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Chapter 3 Notes - MAT 221

by: Niki Neidhart

Chapter 3 Notes - MAT 221 MAT 221 - M200

Marketplace > Syracuse University > Math > MAT 221 - M200 > Chapter 3 Notes MAT 221
Niki Neidhart
GPA 3.78

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Chapter 3 notes - about producing data through observational and experimental studies
Elements of Mathematical Statistics and Probability Theory
X. Au
Class Notes
math; statistics; response; observational; experimental
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This 2 page Class Notes was uploaded by Niki Neidhart on Tuesday February 9, 2016. The Class Notes belongs to MAT 221 - M200 at Syracuse University taught by X. Au in Spring 2016. Since its upload, it has received 9 views. For similar materials see Elements of Mathematical Statistics and Probability Theory in Math at Syracuse University.


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Date Created: 02/09/16
Chapter 3 Producing Data – MAT 221 3.1 & 3.2 - Sources of Data & Design of Experiments Anecdotal Data – unusual cases that we draw conclusions from past experiences - May not me representative of any larger group of cases Available Data – past data we produced that may help us Population – the entire group of individuals we are studying Parameter – the part of the population we are studying and have data for Statistic – a number describing a characteristic of a sample Experimental units – the individuals in an experiment - Called Subjects if they are human - Treatment or factor: the “something” we do to a subject that’s response gets measured Observational Study: Simply observing and recording data of individuals without influencing responses - Cannot establish cause and effect relationships Experimental Study – Deliberately giving individuals a sort of treatment and recording their responses Control – a situation where no treatment is given; serves as reference mark/basis Placebo – a fake treatment to test that the results are from the actual treatment and not the subjects belief that they are being treated Ronald Fisher (1890 – 1962) – randomized comparative experiments; fertilizer Principles of Experimental Design: 1. Control the effects of lurking variables 2. Randomize 3. Replicate treatment on enough subjects to reduce chance of variation in results Biased – systematically favoring certain outcomes - Random assignment is the best way to avoid Blind experiment – one in which the subjects do not know which treatment they are getting until the experiment it completed Double-Blind Experiment – neither the subjects or the experimenter know who has the treatment until the experiment is over 3.3 – Sampling Design - We don’t always get a response from everyone in our sample - Response Bias: people don’t always respond truthfully - Wording effects: the way a question is worded may influence a certain response 3.4 – Toward Statistical Inference Statistical Inference – the process of drawing conclusions about a population from data obtained from a sample Sampling Variability – every time we take a random sample from a population we are likely to get a different set of individuals/ statistics Sampling Distribution – the distribution achieved by repeating the study many times with the same sample size - The larger the sample size, the lower the sample variability - The better the data-collecting technique, the lower the bias


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