uiversity of Techology

More information

Introduction There are two really interesting things to do in statistics.

Introduction There are two really interesting things to do in statistics. ECON 497 Lecture Notes E Page 1 of 1 Metropolita State Uiversity ECON 497: Research ad Forecastig Lecture Notes E: Samplig Distributios Itroductio There are two really iterestig thigs to do i statistics.

More information

Fig. 1: Streamline coordinates

Fig. 1: Streamline coordinates 1 Equatio of Motio i Streamlie Coordiate Ai A. Soi, MIT 2.25 Advaced Fluid Mechaic Euler equatio expree the relatiohip betwee the velocity ad the preure field i ivicid flow. Writte i term of treamlie coordiate,

More information

Confidence Intervals

Confidence Intervals Cofidece Itervals Berli Che Deartmet of Comuter Sciece & Iformatio Egieerig Natioal Taiwa Normal Uiversity Referece: 1. W. Navidi. Statistics for Egieerig ad Scietists. Chater 5 & Teachig Material Itroductio

More information

Read through these prior to coming to the test and follow them when you take your test.

Read through these prior to coming to the test and follow them when you take your test. Math 143 Sprig 2012 Test 2 Iformatio 1 Test 2 will be give i class o Thursday April 5. Material Covered The test is cummulative, but will emphasize the recet material (Chapters 6 8, 10 11, ad Sectios 12.1

More information

6 Sample Size Calculations

6 Sample Size Calculations 6 Sample Size Calculatios Oe of the major resposibilities of a cliical trial statisticia is to aid the ivestigators i determiig the sample size required to coduct a study The most commo procedure for determiig

More information

Chapter 4 Tests of Hypothesis

Chapter 4 Tests of Hypothesis Dr. Moa Elwakeel [ 5 TAT] Chapter 4 Tests of Hypothesis 4. statistical hypothesis more. A statistical hypothesis is a statemet cocerig oe populatio or 4.. The Null ad The Alterative Hypothesis: The structure

More information

ELEC 372 LECTURE NOTES, WEEK 4 Dr. Amir G. Aghdam Concordia University

ELEC 372 LECTURE NOTES, WEEK 4 Dr. Amir G. Aghdam Concordia University ELEC 37 LECTURE NOTES, WEE 4 Dr Amir G Aghdam Cocordia Uiverity Part of thee ote are adapted from the material i the followig referece: Moder Cotrol Sytem by Richard C Dorf ad Robert H Bihop, Pretice Hall

More information

u t u 0 ( 7) Intuitively, the maximum principles can be explained by the following observation. Recall

u t u 0 ( 7) Intuitively, the maximum principles can be explained by the following observation. Recall Oct. Heat Equatio M aximum priciple I thi lecture we will dicu the maximum priciple ad uiquee of olutio for the heat equatio.. Maximum priciple. The heat equatio alo ejoy maximum priciple a the Laplace

More information

MOST PEOPLE WOULD RATHER LIVE WITH A PROBLEM THEY CAN'T SOLVE, THAN ACCEPT A SOLUTION THEY CAN'T UNDERSTAND.

MOST PEOPLE WOULD RATHER LIVE WITH A PROBLEM THEY CAN'T SOLVE, THAN ACCEPT A SOLUTION THEY CAN'T UNDERSTAND. XI-1 (1074) MOST PEOPLE WOULD RATHER LIVE WITH A PROBLEM THEY CAN'T SOLVE, THAN ACCEPT A SOLUTION THEY CAN'T UNDERSTAND. R. E. D. WOOLSEY AND H. S. SWANSON XI-2 (1075) STATISTICAL DECISION MAKING Advaced

More information

Exam II Covers. STA 291 Lecture 19. Exam II Next Tuesday 5-7pm Memorial Hall (Same place as exam I) Makeup Exam 7:15pm 9:15pm Location CB 234

