Statistics Parameters
|
|
- Noel Martin
- 5 years ago
- Views:
Transcription
1 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 populatio variace p populatio proportio 1 Saplig Ditributio Saplig ditributio i probability ditributio of the aple Statitic. Saplig Ditributio of Mea (Paraeter) What i the aplig ditributio of ea? Shape: Noral Paraeter: Mea, Stadard Deviatio I ay ituatio, ea ad tadard deviatio ca copletely deterie the ditributio of a pecific hape. 3 4 Saplig Ditributio of Mea (Ditributio hape) Noral ditributio theore: If a rado aple i take fro a orally ditributed populatio, the the aplig ditributio of ea would be oral. Cetral Liit Theore: Whe a relative large rado aple i take fro ay populatio, regardle of the ditributio of the populatio, the aplig ditributio of ea would be approiately oral. 5 Probability Related to Mea Eaple: Coider the ditributio of eru choleterol level for 40- to 70-year-old ale livig i couity A ha a ea of 11 g/100 l, ad the tadard deviatio of 46 g/100 l. If a rado aple of 100 idividual i take fro thi populatio, what i the probability that the average eru choleterol level of thee 100 idividual i higher tha 5? X N (, ) 6 CI - 1
2 Saplig Ditributio & Cofidece Iterval P(X > 5) =? Paraeter of the aplig ditributio of the ea: Choleterol Level ha a ea 11, d The aplig ditributio of the ea i orally ditributed. X ~ N ( 11, 4.6) P(X > 5) =? X N ( 11, 4.6) = P( X 5) P( Z 3.04) Choleterol Level ha a ea 11,.d = z 8 A Special Notatio Itroductio to Etiatio Cofidece Iterval & Saple Size z = the z core that the proportio of the tadard oral ditributio to the right of it i. Z z.05 = 1.96? z.010 =? z Saplig Error Saple tatitic (poit etiate) Key Eleet of Iterval Etiatio Cofidece Level: A probability that the populatio paraeter fall oewhere withi the iterval. Cofidece iterval Saple tatitic (poit etiate) Saplig Error = 11 Cofidece liit (lower) Cofidece liit (upper) Margi of Error 98 1 F 1 CI -
3 Saplig Ditributio & Cofidece Iterval Saplig Ditributio of Mea The Cofidece Iterval.05 -?.95 _ +?.05 X Cofidece Level _ 1.96 = z.05 / / = =.95 X Withi how ay tadard deviatio of the ea will have 95% of the aplig ditributio? 13 Cofidece Iterval => 95% Saple Mea The Cofidece Iterval.5% 95% Saple _.5% 15 X 95 % of iterval cotai. 5% do ot. Cofidece Iterval for Mea ( Kow) (1-) 100% Cofidece Iterval Etiate for ea of a oral populatio ( X - Z /, X Z / ) or X Z / Kow ay ea that we have very good etiate of. It i ot practical to aue that we kow. Margi of Error 16 Cofidece Iterval Mea ( Ukow & < 30) 1. Auptio Populatio Stadard Deviatio I Ukow Populatio Mut Be Norally Ditributed. Ue Studet t Ditributio 3. Cofidece Iterval Etiate S S ( X - t /, -1, X t /, -1 X t, -1 ) 17 Studet t Table t value For a 90% C.I.: = 3 df = - 1 = =.10 / =.05 t / =? t CI - 3
4 Saplig Ditributio & Cofidece Iterval Average Weight for Feale Te Year Childre I US Ifo. fro a rado aple: = 10, = 80 lb, = lb, aue weight i orally ditributed, fid the 95% cofidece iterval etiate for average weight. Data: How do we kow whether orality auptio i OK? 19 0 Average Weight for Feale Te Year Childre I US Data: = 10, = 80 lb, = lb t / = t.05/ = t 0.05, d.f. = 10 1 = 9, t 0.05, 9 =.6 t / (67.09, 9.91) 1 Shapiro-Wilk orality tet data: TeYearOldChild$Weight W = , p-value = Oe Saple t-tet data: TeYearOldChild$Weight t = , df = 9, p-value =.033e-07 alterative hypothei: true ea i ot equal to 0 95 percet cofidece iterval: aple etiate: ea of 80 Thikig Challege What i the average ittig pule rate for tudet i cla? Fid the 95% cofidece iterval etiate. What i your pule rate? 3 Cofidece iterval with z-core: The (1- )% cofidece iterval etiate for populatio ea: Auptio: If apled fro oral populatio with kow variace,, z / Auptio: If large aple ad if ukow variace, replace, z / 4 CI - 4
5 Saplig Ditributio & Cofidece Iterval Cofidece iterval with t-core: The (1- )% cofidece iterval etiate for populatio ea: Auptio: If apled fro oral populatio with ukow variace,, t /, df -1 (If aple ize i large the orality auptio i iigificat.) t z a aple becoe large 5 Fidig Saple Size for Etiatig C.I. : Margi z z E of Error E Z 6 Proportio Etiatio Paraeter: Populatio Proportio p (or p) (Percetage of people ha o health iurace) Statitic: Saple Proportio i uber of uccee i aple ize Reark: If data i coded a 1 or 0, aple ea i the ae a aple proportio of Data: 1, 0, 0, 1, 0. 4 p Cofidece Iterval Proportio 1. Auptio Two Categorical Outcoe Noral Approiatio Ca Be Ued If p ad (1 p) are both greater tha 5.. Cofidece Iterval Etiate (for large aple) ( (1 - ) (1 - ) - z, z ) (1 - ) z 8 Etiatio Eaple Proportio A rado aple of 400 fro a large couity howed that 3 have diabete. Set up a 95% cofidece iterval etiate for p, the percetage of people that have diabete. 400 (1 - ) Z / (1 -.08) z z %.7% ( 5.3%, 10.7% ) 9 Saple Size (1 - ) C.I.: z (1 - ) Margi of Error E Z z (1 - ) if pilot tudy i doe. E or z to get the larget aple to 0.5 E achieve the goal. 30 CI - 5
