# Pg. 268 - 285. Exploratory data analysis describes what data say by using graphs and numerical summaries. What if we want to ask a large group of individuals.

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Pg. 268 - 285

Exploratory data analysis describes what data say by using graphs and numerical summaries. What if we want to ask a large group of individuals questions?

 To get valid answers, we need to produce our data carefully.  Often we use samples to represent a larger population. Section 5.1 deals with the many ways to choose samples.

 To get valid answers, we need to produce our data carefully.  Once you have chosen a sample, you have a few ways to gather data.

 An observational study observes individuals and variables of interest, but does not try to influence them.  An experiment deliberately imposes some treatment on individuals in order to observe their responses.  An observational study observes individuals and variables of interest, but does not try to influence them.  An experiment deliberately imposes some treatment on individuals in order to observe their responses.

 Explanatory variable – attempts to explain the observed outcome (p. 121)  Lurking variable – a variable not among the explanatory variables, but still may influence the interpretation of relationships among those variables. (p. 226)  Response variable - Measures an outcome of a study. (p. 121)

 Section 5.2 is about designing valid experiments that can be used to determine causation.  Section 5.3 is about using simulation to produce data.

 Population: The entire group of individuals that we want information about.  Sample: The part of the population we actually look at to gather information.  Population: The entire group of individuals that we want information about.  Sample: The part of the population we actually look at to gather information.

 Census: Attempts to contact every individual in the entire population.

 Voluntary Response Samples ▪ Ex: call-in polls, text your vote, etc.  People choose themselves by responding.  People with strong opinions (especially negative opinions) are more likely to respond. ▪ This leads to bias in your sample.

 Convenience Sampling ▪ Ex: Sitting outside a mall or grocery store  Choosing the individuals that are easiest to reach ▪ Another source of bias: convenience sampling is almost guaranteed not to represent the entire population.

 Bias: The design of a study is biased if it systematically favors certain outcomes.

THE SOLUTION! Let chance choose the sample. This is the essential principle of statistical sampling. THE SOLUTION! Let chance choose the sample. This is the essential principle of statistical sampling.

 Simplest way: Put the whole population in a hat and draw out a handful of individuals for your sample.

 Practice problems:  #1-4 (p. 273)

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