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STATS 1350 Lecture notes from week of 1/13/16

by: Noelle Millan

66

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STATS 1350 Lecture notes from week of 1/13/16 1350

Marketplace > Ohio State University > Statistics > 1350 > STATS 1350 Lecture notes from week of 1 13 16
Noelle Millan
OSU

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1/13/16 - 1/15/16
COURSE
STATS 1350
PROF.
Erin Louise Twohy
TYPE
Class Notes
PAGES
3
WORDS
KARMA
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This 3 page Class Notes was uploaded by Noelle Millan on Sunday January 17, 2016. The Class Notes belongs to 1350 at Ohio State University taught by Erin Louise Twohy in Spring 2016. Since its upload, it has received 66 views. For similar materials see STATS 1350 in Statistics at Ohio State University.

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Date Created: 01/17/16
1/13/16 – 1/15/16 CHAPTER 1: WHERE DOES DATA COME FROM? Individuals­ things, “subjects”, “participants”       ­objects described by data       ­building blocks of data       ­doesn’t have to be living Variables­ any characteristic of the individual (have different values) 1. Categorical variable­ puts individuals into specific groups 2. Quantitative variable­ takes numeric values for arithmetic operations (add & average) Ex: age, weight, & height, test scores… WAYS TO COLLECT DATA 1. Observational study­ observes individuals ­measures variables ­DOES NOT GET INVOLVED WITH PARTICIPANTS Ex: survey 2. Experiment­ deliberately imposes treatment on individuals to observe responses *Purpose: to see whether the treatment causes the response Population­ entire group of individuals that we want information from Census­ attempts to get information from EVERY member of the population **Very difficult to do because there are too many people  CHAPTER 2: SAMPLES, GOOD AND BAD Example: OSU football team wants to change the color and style of the uniforms, but they want      feedback from students.  What advice would you give on how to get a sample of the      OSU students? ­email ­survey ­representatives talk at classes ­stopping you on the Oval ­possible census? Types of Samples 1. Convenience­ selecting the easiest individuals to reach Ex: meet­and­greet, passers­by, interviews 1/13/16 – 1/15/16 Pros: easy, fast Cons: very limited sample, doesn’t fully represent population (Types of Samples, cont’d) 2. Voluntary response­ people who choose to respond to general appeal Ex: scientific studies (some), voting, call­in surveys, web polls Pros: easier, more ethical (in terms of scientific studies) Cons: possibly biased because it doesn’t fully represent the entire           Population 3. Random sample­ subjects are selected by random method     Ex: names in a hat, lotteries, gumball machines     Pros: more representative (usually), eliminates bias     Cons: could create unwanted variability 4. Simple random sample (SRS) – start with a list of the whole population, then use a random         method to select “n” of those individuals    Ex: random number table, software generation *each individual has an EQUAL chance of getting   Picked *** “n” = number of individuals in a sample 5. Stratified random sample­ 1. Divide individuals of the population into groups based on     similar characteristics            2. Take SRS within each group            3. Combine all SRS samples into one group Why is #5 a good idea? ­Because ALL groups are represented What Type of Sample? ­ “Quick vote from cnn.com…” ~voluntary response ­ Picking the oranges at the top of the crate… ~convenience sample What Do YOU Think?  1. Does sample size matter?  That depends on the diversity within the population 2. Does the size of the population matter?  That depends on the consistency of the diversity          in that population EXAMPLE: TEACHER SURVEY Headline: “Lack of teacher involvement linked to 2.3 million missed work days” Results: ­based on telephone interviews from 2013­2014   ­sampled from adults around the nation   ­then from those adults, they interviewed ONLY the teachers   ­randomly dialed telephone numbers so no bias was applied *Population: all U.S. K­12 school teachers 1/13/16 – 1/15/16 *Sample: self­identified school teachers *Sample Size: 6,711 self­identified school teachers *Sample Type: stratified random sample

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