{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

Chap10-Introduction-to-Estimation

# Chap10-Introduction-to-Estimation - 1 Chap 10 Intro to...

This preview shows pages 1–3. Sign up to view the full content.

1 CHAPTER 10: INTRODUCTION TO ESTIMATION Stats & Prob. for Bus. Mgmt (Stat1100) Jochem Chap 10: Intro to Estimation box2 Overview/Goals box5 1. Understand the 2 Types of Estimators box5 2. Be able to estimate the Population Mean when the Population Standard Deviation is known 2 box5 3. Be able to Determine the appropriate Sample Size 3 Chap 10: Intro to Estimation box2 0. Where we left off Population & Parameters Individual Member of Population Probability distribution Chap 6-8 Population & Parameters Sample Statistics Sampling distribution Population & Parameters Sample Statistics Sampling distribution Chap 9 4 Chap 10: Intro to Estimation box2 1. Types of Estimators box5 Point Estimator square6 Estimates the value of an unknown parameter through a single value/point. Problems: square6 square6 1. it is unlikely (in fact, the probability is 0) that an estimator that is based on a sample that is smaller than the population has the exact same value as a continuous statistic. square6 2. We have no information about how close the estimator is to the true parameter. square6 3. Point estimators do not reflect the greater accuracy that originates from an increasing sample size.

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
5 Chap 10: Intro to Estimation box2 1. Types of Estimators box5 Interval Estimator square6 Using an interval as an estimation to an unknown parameter (e.g., “ μ lies in between 50.3 and 50.7”). 6 Chap 10: Intro to Estimation box2 1. Concepts of Estimation box5 3 desirable Characteristics for an Estimator square6 1. Unbiasedness square6 If we repeatedly drew samples from the population, then for each sample it is equally likely that the estimate is above or below the true parameter. barb2right expected value of an unbiased estimator across many samples is equal to the true parameter. 7 Chap 10: Intro to Estimation box2 1. Concepts of Estimation box5 3 desirable Characteristics for an Estimator square6 1. Unbiasedness square6 If we repeatedly drew samples from the population, then for each sample it is equally likely that the estimate is above or below the true parameter. barb2right expected value of an unbiased estimator across many samples is equal to the true parameter. square6 2. Consistency square6 the difference between the estimator and the parameter grows smaller as the sample size increases. 8 Chap 10: Intro to Estimation box2 1. Concepts of Estimation box5 3 desirable Characteristics for an Estimator square6 1. Unbiasedness square6 If we repeatedly drew samples from the population, then for each sample it is equally likely that the estimate is above or below the true parameter. barb2right expected value of an unbiased estimator across many samples is equal to the true parameter. square6 2. Consistency square6 the difference between the estimator and the parameter grows smaller as the sample size increases.
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

### Page1 / 8

Chap10-Introduction-to-Estimation - 1 Chap 10 Intro to...

This preview shows document pages 1 - 3. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online