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

Similar documents
Statistical Inference Procedures

Tools Hypothesis Tests

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

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.

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

Chapter 9. Key Ideas Hypothesis Test (Two Populations)

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

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

Stat 3411 Spring 2011 Assignment 6 Answers

STA 4032 Final Exam Formula Sheet

20. CONFIDENCE INTERVALS FOR THE MEAN, UNKNOWN VARIANCE

TESTS OF SIGNIFICANCE

IntroEcono. Discrete RV. Continuous RV s

REVIEW OF SIMPLE LINEAR REGRESSION SIMPLE LINEAR REGRESSION

Common Large/Small Sample Tests 1/55

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

UNIVERSITY OF CALICUT

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

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

Chapter 9: Hypothesis Testing

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

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

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

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

Chapter 8.2. Interval Estimation

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

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

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:

Estimation Theory. goavendaño. Estimation Theory

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.

Statistical Equations

Difference tests (1): parametric

Formula Sheet. December 8, 2011

Topic 9: Sampling Distributions of Estimators

18.05 Problem Set 9, Spring 2014 Solutions

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

TI-83/84 Calculator Instructions for Math Elementary Statistics

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.

Topic 9: Sampling Distributions of Estimators

Topic 9: Sampling Distributions of Estimators

ENGI 4421 Confidence Intervals (Two Samples) Page 12-01

Stat 200 -Testing Summary Page 1

Confidence Intervals. Confidence Intervals

(7 One- and Two-Sample Estimation Problem )

Statistics Parameters

Important Formulas. Expectation: E (X) = Σ [X P(X)] = n p q σ = n p q. P(X) = n! X1! X 2! X 3! X k! p X. Chapter 6 The Normal Distribution.

ON THE SCALE PARAMETER OF EXPONENTIAL DISTRIBUTION

STA Learning Objectives. Population Proportions. Module 10 Comparing Two Proportions. Upon completing this module, you should be able to:

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

MATH/STAT 352: Lecture 15

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

STAT431 Review. X = n. n )

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.

Statistical Inference (Chapter 10) Statistical inference = learn about a population based on the information provided by a sample.

Chapter 22. Comparing Two Proportions. Copyright 2010 Pearson Education, Inc.

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

Société de Calcul Mathématique, S. A. Algorithmes et Optimisation

t distribution [34] : used to test a mean against an hypothesized value (H 0 : µ = µ 0 ) or the difference

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

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

Chapter 13: Tests of Hypothesis Section 13.1 Introduction

Class 27. Daniel B. Rowe, Ph.D. Department of Mathematics, Statistics, and Computer Science. Marquette University MATH 1700

Chapter 22. Comparing Two Proportions. Copyright 2010, 2007, 2004 Pearson Education, Inc.

[ ] ( ) ( ) [ ] ( ) 1 [ ] [ ] Sums of Random Variables Y = a 1 X 1 + a 2 X 2 + +a n X n The expected value of Y is:

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

- 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

October 25, 2018 BIM 105 Probability and Statistics for Biomedical Engineers 1

Chapter 6 Sampling Distributions

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

LECTURE 13 SIMULTANEOUS EQUATIONS

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

Samples from Normal Populations with Known Variances

Chapter 8: Estimating with Confidence

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

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

BIOSTATISTICS. Lecture 5 Interval Estimations for Mean and Proportion. dr. Petr Nazarov

McNemar s Test and Introduction to ANOVA

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

STATISTICAL INFERENCE

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

Statistical Intervals Based on a Single Sample (Devore Chapter Seven)

Elementary Statistics

Properties and Hypothesis Testing

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

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

Statistics 511 Additional Materials

Chapter 13, Part A Analysis of Variance and Experimental Design

The variance of a sum of independent variables is the sum of their variances, since covariances are zero. Therefore. V (xi )= n n 2 σ2 = σ2.

Final Examination Solutions 17/6/2010

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

Chapters 5 and 13: REGRESSION AND CORRELATION. Univariate data: x, Bivariate data (x,y).

