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## Concepts to know before exam one

by: Abriana Prevetti

45

0

3

# Concepts to know before exam one KIN 270

Marketplace > University of Wisconsin - Milwaukee > Health Sciences > KIN 270 > Concepts to know before exam one
Abriana Prevetti
UWM

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Definitions Pictures of graphs
COURSE
stats in Health Proffesion
PROF.
Susan Cashin
TYPE
Class Notes
PAGES
3
WORDS
KARMA
Free

## Popular in Health Sciences

This 3 page Class Notes was uploaded by Abriana Prevetti on Saturday September 24, 2016. The Class Notes belongs to KIN 270 at University of Wisconsin - Milwaukee taught by Susan Cashin in Fall 2016. Since its upload, it has received 45 views. For similar materials see stats in Health Proffesion in Health Sciences at University of Wisconsin - Milwaukee.

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Date Created: 09/24/16
statistics organize, summarize, analyze, and draw  conclusions form data  types of statistics descriptive and inferential  descriptive statistic describes the group that was accessed with  organization and numbers  inferential statistic inferences about a larger group based on data  from a smaller group or sample  types of data qualitative quantitative  population all subjects being studied  sample group of subjects selected from a population  scales of measurement nominal ordinal interval ratio  random sampling everything has an equal chance of being  selected  systematic sampling every "kth" is selected (every 4th person)  stratified sampling subjects are divided then a sample is drawn from each group  cluster sampling takes a group all at once randomly  convenience sampling selections that are convenient  detached statistics no comparison is made (this aspirin works 4  times faster)  how to misinterpret data detached statistics implied connections misleading graphs  raw data data in original form  frequency # of times that value occurs  classes raw data into clusters  cumulative frequency raw scores that fall at or below the value of  interest  percent frequency divided by total number of  observations  cumulative percent cumulative frequency divided by the total number of observations  relative frequency how often something happens divided by all  outcomes Histogram frequency polygon ogive

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