Lecture13 - lecture 13, Samples and Surveys, Sampling...

Info iconThis preview shows pages 1–5. Sign up to view the full content.

View Full Document Right Arrow Icon

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

View Full DocumentRight Arrow Icon

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

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: lecture 13, Samples and Surveys, Sampling Distributions I 1/21 Samples and Surveys Sampling Distribution Sampling Distribution of the Sample Mean Central Limit Theorem (CLT) lecture 13, Samples and Surveys, Sampling Distributions I Outline 1 Samples and Surveys 2 Sampling Distribution Sampling Distribution of the Sample Mean 3 Central Limit Theorem (CLT) lecture 13, Samples and Surveys, Sampling Distributions I 2/21 Samples and Surveys Sampling Distribution Sampling Distribution of the Sample Mean Central Limit Theorem (CLT) lecture 13, Samples and Surveys, Sampling Distributions I Samples and Surveys Samples • Want to learn some numerical facts about a population (called population parameters , e.g., μ , σ ) • Census (A comprehensive survey of the entire population) • Cost and time constraints generally prohibit carrying out a census; in some cases a census is not feasible. • Examine part of the population, called the sample , and make inferences from the sample about the population • Parameters are estimated by sample statistics , i.e., numbers which can be computed from a sample (e.g., ¯ x , s ) Estimate = Parameter + Bias + Chance Error • Bias: systematic error in an estimate. Bias should be avoided as much as possible in survey, by choosing samples that reflect the mix in the entire population (be representative ) • How sample is chosen matters a lot. lecture 13, Samples and Surveys, Sampling Distributions I 3/21 Samples and Surveys Sampling Distribution Sampling Distribution of the Sample Mean Central Limit Theorem (CLT) lecture 13, Samples and Surveys, Sampling Distributions I Samples and Surveys The Literary Digest Case, revisited • 1936 election: Roosevelt vs Landon; • Roosevelt’s percentage: The Digest prediction: 43%; The election result: 62%; • Literary Digest interviewed 2.4 million potential voters (the largest number of people ever replying to a poll!) (total voted: about 45 million) • Literary Digest went bankrupt soon after the 1936 presidential election... • In contrast, Gallup interviewed only 50,000 people, and correctly forecast the Roosevelt victory ( Gallup predicted 56%). lecture 13, Samples and Surveys, Sampling Distributions I 4/21 Samples and Surveys Sampling Distribution Sampling Distribution of the Sample Mean Central Limit Theorem (CLT) lecture 13, Samples and Surveys, Sampling Distributions I Samples and Surveys Example, The Literary Digest, revisited, ctd Q: What went wrong with Literary Digest ’s method? • Literary Digest ’s sampling procedure: mailed questionnaires to 10 million people on telephone books and club membership lists, about 24% responded; I Mistake #1: in 1936, poor people were more likely not to have telephones or join clubs; • Systematic tendency in the sampling procedure to exclude one kind of person or another from the sample — selection bias ; I Mistake #2: about 76% surveyed people did not respond, and only the respondents were taken into account....
View Full Document

This note was uploaded on 12/28/2010 for the course BUSINESS A ISOM 111 taught by Professor Yingyingli during the Fall '10 term at HKUST.

Page1 / 21

Lecture13 - lecture 13, Samples and Surveys, Sampling...

This preview shows document pages 1 - 5. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online