Exam II Covers. STA 291 Lecture 19. Exam II Next Tuesday 5-7pm Memorial Hall (Same place as exam I) Makeup Exam 7:15pm 9:15pm Location CB 234 STA 291 Lecture 19 Exam II Next Tuesday 5-7pm Memorial Hall (Same place as exam I) Makeup Exam 7:15pm 9:15pm Locatio CB 234 STA 291 - Lecture 19 1 Exam II Covers Chapter 9 10.1; 10.2; 10.3; 10.4; 10.6

More information

JOURNAL OF THE INDIAN SOCIETY OF AGRICULTURAL STATISTICS

JOURNAL OF THE INDIAN SOCIETY OF AGRICULTURAL STATISTICS Available olie at www.ia.org.i/jia JOURA OF THE IDIA OIETY OF AGRIUTURA TATITI 64() 00 55-60 Variace Etimatio for te Regreio Etimator of te Mea i tratified amplig UMMARY at Gupta * ad Javid abbir Departmet

More information

Section II. Free-Response Questions -46-

Section II. Free-Response Questions -46- Sectio II Free-Repoe Quetio -46- Formula begi o page 48. Quetio begi o page 51. Table begi o page 60. -47- Formula (I) Decriptive Statitic x = Â x i ( ) 2 1 x = Â x x - 1 i - p = ( - 1) + ( -1 1 2 ) (

More information

Power and Type II Error

Power and Type II Error Statistical Methods I (EXST 7005) Page 57 Power ad Type II Error Sice we do't actually kow the value of the true mea (or we would't be hypothesizig somethig else), we caot kow i practice the type II error

More information

If, for instance, we were required to test whether the population mean μ could be equal to a certain value μ

If, for instance, we were required to test whether the population mean μ could be equal to a certain value μ STATISTICAL INFERENCE INTRODUCTION Statistical iferece is that brach of Statistics i which oe typically makes a statemet about a populatio based upo the results of a sample. I oesample testig, we essetially

More information

Questions about the Assignment. Describing Data: Distributions and Relationships. Measures of Spread Standard Deviation. One Quantitative Variable

Questions about the Assignment. Describing Data: Distributions and Relationships. Measures of Spread Standard Deviation. One Quantitative Variable Quetio about the Aigmet Read the quetio ad awer the quetio that are aked Experimet elimiate cofoudig variable Decribig Data: Ditributio ad Relatiohip GSS people attitude veru their characteritic ad poue

More information

Final Examination Solutions 17/6/2010

Final Examination Solutions 17/6/2010 The Islamic Uiversity of Gaza Faculty of Commerce epartmet of Ecoomics ad Political Scieces A Itroductio to Statistics Course (ECOE 30) Sprig Semester 009-00 Fial Eamiatio Solutios 7/6/00 Name: I: Istructor:

More information

Chapter 23: Inferences About Means

Chapter 23: Inferences About Means Chapter 23: Ifereces About Meas Eough Proportios! We ve spet the last two uits workig with proportios (or qualitative variables, at least) ow it s time to tur our attetios to quatitative variables. For

More information

- E < p. ˆ p q ˆ E = q ˆ = 1 - p ˆ = sample proportion of x failures in a sample size of n. where. x n sample proportion. population proportion

- E < p. ˆ p q ˆ E = q ˆ = 1 - p ˆ = sample proportion of x failures in a sample size of n. where. x n sample proportion. population proportion 1 Chapter 7 ad 8 Review for Exam Chapter 7 Estimates ad Sample Sizes 2 Defiitio Cofidece Iterval (or Iterval Estimate) a rage (or a iterval) of values used to estimate the true value of the populatio parameter

More information

Comparing your lab results with the others by one-way ANOVA

Comparing your lab results with the others by one-way ANOVA Comparig your lab results with the others by oe-way ANOVA You may have developed a ew test method ad i your method validatio process you would like to check the method s ruggedess by coductig a simple

More information