501_Lecture_03 - Chapter 2 highlights Association and...

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Chapter 2 highlights Association and Causation
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Association between a pair of variables Association: Some values of one variable tend to occur more often with certain values of the other variable Both variables measured on same set of individuals Examples: Height and weight of same individual Smoking habits and life expectancy Age and bone-density of individuals Gender and political affiliation Gender and Smoking
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Causation? Caution: Often there are spurious, other variables lurking in the background Shorter women have lower risk of heart attack Countries with more TV sets have better life expectancy rates More deaths by drowning occur when ice cream sales peak Just explore association or investigate a causal relationship? Sometimes both?
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Preliminaries: Who are the individuals observed? What variables are present? Quantitative or categorical ? Association measures depend on types of variables Response variable measures outcome of interest Explanatory variable explains and sometimes causes changes in response variable
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Examples Different amount of alcohol given to mice, body temperature noted (belief: drop in body temperature with increasing amount of alcohol) Response variable? Explanatory variable? SAT scores used to predict college GPA Response variable? Explanatory variable?
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Causation Association does not imply causation! An association between x and y, even if it is very strong, is not itself good evidence that changes in x actually cause changes in y. Causation: Variable X directly causes a change in Variable Y Example: X = plant food Y = plant s growth
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Common Response Other variables may affect the relationship between X and Y Beware of lurking variables Example: for children, X = height Y = Math Score Z =
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Confounding Other variables may affect the relationship between X and Y Can t separate effects of X and Z on Y Example: X = number years of education Y = income Z =
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Section 3.1 Producing Data
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Topics Exploratory Data Analysis vs. Statistical Inference Available data Sampling Experiments vs. observational studies Design of experiments Randomization
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Exploratory Data Analysis vs. Statistical Inference Exploratory Data Analysis reveals interesting features of data but may not be sufficient for definite conclusions more graphical What is the credibility of what I see? How trustworthy are my findings? Statistical Inference gives answers with a controlled degree of confidence more numerical Relies heavily on properly collected data
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Where to collect data? Reliable sources of data:
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This note was uploaded on 02/20/2012 for the course STAT 501 taught by Professor Staff during the Spring '08 term at Purdue University-West Lafayette.

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501_Lecture_03 - Chapter 2 highlights Association and...

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