Statistics for Business and Economics

Similar documents
Chapter 8. Confidence Interval Estimation. Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 8, Slide 1

Chapter 10 - Lecture 2 The independent two sample t-test and. confidence interval

Sampling Distributions and Estimation

Statistics for Economics & Business

A point estimate is the value of a statistic that estimates the value of a parameter.

Math 124: Lecture for Week 10 of 17

Topic-7. Large Sample Estimation

ii. Interval estimation:

Inferential Statistics and Probability a Holistic Approach. Inference Process. Inference Process. Chapter 8 Slides. Maurice Geraghty,

Confidence Intervals Introduction

NOTES ON ESTIMATION AND CONFIDENCE INTERVALS. 1. Estimation

CHAPTER 8 Estimating with Confidence

Today: Finish Chapter 9 (Sections 9.6 to 9.8 and 9.9 Lesson 3)

point estimator a random variable (like P or X) whose values are used to estimate a population parameter

BASIC STATISTICS ECOE 1323

Lecture 5 Point Es/mator and Sampling Distribu/on

CHAPTER 8: CONFIDENCE INTERVAL ESTIMATES for Means and Proportions

B = A x z

Introduction to Probability and Statistics Chapter 7

Estimating Proportions with Confidence

Chapter 8: Estimation of Mean & Proportion. Introduction

Chapter 8 Interval Estimation. Estimation Concepts. General Form of a Confidence Interval

. (The calculated sample mean is symbolized by x.)

Exam 2. Instructor: Cynthia Rudin TA: Dimitrios Bisias. October 25, 2011

Sampling Distributions & Estimators

CHAPTER 8: CONFIDENCE INTERVAL ESTIMATES for Means and Proportions

Chapter 10 Statistical Inference About Means and Proportions With Two Populations. Learning objectives

A random variable is a variable whose value is a numerical outcome of a random phenomenon.

Standard Deviations for Normal Sampling Distributions are: For proportions For means _

14.30 Introduction to Statistical Methods in Economics Spring 2009

Sampling Distributions and Estimation

1 Random Variables and Key Statistics

Confidence Intervals. CI for a population mean (σ is known and n > 30 or the variable is normally distributed in the.

BIOSTATS 540 Fall Estimation Page 1 of 72. Unit 6. Estimation. Use at least twelve observations in constructing a confidence interval

Lecture 4: Parameter Estimation and Confidence Intervals. GENOME 560 Doug Fowler, GS

5 Statistical Inference

Basic formula for confidence intervals. Formulas for estimating population variance Normal Uniform Proportion

Variance and Standard Deviation (Tables) Lecture 10

5. Best Unbiased Estimators

ST 305: Exam 2 Fall 2014

1. Suppose X is a variable that follows the normal distribution with known standard deviation σ = 0.3 but unknown mean µ.

Introduction to Statistical Inference

Lecture 4: Probability (continued)

Outline. Populations. Defs: A (finite) population is a (finite) set P of elements e. A variable is a function v : P IR. Population and Characteristics

18.S096 Problem Set 5 Fall 2013 Volatility Modeling Due Date: 10/29/2013

Exam 1 Spring 2015 Statistics for Applications 3/5/2015

Parametric Density Estimation: Maximum Likelihood Estimation

AY Term 2 Mock Examination

4.5 Generalized likelihood ratio test

1. Find the area under the standard normal curve between z = 0 and z = 3. (a) (b) (c) (d)

The Idea of a Confidence Interval

x satisfying all regularity conditions. Then

A Bayesian perspective on estimating mean, variance, and standard-deviation from data

CHAPTER 8 CONFIDENCE INTERVALS

Lecture 5: Sampling Distribution

APPLIED STATISTICS Complementary Course of BSc Mathematics - IV Semester CUCBCSS Admn onwards Question Bank

Confidence Intervals based on Absolute Deviation for Population Mean of a Positively Skewed Distribution

Chpt 5. Discrete Probability Distributions. 5-3 Mean, Variance, Standard Deviation, and Expectation

Models of Asset Pricing

Online appendices from Counterparty Risk and Credit Value Adjustment a continuing challenge for global financial markets by Jon Gregory

Data Analysis and Statistical Methods Statistics 651

r i = a i + b i f b i = Cov[r i, f] The only parameters to be estimated for this model are a i 's, b i 's, σe 2 i

These characteristics are expressed in terms of statistical properties which are estimated from the sample data.

