Section 18: confidence interval & hypothesis testing using sample means (sigma unknown)

Size: px
Start display at page:

Download "Section 18: confidence interval & hypothesis testing using sample means (sigma unknown)"

Transcription

1 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 chocolate chip per package, with a tadard deviatio of chip. (a) Fid 95%, 90%, ad 99% cofidece iterval for the mea umber of chocolate chip i all Chip Ahoy package. df % cofidece: t ± ± ( , ) 90% cofidece: t ± ± ( , ) 99% cofidece: t ± ± ( , ) (b) The compay claim that Chip Ahoy package cotai o average 1300 chocolate chip. Do we have evidece at the 10%, 5%, 1% level that they re exaggeratig? H 0 : μ 1300 H a : μ < 1300 t tail: > > p value: 0.01 < P < 0.025

2 (c) Jack claim that Chip Ahoy package cotai o average 1300 chocolate chip. Do we have evidece at the 10%, 5%, 1% level that he i mitake? H a : μ tail: > > p value: 0.02 < P < How may lick doe it take to get to the Tootie Roll ceter of a Tootie Pop? A imple radom ample of 22 attempt yield a average of 508 lick per pop, with a tadard deviatio of 164 lick. (a) Fid 95%, 90%, ad 99% cofidece iterval for the mea umber of lick for all pop. df % cofidece: t ± ± (435.27, ) 90% cofidece: t ± ± (447.83, ) 99% cofidece: t ± ± (409.01, )

3 (b) Mr. Owl claim that it take o average 500 lick. Do we have evidece at the 10%, 5%, 1% level that he uderetimatig? Do we have evidece at the 10%, 5%, 1% level that he mitake? H 0 : μ 500 H a : μ > 500 t tail: > p value: 0.25 < P P.10 H a : μ tail: > p value: 0.50 < P P.10 (c) Mr. Turtle claim that it take o average 580 lick. Do we have evidece at the 10%, 5%, 1% level that he overetimatig? Do we have evidece at the 10%, 5%, 1% level that he mitake? H 0 : μ 580 H a : μ < 580 t

4 1 tail: > > p value: < P < 0.05 H a : μ tail: > > p value: 0.05 < P < What i the average commute time for people goig to work i the ortheat? A imple radom ample of 30 people who drive to work i the ortheat reult i a mea time of miute with a tadard deviatio of miute. (a) Fid 80%, 98%, ad 95% cofidece iterval for the average commute time i the ortheat. df % cofidece: t ± ± (25.57,30.37) 98% cofidece: t ± ± (23.46, 32.48) 95% cofidece: t ± ±

5 (24.22, 31.72) (b) Jack claim that o average i take 32 miute for omeoe i the ortheat to get to work. Do we have evidece at the 10%, 5%, 1% level that he overetimatig? Do we have evidece at the 10%, 5%, 1% level that he mitake? H 0 : μ 32 H a : μ < 32 t tail: > > p value: 0.01 < P < H a : μ 32 2 tail: > > p value: 0.02 < P < 0.05 (c) Sam claim that o average i take 25 miute for omeoe i the ortheat to get to work. Do we have evidece at the 10%, 5%, 1% level that he uderetimatig? Do we have evidece at the 10%, 5%, 1% level that he mitake? H 0 : μ 25 H a : μ > 25

6 t tail: > > p value: 0.05 < P < H a : μ 25 2 tail: > > p value: 0.10 < P < 0.15 P Joatha Ket ha bee uig a certai brad of chicke feed for year, ad o average hi chicke weigh 62.2 ouce. He tet a ew type of chicke feed o 9 chicke, ad their average weight i ouce, with a tadard deviatio of ouce. (a) Fid 99%, 95%, ad 85% cofidece iterval for the average weight that would be gaied by all chicke uig the ew feed. df % cofidece: t ± ± (62.07, 66.69) 95% cofidece: t ± ± (62.79, 65.97)

