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## Statistic and Society, January 12 Notes

1 review
by: Hallie Tepperman

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# Statistic and Society, January 12 Notes STAT 113

Marketplace > Purdue University > Math > STAT 113 > Statistic and Society January 12 Notes
Hallie Tepperman
Purdue
STAT 113
Ellen Gundlach

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These notes cover the information from Unit 1 (Sampling) Part 1 (Population vs. Sample)
COURSE
STAT 113
PROF.
Ellen Gundlach
TYPE
Class Notes
PAGES
2
WORDS
CONCEPTS
Math, Statistics, Sampling, population, sample
KARMA
25 ?

## 2

1 review
"If you want to pass this class, use these notes. Period. I for sure will!"
Frances Cronin

## Popular in Math

This 2 page Class Notes was uploaded by Hallie Tepperman on Monday January 25, 2016. The Class Notes belongs to STAT 113 at Purdue University taught by Ellen Gundlach in Fall2014. Since its upload, it has received 18 views. For similar materials see STAT 113 in Math at Purdue University.

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## Reviews for Statistic and Society, January 12 Notes

If you want to pass this class, use these notes. Period. I for sure will!

-Frances Cronin

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Date Created: 01/25/16
Hallie Tepperman Statistic and Society January 12 2016 Unit 1 Sampling Part 1 Population vs Sample 0 Individuals the objects described by a set of data I Variable any characteristic of an individual can take different values for different individuals ex Height weight hometown major 0 Proportion O of successes total sample size I Example 11 out of 50 children say chocolate is their favorite avor of ice cream I Proportion 1150 022 or 22 0 Population the entire group of individuals about which we want information I Census attempts to get information from every member of the population time consuming expensive hard to do well but very rare 0 Examples inventory short version of the US census I Sample a part of the population that we actually examine in order to gather information about the whole population 0 Cheaper quicker easier than taking a census 0 Valid information if done well especially if a random sample from the population I Types of samples 0 Convenience sample NOT RANDOM NOT THE BEST 0 Selection of whichever individuals are easiest to reach shopping mall surveys 0 Voluntary response sample NOT RANDOM NOT THE BEST sometimes the other ethical choice for experiments using people 0 Consists of people who choose themselves by responding to a general appeal callin radio web poll 0 Biased because people with strong opinions especially negative opinions are most likely to respond 0 Random Sample MUCH BETTER 0 Eliminates bias by allowing impersonal chance to do the choosing O Gives all individuals an equal chance to be chosen 0 Types of Random Samples I Simple random sample size SRS start with a list of the whole population the use a random method such as a random number table or software to select 11 of those individuals with each individual haVing an equal chance of being chosen Example pull names out of a hat I We could randomly select 120 people from a master list of all students taking STAT 113 this semester I Strati ed random sample divide the individuals from the population into groups based on some characteristic gender year in school major or hometown for example then take simple random samples Within each of those groups combine all those samples into one big sample I We could sort the students by their lecture section and take a SRS of 10 students from each of the 20 recitation sections of STAT 113 to get a total sample size of 200 0 Population vs Sample Parameter vs Statistics 0 Population is EVERYBODY that you re interested in I Parameter the number that is true for the population 0 Sample is the group that you actually collect information from I Statistic the number that you measure that s true for the sample 0 Possible problems With sample statistics 0 Bias consistent repeated deviation of the sample statistic from the population parameter in the same direction When we take many samples I Choosing a random sample Will reduce bias 0 Variability how spread out the sampling distribution is for the statistic I Determined by sampling design and size 0 Larger samples have smaller variability 0 Population size is not important to variability 0 We want both small bias and variability High Bias ng Bias ng Variability High Variability High Bias LW Bi S High Variability Law iiariaility

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