6 Saplig Ditributio & Cofidece Iterval Saple Size (No prior iforatio o p) Saple Size Eaple: If oe wihe to do a urvey to etiate the populatio proportio with 95% cofidece ad a argi of error of 3%, how large a aple i eeded? Z / = 1.96; E =.03 = (1.96 /.03 ).5 = A aple of ize 1068 i eeded. 31 Saple Size (With prior iforatio o p) Saple Size Eaple: If oe wihe to to etiate the percetage of people ifected with Wet Nile i a populatio with 95% cofidece ad a argi of error of 3%, how large a aple i eeded? (A pilot tudy ha bee doe, ad the aple proportio wa 6%.) Z / = 1.96; E =.03 = (1.96 /.03 ).06 (1.06) = 40.7 A aple of ize 41 i eeded. How large a aple wa ued for pilot tudy? 3 Eaple: Reearcher wih to etiate the percetage of hopital eployee ifected by a certai dieae i a coutry. Out of 500 radoly choe hopital eployee, 64 were ifected. Fid the 95% cofidece iterval etiate for percetage of hopital eployee ifected by thi dieae i thi coutry. 33 CI - 6
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 informationChapter 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 informationStatistical 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 informationTables 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 informationx 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 informationChapter 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 informationM227 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 informationIntroEcono. 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 informationFormula 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 informationComments 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 informationMATH/STAT 352: Lecture 15
MATH/STAT 352: Lecture 15 Sectios 5.2 ad 5.3. Large sample CI for a proportio ad small sample CI for a mea. 1 5.2: Cofidece Iterval for a Proportio Estimatig proportio of successes i a biomial experimet
More informationCOMPARISONS 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 informationTESTS 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 informationSTA 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 informationTools 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 informationVIII. 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 information20. 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 informationS 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 informationREVIEW 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 informationStatistics 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 informationBIOS 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 informationStatistical 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 informationConfidence 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 informationChapter 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 informationEstimation 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 information100(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 informationUNIVERSITY 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 informationStat 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 informationThe Coupon Collector Problem in Statistical Quality Control
The Coupo Collector Proble i Statitical Quality Cotrol Taar Gadrich, ad Rachel Ravid Abtract I the paper, the author have exteded the claical coupo collector proble to the cae of group drawig with iditiguihable
More informationData Analysis and Statistical Methods Statistics 651
Data Aalysis ad Statistical Methods Statistics 651 http://www.stat.tau.edu/~suhasii/teachig.htl Suhasii Subba Rao Exaple The itroge cotet of three differet clover plats is give below. 3DOK1 3DOK5 3DOK7
More informationUNIT 8: INTRODUCTION TO INTERVAL ESTIMATION
STATISTICAL METHODS FOR BUSINESS UNIT 8: INTRODUCTION TO INTERVAL ESTIMATION 8..- Itroductio to iterval estimatio 8..- Cofidece itervals. Costructio ad characteristics 8.3.- Cofidece itervals for the mea
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
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 informationTI-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 informationSOLUTION: 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 informationConfidence Interval for one population mean or one population proportion, continued. 1. Sample size estimation based on the large sample C.I.