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

Expectation and Variance of a random variable

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

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

Sample Size Determination (Two or More Samples)

Comparing Means: t-tests for Two Independent Samples

Suggested Answers To Exercises. estimates variability in a sampling distribution of random means. About 68% of means fall

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

Econ 325/327 Notes on Sample Mean, Sample Proportion, Central Limit Theorem, Chi-square Distribution, Student s t distribution 1.

Transcription:

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 two mea with idepedet ample whe populatio variace are ot equal Tet two proportio with idepedet ample. INTRODUCTION There are may itace that require the compario betwee two mea or two proportio: o Compare the average tread wear betwee two et of tire. o Compare the effectivee of two differet fertilizer. o Compare the proportio of me who exercie regularly to the proportio of wome who exercie regularly. The baic tep of hypothei tetig are ued whe we compare two mea or two proportio. The cae that we will examie i thi chapter are: o Two mea with idepedet ample whe the two populatio variace are kow (z tet with all three method of hypothei tetig: Traditioal, P-value, Cofidece Iterval.) o Two variace with idepedet ample - F tet. o Two mea with idepedet ample whe the populatio variace are ot kow (t tet with all three method of hypothei tetig: Traditioal, P-value, Cofidece Iterval.) Whe variace are ot kow, we ditiguih betwee two cae: The two populatio variace are equal The two populatio variace are ot equal o Two populatio with idepedet ample (z tet with all three method of hypothei tetig: Traditioal, P-value, Cofidece Iterval.) The BIG differece i i the defiitio ad computatio of the TEST VALUE. NOTATION: We ue the ame otatio we have bee uig o far for the variou tatitic ad parameter with the additio of two ubcript: Subcript 1 deote parameter for Populatio 1 ad tatitic for ample derived from Populatio 1. Subcript deote parameter for Populatio ad tatitic for ample derived from Populatio. Sectio 9-1 1 4/0/007

M7 Chapter 9 Sectio TWO MEANS INDEPENDENT (large) SAMPLES σ 1 & σ KNOWN or 1, 30 Tet Value: for the Traditioal or P-value method, ue the z-ditributio with the followig Tet Value: Tet Value z = ( X 1 X ) ( µ 1 µ ) σ 1 σ + Null ad Alterative Hypothee: Two-Tailed: H 0 : µ 1 = µ (or µ 1 µ = 0) H 1: µ 1 µ (or µ 1 µ 0) Right-Tailed: H 0 : µ 1 = µ (or µ 1 µ = 0) H 1: µ 1 > µ (or µ 1 µ > 0) Left-Tailed: H 0 : µ 1 = µ (or µ 1 µ = 0) H 1: µ 1 < µ (or µ 1 µ < 0 ) Cofidece Iterval ca be cotructed a follow: Error 1 σ σ E = zα + σ1 σ σ1 σ α Cofidece Iterval: ( ) α µ µ ( ) X X z + < < X X + z + Example 9-1, 9-, 9-3. Excel: Ue MEGASTAT > Hypothei Tet > Compare Two Idepedet Group, z-tet. Sectio 9-4/0/007

M7 Chapter 9 Sectio 3 TWO VARIANCES INDEPENDENT SAMPLES Ue F tet. F ditributio : i a amplig ditributio of the ratio 1 of the variace of two ample elected from two ormally ditributed populatio with equal populatio variace. Propertie of F Ditributio o The value of F caot be egative, becaue variace are alway poitive or zero o The ditributio i poitively kewed. o The mea value of F i approximately equal to 1. o The F ditributio i a family of curve baed o the degree of freedom of the variace of the umerator ad the degree of freedom of the variace of the deomiator F Ditributio F tet: F = 1, where 1 i the larget of the two variace i quetio. F Tet ha two degree of freedom, oe for the umerator, ad oe for the deomiator, deoted repectively a: d.f.n = 1-1. ad d.f.d = -1. Ue Table H, i Appedix C, or the Excel fuctio: FDIST ad FINV The table ue oe-tailed value for the α. Thu, if a two-tailed tet i eeded, ay with α = 0.05, the ue the α = 0.05 table etry. Hypothee for variace tetig: Right-Tailed Left-Tailed Two-Tailed 0 : 1 = 1 : 1 > 0 : 1 = 1 : 1 Sectio 9-3 3 4/0/007

