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Chapter 8: Sampling to Produce Data; Chapter 9: Experiments to Produce Data

by: Rachel Kasashima

Chapter 8: Sampling to Produce Data; Chapter 9: Experiments to Produce Data MATH 243

Marketplace > University of Oregon > Mathematics (M) > MATH 243 > Chapter 8 Sampling to Produce Data Chapter 9 Experiments to Produce Data
Rachel Kasashima
GPA 3.95
Intro Probability and Statistics
Harker H

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Chapter notes on powerpoint
Intro Probability and Statistics
Harker H
Class Notes
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This 13 page Class Notes was uploaded by Rachel Kasashima on Monday October 19, 2015. The Class Notes belongs to MATH 243 at University of Oregon taught by Harker H in Fall 2015. Since its upload, it has received 23 views. For similar materials see Intro Probability and Statistics in Mathematics (M) at University of Oregon.

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Date Created: 10/19/15
Chapter 8 Sampling to Produce Data De nitions The population in a statistical study is the entire group of individuals sample is a part of the population from Which we collect information sampling design describes exactly how to choose a sample from the popula tion To plan a sample survey we need to know 0 What population we want to describe 0 What we want to measure Then we need the sample design Example political scientist wants to know how population college studean feel about the Social Security system She obtains a list of the undergraduates at her college and mails questions to 250 students se lected at random Only 104 question naires are returned 8mm Problem omv obtatneot info from Students at her college 0 sample selected by taking the mem bers of the population that are easiest to reach is called a convenience sample o voluntary response sample consists of people Who choose themselves by responding to a broad appeal o The design of a statistical study is biased if it systematically favors an outcome SRS simple random sample SRS of size consists of individuals from the pop ulation chosen in such a way that every set of individuals has an equal chance to be the sample actually selected o n SRS is like drawing names from a hat 0 n SRS does not favor any part of the population o n SRS is better than convenience or voluntary sampling o The process of drawing conclusions about a population on the basis of sam ple data is called inference 0 Random sampling is used to elimi nate bias in selecting samples which allows for trustworthy inference about the population 0 Larger random samples are better Cautions When Choosing your sample you need to be concerned about undercoverage nonresponse response bias and wording of questions Chapter 9 Experiments to Produce Data n observational study observes indi viduals and measures variables of in terest but does not attempt to in uence the responses n experiment deliberately imposes some treatment on individuals in order to ob serve their responses De nitions Two variables explanatory or lurking are confounded When their effects on a response variable cannot be distinguished from each other The individuals in an experiment are often called subjects The explanatory variables are often called factors treatment is any speci c experimen tal condition applied to the subjects Vamwmmmr When considering the outcome of an experiment an observed effect so large that it would rarely occur by chance is called statistically signi cant 9 The basic principles of statistical de sign of experiments are 0 Control the effects of lurking variables on the response most simply by com paring two are more treatments 0 Randomize use chance to assign sub jects to treatments 0 Use enough subjects to reduce chance variation in the results Matched pairs design matched pairs design compares two treatments Choose pairs of subjects that are as closely matched as possible Use chance to decide Which subject in a pair gets the rst treatment Sometimes each quotpairquot in a matched pairs design consists of just one subject Who gets both treatments The order of the treatments is randomized


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