Cofidece Iterval for oe populatio mea or oe populatio proportio, cotiued 1. ample size estimatio based o the large sample C.I. for p ˆ(1 ˆ) ˆ(1 ˆ) From the iterval ˆ p p Z p ˆ, p Z p p L legh of your 100(1
More informationInterval Estimation (Confidence Interval = C.I.): An interval estimate of some population parameter is an interval of the form (, ),
Cofidece Iterval Estimatio Problems Suppose we have a populatio with some ukow parameter(s). Example: Normal(,) ad are parameters. We eed to draw coclusios (make ifereces) about the ukow parameters. We
More informationInferential Statistics. Inference Process. Inferential Statistics and Probability a Holistic Approach. Inference Process.
Iferetial Statistics ad Probability a Holistic Approach Iferece Process Chapter 8 Poit Estimatio ad Cofidece Itervals This Course Material by Maurice Geraghty is licesed uder a Creative Commos Attributio-ShareAlike
More informationSection 18: confidence interval & hypothesis testing using sample means (sigma unknown)
Sectio 18: cofidece iterval & hypothei tetig uig ample mea (igma ukow) 1. A imple radom ample of 40 package of Chip Ahoy cookie reveal a average of 1261.6 chocolate chip per package, with a tadard deviatio
More informationBIOSTATISTICS. Lecture 7 Hypothesis about Means and Proportions of Two Populations. dr. Petr Nazarov.
Geoic Reearch Uit BIOSTATISTICS Lecture 7 Hyothei about Mea ad Proortio o Two Poulatio dr. Petr Nazarov 7-04-07 etr.azarov@lih.lu Lecture 7. Hyothei about ea ad roortio o two oulatio OUTLINE Lecture 7
More informationBelow are the following formulas for the z-scores section.
Statitic 010: Statitic for the Social ad Behavioral Sciece Formula Hadout Below are the followig formula for the z-core ectio. eaure of cetral tedecy ad variability ea Rage Rage = highet lowet Variace
More informationCHAPTER 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 informationGrant MacEwan University STAT 151 Formula Sheet Final Exam Dr. Karen Buro
Grat MacEwa Uiverity STAT 151 Formula Sheet Fial Exam Dr. Kare Buro Decriptive Statitic Sample Variace: = i=1 (x i x) 1 = Σ i=1x i (Σ i=1 x i) 1 Sample Stadard Deviatio: = Sample Variace = Media: Order
More information11/19/ 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 informationSTUDENT 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 informationSample Size Determination (Two or More Samples)
Sample Sie Determiatio (Two or More Samples) STATGRAPHICS Rev. 963 Summary... Data Iput... Aalysis Summary... 5 Power Curve... 5 Calculatios... 6 Summary This procedure determies a suitable sample sie
More information18.05 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 informationHypothesis tests and confidence intervals
Hypothesis tests ad cofidece itervals The 95% cofidece iterval for µ is the set of values, µ 0, such that the ull hypothesis H 0 : µ = µ 0 would ot be rejected by a two-sided test with α = 5%. The 95%
More informationAnnouncements. Unit 5: Inference for Categorical Data Lecture 1: Inference for a single proportion
Housekeepig Aoucemets Uit 5: Iferece for Categorical Data Lecture 1: Iferece for a sigle proportio Statistics 101 Mie Çetikaya-Rudel PA 4 due Friday at 5pm (exteded) PS 6 due Thursday, Oct 30 October 23,
More informationA quick activity - Central Limit Theorem and Proportions. Lecture 21: Testing Proportions. Results from the GSS. Statistics and the General Population
