MATH119

# MATH119 - (Lesson 1 Intro Ch.1 1.01 CHAPTER 1 INTRODUCTION...

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

(Lesson 1: Intro; Ch.1) 1.01 CHAPTER 1: INTRODUCTION LESSON 1: INTRODUCTION PART A: STRUCTURE OF THE TEXT ( 4 TH EDITION OF TRIOLA’S ESSENTIALS) Statistics Probability (Chapters 4-6) | 1) Designing an Experiment | (Chapter 1) | | 2) Collecting Data | (Chapter 1) | | 3) Describing Data | (Chapters 2, 3) | | 4) Interpreting Data using ±²²²²²² (Chapters 3, 7-11) S i z e : N elements (or members) Population [of interest] All adult Americans? All registered voters in California? 2) ± ± 4) S i z e : n elements (or members) Sample For a poll? A scientific study? T h e N i e l s e n s ? 3) The population must be carefully defined. For example … • Do “adult Americans” include illegal immigrants? • Different polls of “likely U.S. voters” use different models for the purposes of screening poll respondents. Voter enthusiasm and voting history may be issues.

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

View Full Document
1.02 PART B: COLLECTING DATA; SAMPLING METHODS If we manage to collect data from each element of the population, we have ourselves a census . Often, a census is impossible or impractical, so we collect data on only some elements of the population. These elements make up a sample from the population. How do we select a sample so that it is representative of the overall population? Section 1-4 details a number of methods. We will discuss related issues later, when we discuss polls in Chapter 7 . A common problem in practice is the use of overly homogeneous samples in which the elements within the sample are much more similar than is the case within the overall population. For example, you wouldn’t want to restrict your sample to a single state if you wanted to study the national popularity of the President. We will typically assume that a sample from a population is a simple random sample (SRS) . When constructing an SRS, each group of n elements in the population is equally likely to be our selected sample of n elements. Example If n = 4 , then the two samples below (each represented by four black squares) are equally likely to be selected: Note : When constructing a random sample in general, each individual element is equally likely to be among those selected for the sample, but some groups of n elements may be more likely to be selected than others. Selections could be linked.
This is the end of the preview. Sign up to access the rest of the document.

## This note was uploaded on 09/08/2011 for the course MATH 119 taught by Professor Staff during the Spring '11 term at Mesa CC.

### Page1 / 12

MATH119 - (Lesson 1 Intro Ch.1 1.01 CHAPTER 1 INTRODUCTION...

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

View Full Document
Ask a homework question - tutors are online