7 85% cofidece: t ± ± (63.28, 65.48) (b) Doe Joatha have evidece at the 10%, 5%, 1% level that the ew chicke feed i better tha the old brad? H 0 : μ 62.2 H a : μ > 62.2 t tail: > > p value: < P < 0.01 P < A certai comedia meaure the effectivee of hi routie by the umber of time he ha to wait for laughter to ubide before cotiuig; hi old routie averaged 63.2 time. He i tryig a ew routie, ad of it 15 performace, the average umber of time he had to wait wa time, with a tadard deviatio of 19.3 time. (a) Fid 99%, 95%, 90% cofidece iterval for the average umber of waitig time for all poible performace of hi ew routie (i.e., hi meaure of how good it i). df % cofidece: t ± ± (54.69, 84.37) 95% cofidece: t 2.145

8 ± ± (58.84, 80.22) 90% cofidece: t ± ± (60.75, 78.31) (b) Doe he have evidece at the 10%, 5%, 1% level that hi ew routie i better? H 0 : μ 63.2 H a : μ > 63.2 t tail: > > p value: 0.10 < P < P A imple radom ample of 33 brow M&M reveal that their average weight i gram with a tadard deviatio of gram. (a) Fid 80%, 90%, 95%, 99% cofidece iterval for the true average weight of (all) brow M&M. df % cofidece: t ± ± (0.9038, ) 90% cofidece: t 1.694

9 ± ± (0.9012, ) 95% cofidece: t ± ± (0.8988, ) 99% cofidece: t ± ± (0.8940, ) (b) Horatio, a lover of brow M&M with too much time o hi had, grumble that he believe that o average, a brow M&M weigh oly gram. Do we have evidece that Horatio i uderetimatig the average weight of brow M&M? H 0 : μ H a : μ > t tail: > p value: 0.25 < P P A imple radom ample of 20 tatitic tudet durig a tatitic exam give a average pule rate 74.4 with a tadard deviatio of 10. (a) Fid 90%, 95%, 99% cofidece iterval for the average pule rate of all tatitic tudet durig a exam.

10 df % cofidece: t ± ± (70.53, 78.27) 95% cofidece: t ± ± (69.72, 79.08) 99% cofidece: t ± ± (68.00, 80.80) (b) If a good exercie regime would give uch tudet a pule rate of 60, do we have evidece at the 10%, 5%, 1% level that a tatitic exam ha a greater effect o pule rate tha uch exercie? H 0 : μ 60 H a : μ > 60 t tail: > p value: P < P <.01

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

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

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 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

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

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

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

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

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

2. We can be 90% confident that the proportion of people in the city who know how to ski is between and 0.358

2. We can be 90% confident that the proportion of people in the city who know how to ski is between and 0.358 Fial Exam (sample) This particular sample covers sectios 10 19 iclusive. I. I a particular large Midwest city, a simple radom sample of 400 people reveals that 128 of them kow how to ski. 1. A 99.8% cofidece

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

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

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

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

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

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

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

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

Estimating the Population Mean - when a sample average is calculated we can create an interval centered on this average

Estimating the Population Mean - when a sample average is calculated we can create an interval centered on this average 6. Cofidece Iterval for the Populatio Mea p58 Estimatig the Populatio Mea - whe a sample average is calculated we ca create a iterval cetered o this average x-bar - at a predetermied level of cofidece

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

7-1. Chapter 4. Part I. Sampling Distributions and Confidence Intervals

7-1. 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 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

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

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

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

Comparing Two Populations. Topic 15 - Two Sample Inference I. Comparing Two Means. Comparing Two Pop Means. Background Reading

Comparing Two Populations. Topic 15 - Two Sample Inference I. Comparing Two Means. Comparing Two Pop Means. Background Reading Topic 15 - Two Sample Iferece I STAT 511 Professor Bruce Craig Comparig Two Populatios Research ofte ivolves the compariso of two or more samples from differet populatios Graphical summaries provide visual

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

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

Confidence Interval for one population mean or one population proportion, continued. 1. Sample size estimation based on the large sample C.I.

Confidence 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 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

University of California, Los Angeles Department of Statistics. Hypothesis testing

University of California, Los Angeles Department of Statistics. Hypothesis testing Uiversity of Califoria, Los Ageles Departmet of Statistics Statistics 100B Elemets of a hypothesis test: Hypothesis testig Istructor: Nicolas Christou 1. Null hypothesis, H 0 (claim about µ, p, σ 2, µ

More information

Interval Estimation (Confidence Interval = C.I.): An interval estimate of some population parameter is an interval of the form (, ),

Interval 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 information

Inferential Statistics. Inference Process. Inferential Statistics and Probability a Holistic Approach. Inference Process.