A quick activity - Cetral Limit Theorem ad Proportios Lecture 21: Testig Proportios Statistics 10 Coli Rudel Flip a coi 30 times this is goig to get loud! Record the umber of heads you obtaied ad calculate
More informationTopic 10: Introduction to Estimation
Topic 0: Itroductio to Estimatio Jue, 0 Itroductio I the simplest possible terms, the goal of estimatio theory is to aswer the questio: What is that umber? What is the legth, the reactio rate, the fractio
More information7-1. Chapter 4. Part I. Sampling Distributions and Confidence Intervals
7-1 Chapter 4 Part I. Samplig Distributios ad Cofidece Itervals 1 7- Sectio 1. Samplig Distributio 7-3 Usig Statistics Statistical Iferece: Predict ad forecast values of populatio parameters... Test hypotheses
More informationMathematical Notation Math Introduction to Applied Statistics
Mathematical Notatio Math 113 - Itroductio to Applied Statistics Name : Use Word or WordPerfect to recreate the followig documets. Each article is worth 10 poits ad ca be prited ad give to the istructor
More informationWe have also learned that, thanks to the Central Limit Theorem and the Law of Large Numbers,
Cofidece Itervals III What we kow so far: We have see how to set cofidece itervals for the ea, or expected value, of a oral probability distributio, both whe the variace is kow (usig the stadard oral,
More informationStatistics 20: Final Exam Solutions Summer Session 2007
1. 20 poits Testig for Diabetes. Statistics 20: Fial Exam Solutios Summer Sessio 2007 (a) 3 poits Give estimates for the sesitivity of Test I ad of Test II. Solutio: 156 patiets out of total 223 patiets
More informationMTH 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 informationMOST 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 informationMathacle. PSet Stats, Concepts In Statistics Level Number Name: Date:
PSet ----- Stats, Cocepts I Statistics 7.3. Cofidece Iterval for a Mea i Oe Sample [MATH] The Cetral Limit Theorem. Let...,,, be idepedet, idetically distributed (i.i.d.) radom variables havig mea µ ad
More informationStatistics 300: Elementary Statistics
Statistics 300: Elemetary Statistics Sectios 7-, 7-3, 7-4, 7-5 Parameter Estimatio Poit Estimate Best sigle value to use Questio What is the probability this estimate is the correct value? Parameter Estimatio
More informationClass 23. Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science. Marquette University MATH 1700
Class 23 Daiel B. Rowe, Ph.D. Departmet of Mathematics, Statistics, ad Computer Sciece Copyright 2017 by D.B. Rowe 1 Ageda: Recap Chapter 9.1 Lecture Chapter 9.2 Review Exam 6 Problem Solvig Sessio. 2
More informationTopic 9: Sampling Distributions of Estimators
Topic 9: Samplig Distributios of Estimators Course 003, 2018 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be
More informationExam 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 informationMBACATÓLICA. Quantitative Methods. Faculdade de Ciências Económicas e Empresariais UNIVERSIDADE CATÓLICA PORTUGUESA 9. SAMPLING DISTRIBUTIONS
MBACATÓLICA Quatitative Methods Miguel Gouveia Mauel Leite Moteiro Faculdade de Ciêcias Ecoómicas e Empresariais UNIVERSIDADE CATÓLICA PORTUGUESA 9. SAMPLING DISTRIBUTIONS MBACatólica 006/07 Métodos Quatitativos
More informationStatistical Inference (Chapter 10) Statistical inference = learn about a population based on the information provided by a sample.