Bayes Estimator for Coefficient of Variation and Inverse Coefficient of Variation for the Normal Distribution

Quantitative Analysis

Dr. Maddah ENMG 624 Financial Eng g I 03/22/06. Chapter 6 Mean-Variance Portfolio Theory

An Empirical Study of the Behaviour of the Sample Kurtosis in Samples from Symmetric Stable Distributions

CAPITAL ASSET PRICING MODEL

Control Charts for Mean under Shrinkage Technique

Point Estimation by MLE Lesson 5

SCHOOL OF ACCOUNTING AND BUSINESS BSc. (APPLIED ACCOUNTING) GENERAL / SPECIAL DEGREE PROGRAMME


Probability and Statistical Methods. Chapter 8 Fundamental Sampling Distributions

Probability and Statistical Methods. Chapter 8 Fundamental Sampling Distributions

Models of Asset Pricing

Models of Asset Pricing

DOWLING COLLEGE: School of Education Department of Educational Administration, Leadership, and Technology

Point Estimation by MLE Lesson 5

Chapter 17 Sampling Distribution Models

Question 1 (4 points) A restaurant manager is developing a clientele profile. Some of the information for the profile follows:

Appendix 1 to Chapter 5

= α e ; x 0. Such a random variable is said to have an exponential distribution, with parameter α. [Here, view X as time-to-failure.

1036: Probability & Statistics

of Asset Pricing R e = expected return

of Asset Pricing APPENDIX 1 TO CHAPTER EXPECTED RETURN APPLICATION Expected Return

Topic 14: Maximum Likelihood Estimation

FINM6900 Finance Theory How Is Asymmetric Information Reflected in Asset Prices?

Unbiased estimators Estimators

CHANGE POINT TREND ANALYSIS OF GNI PER CAPITA IN SELECTED EUROPEAN COUNTRIES AND ISRAEL

Parameter Uncertainty in Loss Ratio Distributions and its Implications

Rafa l Kulik and Marc Raimondo. University of Ottawa and University of Sydney. Supplementary material

Subject CT1 Financial Mathematics Core Technical Syllabus

Quantitative Analysis

SOLUTION QUANTITATIVE TOOLS IN BUSINESS NOV 2011

Just Lucky? A Statistical Test for Option Backdating

0.1 Valuation Formula:

Estimating the Parameters of the Three-Parameter Lognormal Distribution

I. Measures of Central Tendency: -Allow us to summarize an entire data set with a single value (the midpoint).

1 Estimating the uncertainty attached to a sample mean: s 2 vs.

ECON 5350 Class Notes Maximum Likelihood Estimation

Transcription:

Statistics for Busiess ad Ecoomics Chapter 8 Estimatio: Additioal Topics Copright 010 Pearso Educatio, Ic. Publishig as Pretice Hall Ch. 8-1

8. Differece Betwee Two Meas: Idepedet Samples Populatio meas, idepedet samples Goal: Form a cofidece iterval for the differece betwee two populatio meas, µ µ Differet data sources Urelated Idepedet Sample selected from oe populatio has o effect o the sample selected from the other populatio The poit estimate is the differece betwee the two sample meas: Copright 010 Pearso Educatio, Ic. Publishig as Pretice Hall Ch. 8-

σ ad σ Ukow, Assumed Equal Populatio meas, idepedet samples σ ad σ kow σ ad σ ukow σ ad σ assumed equal σ ad σ assumed uequal * Assumptios: Samples are radoml ad idepedetl draw Populatios are ormall distributed Populatio variaces are ukow but assumed equal Copright 010 Pearso Educatio, Ic. Publishig as Pretice Hall Ch. 8-3