Inferential 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 information

Worksheet 23 ( ) Introduction to Simple Linear Regression (continued)

Worksheet 23 ( ) Introduction to Simple Linear Regression (continued) Worksheet 3 ( 11.5-11.8) Itroductio to Simple Liear Regressio (cotiued) This worksheet is a cotiuatio of Discussio Sheet 3; please complete that discussio sheet first if you have ot already doe so. This

More information

Widely used? average out effect Discrete Prior. Examplep. More than one observation. using MVUE (sample mean) yy 1 = 3.2, y 2 =2.2, y 3 =3.6, y 4 =4.

Widely used? average out effect Discrete Prior. Examplep. More than one observation. using MVUE (sample mean) yy 1 = 3.2, y 2 =2.2, y 3 =3.6, y 4 =4. Dicrete Prior for (μ Widely ued? average out effect Dicrete Prior populatio td i kow equally likely or ubjective weight π ( μ y ~ π ( μ l( y μ π ( μ e Examplep ( μ y Set a ubjective prior ad a gueig value

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

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

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

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

This chapter focuses on two experimental designs that are crucial to comparative studies: (1) independent samples and (2) matched pair samples.

This chapter focuses on two experimental designs that are crucial to comparative studies: (1) independent samples and (2) matched pair samples. Chapter 9 & : Comparig Two Treatmets: This chapter focuses o two eperimetal desigs that are crucial to comparative studies: () idepedet samples ad () matched pair samples Idepedet Radom amples from Two

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

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

Topic 9: Sampling Distributions of Estimators

Topic 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 information

MATH CALCULUS II Objectives and Notes for Test 4

MATH CALCULUS II Objectives and Notes for Test 4 MATH 44 - CALCULUS II Objectives ad Notes for Test 4 To do well o this test, ou should be able to work the followig tpes of problems. Fid a power series represetatio for a fuctio ad determie the radius

More information

Confidence Intervals for the Population Proportion p

Confidence Intervals for the Population Proportion p Cofidece Itervals for the Populatio Proportio p The cocept of cofidece itervals for the populatio proportio p is the same as the oe for, the samplig distributio of the mea, x. The structure is idetical:

More information

http://www.xelca.l/articles/ufo_ladigsbaa_houte.aspx imulatio Output aalysis 3/4/06 This lecture Output: A simulatio determies the value of some performace measures, e.g. productio per hour, average queue

More information

IE 230 Probability & Statistics in Engineering I. Closed book and notes. No calculators. 120 minutes.

IE 230 Probability & Statistics in Engineering I. Closed book and notes. No calculators. 120 minutes. Closed book ad otes. No calculators. 120 miutes. Cover page, five pages of exam, ad tables for discrete ad cotiuous distributios. Score X i =1 X i / S X 2 i =1 (X i X ) 2 / ( 1) = [i =1 X i 2 X 2 ] / (

More information

Chapter 1 (Definitions)

Chapter 1 (Definitions) FINAL EXAM REVIEW Chapter 1 (Defiitios) Qualitative: Nomial: Ordial: Quatitative: Ordial: Iterval: Ratio: Observatioal Study: Desiged Experimet: Samplig: Cluster: Stratified: Systematic: Coveiece: Simple

More information

Exam 2 Instructions not multiple versions

Exam 2 Instructions not multiple versions Exam 2 Istructios Remove this sheet of istructios from your exam. You may use the back of this sheet for scratch work. This is a closed book, closed otes exam. You are ot allowed to use ay materials other

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

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

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

Section 9.2. Tests About a Population Proportion 12/17/2014. Carrying Out a Significance Test H A N T. Parameters & Hypothesis

Section 9.2. Tests About a Population Proportion 12/17/2014. Carrying Out a Significance Test H A N T. Parameters & Hypothesis Sectio 9.2 Tests About a Populatio Proportio P H A N T O M S Parameters Hypothesis Assess Coditios Name the Test Test Statistic (Calculate) Obtai P value Make a decisio State coclusio Sectio 9.2 Tests

