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# Purdue - STAT 113 - STAT 113, WEEK 1 NOTES - Class Notes

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Purdue - STAT 113 - STAT 113, WEEK 1 NOTES - Class Notes

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NOTES: Statistics and Society 113  Vocab and Samples: Chapters 1-4 Vocabulary: Individuals: objects described by a set of data Variable: any characteristic of an individual, different values Proportion:     # of successes        =    X                           total sample size           n Population: the entire groups of individuals about which we want information Consensus: attempts to get information from every member of the population (time
consuming and expensive).
Sample: a part of the population that we examine in order to gather information
Parameter: number that is true for the whole population Statistic: number that is true for the sample Bias: consistent, repeated deviation of the sample statistic from the population
parameter in the same direction when we take many samples (choosing a random
sample will reduce bias).
Variability: how spread out the sampling distribution is for the statistic. Determined
by sampling design and sample size “n” (larger samples have smaller variability).
Types of Samples: Convenience Sample: NOT A RANDOM SAMPLE, NOT THE BEST SAMPLE Selection of which individuals are easiest to reach, ex: Mall Surveys (Anyone
is given a survey)
Voluntary Response Sample: NOT A RANDOM SAMPLE, NOT THE BEST SAMPLE Consists of people who choose themselves by responding to a general appeal Biased because people with strong opinions are most likely to respond, often
negative responses, ex: Restaurant Surveys (How was your service? People
with bad service are more likely to fill out the survey)
Random Sample: RANDOM Eliminates bias Gives individuals equal chance to be chosen Two types
o Simple Random (SRS)
o Stratified Random
_____________________________________________________________________________________ Simple Random (SRS): The one we’ll be using in STAT 113 Have a list of the whole population, then use a random method to select our
sample, each individual has equal chance of being chosen
Stratified Random: Divide the individuals from the population into groups based on some
characteristic, then take simple random samples within each of these groups,
combine all of those samples into one big sample
Problems with Samples: Random sampling error: deviation between the sample statistic and the
population parameter caused by chance in selecting a random sample
o Each time you take a random sample from the population, you will get  a slightly different statistic, due to random variability **Taking a larger sample will help reduce random sampling error** Undercoverage: occurs when some groups in the population are left out of
the process of choosing the sample
Response error/bias: occurs when a subject gives an incorrect response
(lying, remembering incorrectly, doesn’t understand the question, etc.)
Nonresponse: the failure to obtain data from an individual selected for a
sample. Usually happens because some subjects can’t be contacted or
because those who are contacted refuse to cooperate
How to deal with nonsampling errors: Substitute other households for the nonresponders, hopefully from the same
neighborhood
Once the responses are in, statistical methods can be used to weight the
responses to correct the bias
How to determine: How good is a survey? Who carried out the survey? What was the population?

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