Statistical Iferece (Chapter 10) Statistical iferece = lear about a populatio based o the iformatio provided by a sample. Populatio: The set of all values of a radom variable X of iterest. Characterized
More informationContents Two Sample t Tests Two Sample t Tests
Cotets 3.5.3 Two Saple t Tests................................... 3.5.3 Two Saple t Tests Setup: Two Saples We ow focus o a sceario where we have two idepedet saples fro possibly differet populatios. Our
More informationTABLES AND FORMULAS FOR MOORE Basic Practice of Statistics
TABLES AND FORMULAS FOR MOORE Basic Practice of Statistics Explorig Data: Distributios Look for overall patter (shape, ceter, spread) ad deviatios (outliers). Mea (use a calculator): x = x 1 + x 2 + +
More informationTopic 9: Sampling Distributions of Estimators
Topic 9: Samplig Distributios of Estimators Course 003, 2016 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be
More informationTABLES AND FORMULAS FOR MOORE Basic Practice of Statistics
TABLES AND FORMULAS FOR MOORE Basic Practice of Statistics Explorig Data: Distributios Look for overall patter (shape, ceter, spread) ad deviatios (outliers). Mea (use a calculator): x = x 1 + x 2 + +
More informationChapter 20. Comparing Two Proportions. BPS - 5th Ed. Chapter 20 1
Chapter 0 Comparig Two Proportios BPS - 5th Ed. Chapter 0 Case Study Machie Reliability A study is performed to test of the reliability of products produced by two machies. Machie A produced 8 defective
More informationTopic 9: Sampling Distributions of Estimators
Topic 9: Samplig Distributios of Estimators Course 003, 2018 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be
More informationData 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 informationChapter 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 informationComputing Confidence Intervals for Sample Data
Computig Cofidece Itervals for Sample Data Topics Use of Statistics Sources of errors Accuracy, precisio, resolutio A mathematical model of errors Cofidece itervals For meas For variaces For proportios
More informationLecture 5. Materials Covered: Chapter 6 Suggested Exercises: 6.7, 6.9, 6.17, 6.20, 6.21, 6.41, 6.49, 6.52, 6.53, 6.62, 6.63.
STT 315, Summer 006 Lecture 5 Materials Covered: Chapter 6 Suggested Exercises: 67, 69, 617, 60, 61, 641, 649, 65, 653, 66, 663 1 Defiitios Cofidece Iterval: A cofidece iterval is a iterval believed to
More informationMathacle 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 informationSTA Learning Objectives. Population Proportions. Module 10 Comparing Two Proportions. Upon completing this module, you should be able to:
STA 2023 Module 10 Comparig Two Proportios Learig Objectives Upo completig this module, you should be able to: 1. Perform large-sample ifereces (hypothesis test ad cofidece itervals) to compare two populatio
More informationRule of probability. Let A and B be two events (sets of elementary events). 11. If P (AB) = P (A)P (B), then A and B are independent.
Percetile: the αth percetile of a populatio is the value x 0, such that P (X x 0 ) α% For example the 5th is the x 0, such that P (X x 0 ) 5% 05 Rule of probability Let A ad B be two evets (sets of elemetary
More informationMATH 320: Probability and Statistics 9. Estimation and Testing of Parameters. Readings: Pruim, Chapter 4
MATH 30: Probability ad Statistics 9. Estimatio ad Testig of Parameters Estimatio ad Testig of Parameters We have bee dealig situatios i which we have full kowledge of the distributio of a radom variable.
More informationz is the upper tail critical value from the normal distribution
Statistical Iferece drawig coclusios about a populatio parameter, based o a sample estimate. Populatio: GRE results for a ew eam format o the quatitative sectio Sample: =30 test scores Populatio Samplig
More informationEstimation of a population proportion March 23,
1 Social Studies 201 Notes for March 23, 2005 Estimatio of a populatio proportio Sectio 8.5, p. 521. For the most part, we have dealt with meas ad stadard deviatios this semester. This sectio of the otes
More informationOverview. 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 informationStatistical Intervals Based on a Single Sample (Devore Chapter Seven)
Statitical Iterval Baed o a Sigle Sample Devore Chapter Seve MATH-252-01: robability ad Statitic II Sprig 2018 Cotet 0 Itroductio 1 0.1 Motivatio...................... 1 0.2 Remider of Notatio................
More informationMIT : Quantitative Reasoning and Statistical Methods for Planning I
MIT 11.220 Sprig 06 Recitatio 4 March 16, 2006 MIT - 11.220: Quatitative Reasoig ad Statistical Methods for Plaig I Recitatio #4: Sprig 2006 Cofidece Itervals ad Hypothesis Testig I. Cofidece Iterval 1.
More informationStatistical 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 informationFormulas and Tables for Gerstman
Formulas ad Tables for Gerstma Measuremet ad Study Desig Biostatistics is more tha a compilatio of computatioal techiques! Measuremet scales: quatitative, ordial, categorical Iformatio quality is primary
More informationStatistics 511 Additional Materials
Cofidece Itervals o mu Statistics 511 Additioal Materials This topic officially moves us from probability to statistics. We begi to discuss makig ifereces about the populatio. Oe way to differetiate probability
More informationApril 18, 2017 CONFIDENCE INTERVALS AND HYPOTHESIS TESTING, UNDERGRADUATE MATH 526 STYLE
April 18, 2017 CONFIDENCE INTERVALS AND HYPOTHESIS TESTING, UNDERGRADUATE MATH 526 STYLE TERRY SOO Abstract These otes are adapted from whe I taught Math 526 ad meat to give a quick itroductio to cofidece
More informationConfidence Level We want to estimate the true mean of a random variable X economically and with confidence.