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

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

Statistical Inference About Means and Proportions With Two Populations

Statistical Inference About Means and Proportions With Two Populations Departmet of Quatitative Methods & Iformatio Systems Itroductio to Busiess Statistics QM 220 Chapter 10 Statistical Iferece About Meas ad Proportios With Two Populatios Fall 2010 Dr. Mohammad Zaial 1 Chapter

More information

Chem Exam 1-9/14/16. Frequency. Grade Average = 72, Median = 72, s = 20

Chem Exam 1-9/14/16. Frequency. Grade Average = 72, Median = 72, s = 20 0 4 8 6 0 4 8 3 36 40 44 48 5 56 60 64 68 7 76 80 84 88 9 96 00 Chem 53 - Exam - 9/4/6 8 7 6 5 4 3 Frequecy 0 Grade Average = 7, Media = 7, = 0 Exam Chem 53 September 4, 065 Quetio, 7 poit each for quetio

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

MATH/STAT 352: Lecture 15

MATH/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 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

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

Chapter 7. , and is unknown and n 30 then X ~ t n

Chapter 7. , and is unknown and n 30 then X ~ t n Chpter 7 Sectio 7. t-ditributio ( 3) Summry: C.L.T. : If the rdom mple of ize 3 come from ukow popultio with me d S.D. where i kow or ukow, the X ~ N,. Note: The hypothei tetig d cofidece itervl re built

More information

IUT of Saint-Etienne Sales and Marketing department Mr Ferraris Prom /12/2015

IUT of Saint-Etienne Sales and Marketing department Mr Ferraris Prom /12/2015 IUT of Sait-Etiee Sales ad Marketig departmet Mr Ferraris Prom 2014-2016 09/12/2015 MATHEMATICS 3 rd semester, Test 2 legth : 2 hours coefficiet 2/3 The graphic calculator is allowed. Ay persoal sheet

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

SOLUTIONS y n. n 1 = 605, y 1 = 351. y1. p y n. n 2 = 195, y 2 = 41. y p H 0 : p 1 = p 2 vs. H 1 : p 1 p 2.

SOLUTIONS y n. n 1 = 605, y 1 = 351. y1. p y n. n 2 = 195, y 2 = 41. y p H 0 : p 1 = p 2 vs. H 1 : p 1 p 2. STAT 400 UIUC Practice Problems # SOLUTIONS Stepaov Dalpiaz The followig are a umber of practice problems that may be helpful for completig the homework, ad will likely be very useful for studyig for exams..

More information

Grant MacEwan University STAT 151 Formula Sheet Final Exam Dr. Karen Buro

Grant 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 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

Introduction to Probability and Statistics Twelfth Edition

Introduction to Probability and Statistics Twelfth Edition Itroductio to Probability ad Statistics Twelfth Editio Robert J. Beaver Barbara M. Beaver William Medehall Presetatio desiged ad writte by: Barbara M. Beaver Itroductio to Probability ad Statistics Twelfth

More information

SOLUTIONS. 1. a) Let X and Y be random variables with. σ Y = 9. σ X 30 σ XY σ X 30 ρ σ X σ Y σ X + 24 σ XY

SOLUTIONS. 1. a) Let X and Y be random variables with. σ Y = 9. σ X 30 σ XY σ X 30 ρ σ X σ Y σ X + 24 σ XY STAT 400 UIU Practice Problems #8 Stepaov Dalpiaz SOLUTIONS The followig are a umber of practice problems that may be helpful for completig the homework, ad will likely be very useful for studyig for exams..

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

Topic 9: Sampling Distributions of Estimators

Topic 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 information

Mathacle. PSet Stats, Concepts In Statistics Level Number Name: Date:

Mathacle. 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 information

a.) If random samples of size n=16 are selected, can we say anything about the x~ distribution of sample means?

a.) If random samples of size n=16 are selected, can we say anything about the x~ distribution of sample means? Sectio 7.5 4.) Suppose that x has a distributio with u=72 ad r=8. a.) If radom samples of size =16 are selected, ca we say aythig about the x~ distributio of sample meas? 8 X N 72, N 72, 2 16 b.) If the

More information

Expectation and Variance of a random variable

Expectation and Variance of a random variable Chapter 11 Expectatio ad Variace of a radom variable The aim of this lecture is to defie ad itroduce mathematical Expectatio ad variace of a fuctio of discrete & cotiuous radom variables ad the distributio