M7 Chapter 9 Sectio 3 Note o the Ue of the F Tet The larget variace hould alway be placed i the umerator For a two=tailed tet, the α value mut be divided by ad the critical value placed o the right ide of the F curve. If the tadard deviatio itead f the variace are give i the problem, they mut be quared for the formula for the F tet. If the degree of freedom caot be foud i the table, ue the cloet value o the maller ide I Hypothei Tetig, for the tet value ue the F value The aumptio that the larger variace i i the umerator, make ueceary to fid the left tail of the critical regio; i additio, left-tailed tet doe ot have a meaig. Aumptio for Tetig the Differece Betwee two Variace The populatio from which the ample were obtaied mut be ormally ditributed The ample mut be idepedet of each other Example 9-4, 9 5; alo ue Excel. Example 9-6. NOTE: Calculatig P-value through the table i a rather tediou tak. Ue the EXCEL fuctio FDIST itead, if there i a eed to ue the P-value method. Sectio 9-3 4 4/0/007

M7 Chapter 9 Sectio 4 TWO MEANS INDEPENDENT (Small) SAMPLES σ 1 & σ NOT Kow AND Oe or Both Sample Size Are Le Tha 30 Ue t ditributio Two cae: o Populatio variace are equal o Populatio variace ot equal. Whe problem doe ot tate whether the populatio variace are equal or ot, there are two optio: o Aume that they are NOT equal o Ue F tet firt. Ue F tet oly if the problem pecifically ak you to ue it! Otherwie, aume that the two populatio variace are ot equal. Tet Value for Uequal Variace: t = ( X 1 X ) ( µ 1 µ ) 1 +, d. f. = mi ( 1, 1) Tet Value for Equal Variace: t = (Sometime called pooled variace) ( X 1 X ) ( µ 1 µ ) 1 1 + ( 1) ( 1) 1 1 + + d. f. = +, Example 9-9, 9-10 Ue MEGASTAT > Hypothei Tet > Compare Two Idepedet Group ad chooig the appropriate t-tet. X X E < < X X + E, I order to calculate Cofidece Iterval: ( ) µ µ ( ) ue the followig formula for the error: Uequal Variace: 1 E = tα + with d. f. = mi ( 1 1, 1) Equal Variace: ( ) + ( ) 1 1 1 1 1 1 E = tα + + with d. f. = 1 + Sectio 9-4 5 4/0/007

M7 Chapter 9 Sectio 6 TWO PROPORTIONS INDEPENDENT SAMPLES Hypothee: Two-Tailed: H0 : p1 = p (or p1 p = 0 ) H1: p1 p (or p1 p 0 ) Right-Tailed: H0 : p1 = p (or p1 p = 0 ) H1: p1 > p (or p1 p > 0 ) Left-Tailed: H0 : p1 = p (or p1 p = 0 ) H1: p1 < p (or p1 p < 0) Tet Value for Traditioal ad P-value method ( pˆ Tet Value: 1 pˆ ) ( p1 p) z =, where: X 1 + p = X, pˆ 1 = X, q = 1 p, pˆ = X 1 1 1 + pq + 1 Example 9-15. Do ame Example uig MEGASTAT Cofidece Iterval pˆ Error 1qˆ 1 pˆ qˆ E = z α + pˆ Iterval: 1qˆ 1 pˆ qˆ ( ˆ pˆ p1 pˆ ) zα + < p 1 p < 1qˆ 1 pˆ qˆ ( pˆ 1 pˆ ) + zα + Sectio 9-6 6 4/0/007