Cofidece Iterval 700 Samples Sample Mea 03 Cofidece Level 095 Margi of Error 0037 We wat to estimate the true mea of a radom variable X ecoomically ad with cofidece True Mea μ from the Etire Populatio
More informationStat 200 -Testing Summary Page 1
Stat 00 -Testig Summary Page 1 Mathematicias are like Frechme; whatever you say to them, they traslate it ito their ow laguage ad forthwith it is somethig etirely differet Goethe 1 Large Sample Cofidece
More informationOctober 25, 2018 BIM 105 Probability and Statistics for Biomedical Engineers 1
October 25, 2018 BIM 105 Probability ad Statistics for Biomedical Egieers 1 Populatio parameters ad Sample Statistics October 25, 2018 BIM 105 Probability ad Statistics for Biomedical Egieers 2 Ifereces
More informationSTATISTICAL INFERENCE
STATISTICAL INFERENCE POPULATION AND SAMPLE Populatio = all elemets of iterest Characterized by a distributio F with some parameter θ Sample = the data X 1,..., X, selected subset of the populatio = sample
More informationAP Statistics Review Ch. 8
AP Statistics Review Ch. 8 Name 1. Each figure below displays the samplig distributio of a statistic used to estimate a parameter. The true value of the populatio parameter is marked o each samplig distributio.
More informationConfidence 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 informationDifference 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 informationChapters 5 and 13: REGRESSION AND CORRELATION. Univariate data: x, Bivariate data (x,y).
Chapters 5 ad 13: REGREION AND CORRELATION (ectios 5.5 ad 13.5 are omitted) Uivariate data: x, Bivariate data (x,y). Example: x: umber of years studets studied paish y: score o a proficiecy test For each
More informationFinal Review. Fall 2013 Prof. Yao Xie, H. Milton Stewart School of Industrial Systems & Engineering Georgia Tech
Fial Review Fall 2013 Prof. Yao Xie, yao.xie@isye.gatech.edu H. Milto Stewart School of Idustrial Systems & Egieerig Georgia Tech 1 Radom samplig model radom samples populatio radom samples: x 1,..., x
More informationAgreement of CI and HT. Lecture 13 - Tests of Proportions. Example - Waiting Times
Sigificace level vs. cofidece level Agreemet of CI ad HT Lecture 13 - Tests of Proportios Sta102 / BME102 Coli Rudel October 15, 2014 Cofidece itervals ad hypothesis tests (almost) always agree, as log
More informationStatistics 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 informationChapter 22. Comparing Two Proportions. Copyright 2010, 2007, 2004 Pearson Education, Inc.
Chapter 22 Comparig Two Proportios Copyright 2010, 2007, 2004 Pearso Educatio, Ic. Comparig Two Proportios Read the first two paragraphs of pg 504. Comparisos betwee two percetages are much more commo
More informationST 305: Exam 3 ( ) = P(A)P(B A) ( ) = P(A) + P(B) ( ) = 1 P( A) ( ) = P(A) P(B) σ X 2 = σ a+bx. σ ˆp. σ X +Y. σ X Y. σ X. σ Y. σ n.
ST 305: Exam 3 By hadig i this completed exam, I state that I have either give or received assistace from aother perso durig the exam period. I have used o resources other tha the exam itself ad the basic
More informationConfidence Intervals รศ.ดร. อน นต ผลเพ ม Assoc.Prof. Anan Phonphoem, Ph.D. Intelligent Wireless Network Group (IWING Lab)
Cofidece Itervals รศ.ดร. อน นต ผลเพ ม Assoc.Prof. Aa Phophoem, Ph.D. aa.p@ku.ac.th Itelliget Wireless Network Group (IWING Lab) http://iwig.cpe.ku.ac.th Computer Egieerig Departmet Kasetsart Uiversity,
More information