More information

SSS1 Class Test 2 Confidence Intervals (2009/2010) (Version: 8 th Oct 2009) (This test does not contribute towards your final mark)

SSS1 Class Test 2 Confidence Intervals (2009/2010) (Version: 8 th Oct 2009) (This test does not contribute towards your final mark) SSS1 Class Test 2 Cofidece Itervals (2009/2010) (Versio: 8 th Oct 2009) (This test does ot cotribute towards your fial mark) 1. Fid the followig probability: Pr(-2 < z < 2) This is the same as 1 mius the

More information

Lecture 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.

Lecture 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 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

AP Statistics Review Ch. 8

AP 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 information

ST 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 ( ) = 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 information

5. A formulae page and two tables are provided at the end of Part A of the examination PART A

5. A formulae page and two tables are provided at the end of Part A of the examination PART A Istructios: 1. You have bee provided with: (a) this questio paper (Part A ad Part B) (b) a multiple choice aswer sheet (for Part A) (c) Log Aswer Sheet(s) (for Part B) (d) a booklet of tables. (a) I PART

More information

Topic 9: Sampling Distributions of Estimators

Topic 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 information

A quick activity - Central Limit Theorem and Proportions. Lecture 21: Testing Proportions. Results from the GSS. Statistics and the General Population

A 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 information

Instructor: Judith Canner Spring 2010 CONFIDENCE INTERVALS How do we make inferences about the population parameters?

Instructor: Judith Canner Spring 2010 CONFIDENCE INTERVALS How do we make inferences about the population parameters? CONFIDENCE INTERVALS How do we make ifereces about the populatio parameters? The samplig distributio allows us to quatify the variability i sample statistics icludig how they differ from the parameter

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

Frequentist Inference

Frequentist Inference Frequetist Iferece The topics of the ext three sectios are useful applicatios of the Cetral Limit Theorem. Without kowig aythig about the uderlyig distributio of a sequece of radom variables {X i }, for

More information

1 Inferential Methods for Correlation and Regression Analysis

1 Inferential Methods for Correlation and Regression Analysis 1 Iferetial Methods for Correlatio ad Regressio Aalysis I the chapter o Correlatio ad Regressio Aalysis tools for describig bivariate cotiuous data were itroduced. The sample Pearso Correlatio Coefficiet

More information

Class 23. Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science. Marquette University MATH 1700

Class 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 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

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

z is the upper tail critical value from the normal distribution

z 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 information

MIT : Quantitative Reasoning and Statistical Methods for Planning I

MIT : 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 information

1036: Probability & Statistics

1036: Probability & Statistics 036: Probability & Statistics Lecture 0 Oe- ad Two-Sample Tests of Hypotheses 0- Statistical Hypotheses Decisio based o experimetal evidece whether Coffee drikig icreases the risk of cacer i humas. A perso

More information

Thomas Whitham Form Centre

Thomas Whitham Form Centre Thomas Whitham Form Cetre Statistics Module . The Uiform Distributio (or rectagular distributio) U(a, b) a b Pdf f x a x b a b otherwise cdf E F x a b var, b a x a, b a, by symmetry x a a x b x b (proof

More information

HYPOTHESIS TESTS FOR ONE POPULATION MEAN WORKSHEET MTH 1210, FALL 2018

HYPOTHESIS TESTS FOR ONE POPULATION MEAN WORKSHEET MTH 1210, FALL 2018 HYPOTHESIS TESTS FOR ONE POPULATION MEAN WORKSHEET MTH 1210, FALL 2018 We are resposible for 2 types of hypothesis tests that produce ifereces about the ukow populatio mea, µ, each of which has 3 possible

More information

Agreement of CI and HT. Lecture 13 - Tests of Proportions. Example - Waiting Times

Agreement 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 information

Review - Week 10. There are two types of errors one can make when performing significance tests:

Review - Week 10. There are two types of errors one can make when performing significance tests: Review - Week Read: Chaper -3 Review: There are wo ype of error oe ca make whe performig igificace e: Type I error The ull hypohei i rue, bu we miakely rejec i (Fale poiive) Type II error The ull hypohei

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

Statistics 511 Additional Materials

Statistics 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 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