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CSU - STAT 201 - Study Guide - Midterm

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CSU - STAT 201 - Study Guide - Midterm

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background image Statistics Note Bundle Exam 1 Statistics 201 8/25/16 Statistics­ how data is collected, analyzed& interpreted  ­­descriptive statistics­ describes a dataset, the data itself don’t generalize the facts about  dataset to a larger group. ­­ inferential­ generalize (generalizations are called inferences), contain uncertainty.  Data­ information, takes the form of observed measurements or descriptions ­­Stats­ how we apply data to the real world ­­Variables­ items of interest, can take on different values, the type of measurement being taken Population­ entire overall group we are interested in  ­­Population of interest/target population­ i.e if we want average height of US girls, the  population of interest is US girls. Can be large or small. ­­Parameter­ # pertaining to a population (ie height of U.S. girls ­­Statistic – any # calculated using data to estimate parameters Sample – subset of entire population we collect data on, the variable of interest is  measured on them ­­Observation­ single member of a sample ­­Census­ measurements obtained from every member of a sample 
background image Conerns­> is the sample large enough, is the sample representative of the population of  interest?   Statistics 201 8/30/16 Bias­ if the statistic is made in a way that shows it might differ from the  population parameter it was meant to estimate.  ­­Sampling bias­ when the sample isn’t representative of the population of interest ­­Self selection bias­ when individuals select themselves. i.e: when voting for the most  talented musician and the musician votes for themselves. ­­Nonresponsive bias­  when certain types of respondants are more or less likely to  answer a survey honestly. i.e: high school kids raising their hands for a survey on  virginity. Simple random sample (SRS) – sample of a population where each unit of the  population has an equal opportunity to be selected. ­why? ­­ because it can help overcome self­selection bias and sampling bias Observational study­ variable values observed & recorded from already existing  data Controlled experiment­ researcher assigns members of study to different groups  which get different experimental conditions. ­­Treatment group­ undergoes the procedure ­­Control group­ does not undergo the procedure
background image ­­Placebo effect­ if a person believes a treatment will be beneficial, there is a chance  they might have the beneficial effect regardless of being treated or not.  Correlation­ doesn’t imply causation Confounding variables­ help explain data but is not accounted for in the study Blinding – an attempt to eliminate bias by not telling the treatment and control  group which is getting the treatment ­­Double­blinding­ neither the research groups or the researcher know which group is  the control and which is the treatment group.  9/01/16 Location­ where is the data set “ located” in a # line? Where is its center? Spread­ how dispersed is the data 5 number summary­ minimum and maximum, Q 1,  Q 3,  median ­Outliers­ any unusual values in the data set ­Shape­ what is the shape of the distribution of values in a dataset? ­Center­ mean and median ­­mean­ average, sum of data divided by sample size, denoted by an x with a line  above it ­­­ sample size # of obsrvations in a sample “n” mean = sum of data/ sample size ­­Median – if you put data #’s in order smallest to largest, the # in the middle is the  median, separates the upper 50% from the lower 50%

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School: Colorado State University
Department: Statistics
Course: General Statistics
Professor: Xiaowen Hu
Term: Spring 2016
Tags: Statistics
Name: Statistics 201 Notes For exam 1
Description: This bundle contains all the notes from class leading up to 9/19/16. An easy way to access notes for preparing for the exam
Uploaded: 09/19/2016
8 Pages 25 Views 20 Unlocks
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