σ ad σ Ukow, Assumed Equal (cotiued Populatio meas, idepedet samples σ ad σ kow σ ad σ ukow σ ad σ assumed equal σ ad σ assumed uequal * Formig iterval estimates: The populatio variaces are assumed equal, so use the two sample stadard deviatios ad pool them to estimate σ use a t value with ( + degrees of freedom Copright 010 Pearso Educatio, Ic. Publishig as Pretice Hall Ch. 8-4

σ ad σ Ukow, Assumed Equal (cotiued Populatio meas, idepedet samples σ ad σ kow The pooled variace is σ ad σ ukow σ ad σ assumed equal * s p = ( 1s + + ( 1s σ ad σ assumed uequal Copright 010 Pearso Educatio, Ic. Publishig as Pretice Hall Ch. 8-5

Cofidece Iterval, σ ad σ Ukow, Equal σ ad σ ukow σ ad σ assumed equal σ ad σ assumed uequal * The cofidece iterval for µ 1 µ is: ( s s t p p p,α/ µ X µ Y ( t + + < < + +,α/ + s s p Where s p = ( 1s + ( + 1s Copright 010 Pearso Educatio, Ic. Publishig as Pretice Hall Ch. 8-6

Pooled Variace Eample Stadardized tests are take b studets from large ( ad small ( high schools. Form a cofidece iterval for the differece i scores. You collect the followig data: Score Score Number Obs. 9 15 Sample mea 81.31 78.61 Sample var. 60.76 48.4 Assume both populatios are ormal with equal variaces, ad use 95% cofidece Copright 010 Pearso Educatio, Ic. Publishig as Pretice Hall Ch. 8-7

Calculatig the Pooled Variace The pooled variace is: ( S p = 1S + ( 1S ( 1+ ( 1 = 8 60.76 +14 48.4 = 5.79 The t value for a 95% cofidece iterval is: t +, α / = t, 0.05 =.074 Copright 010 Pearso Educatio, Ic. Publishig as Pretice Hall Ch. 8-8

Calculatig the Cofidece Limits The 95% cofidece iterval is ( ± t +,α / s p + s p (81.31 78.61 ± (.074 5.79 9 + 5.79 15 3.65 < µ X µ Y < 9.05 We are 95% cofidet that the mea differece i scores is betwee -3.65 ad 9.05. Copright 010 Pearso Educatio, Ic. Publishig as Pretice Hall Ch. 8-9

8.3 Two Populatio Proportios Populatio proportios Goal: Form a cofidece iterval for the differece betwee two populatio proportios, p p Assumptios: Both sample sizes are large The poit estimate for the differece is Copright 010 Pearso Educatio, Ic. Publishig as Pretice Hall Ch. 8-10

Two Populatio Proportios (cotiued Populatio proportios The radom variable Z = ( (1 (p + p (1 is approimatel ormall distributed Copright 010 Pearso Educatio, Ic. Publishig as Pretice Hall Ch. 8-11

Cofidece Iterval for Two Populatio Proportios Populatio proportios The cofidece limits for p p are: ˆ ± Zα / (p (1 + (1 Copright 010 Pearso Educatio, Ic. Publishig as Pretice Hall Ch. 8-1

Eample: Two Populatio Proportios Form a 90% cofidece iterval for the differece betwee the proportio of me ad the proportio of wome who have college degrees. I a radom sample, 6 of 50 me ad 8 of 40 wome had a eared college degree Copright 010 Pearso Educatio, Ic. Publishig as Pretice Hall Ch. 8-13

Eample: Two Populatio Proportios Me: 6 = = 50 0.5 (cotiued Wome: 8 = = 40 0.70 (1 (1 0.5(0.48 50 0.70(0.30 40 + = + = 0.101 For 90% cofidece, Z α/ = 1.645 Copright 010 Pearso Educatio, Ic. Publishig as Pretice Hall Ch. 8-14

Eample: Two Populatio Proportios (cotiued The cofidece limits are: ( ± Z α/ (1 + (1 = (.5.70 ± 1.645 (0.101 so the cofidece iterval is -0.3465 < P P < -0.0135 Sice this iterval does ot cotai zero we are 90% cofidet that the two proportios are ot equal Copright 010 Pearso Educatio, Ic. Publishig as Pretice Hall Ch. 8-15