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## Statistics 201 notes week 1

by: Jessica Namesnik

28

0

5

# Statistics 201 notes week 1 STAT-201

Marketplace > Colorado State University > Statistics > STAT-201 > Statistics 201 notes week 1
Jessica Namesnik
CSU

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These notes cover the material from the lectures of week 1
COURSE
General Statistics
PROF.
Kirk Ketelsen
TYPE
Class Notes
PAGES
5
WORDS
CONCEPTS
Statistics
KARMA
Free

## Popular in Statistics

This 5 page Class Notes was uploaded by Jessica Namesnik on Thursday September 1, 2016. The Class Notes belongs to STAT-201 at Colorado State University taught by Kirk Ketelsen in Fall 2016. Since its upload, it has received 28 views. For similar materials see General Statistics in Statistics at Colorado State University.

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Date Created: 09/01/16
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  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 ­­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  Q  media1, 3, ­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% ­­­Compute rank­ (n+1)/2   tells you which ordered observation will be the median. If  the rank is an integer value (3, 5, ect) go right to it in the ordered data set otherwise  compute the average of the 2 surrounding observations. i.e. If the rank=5 go to the 5   th ordered observation for the median. ­­Lower quartile (Q )­1below the median, separates the lower 25% from the upper  75% of the data ­­Upper quartile (Q )­3 above the median, separates the lower 75% from the top 25%  of data ­­­To calculate: put parenthesis on either side of the median to separate the lower and  upper halves of the data set. i.e  1,2,3,4,5,6,7,8,9   n=9  rank= (9+1)/2 =5 so median is 5 . So 1,2,3,4) 5 (6,7,8,9 . ­­  Q =the median of the lower half of the data (1,2,3,4) 1 ­­Q 3the median of the upper half of the data (6,7,8,9)  ­Extremes­ minimum and maximum ­­Box plot /box &whisker plot     Min         1    median       Q3                                     Whiskers go to min, max, or furthest outliers, 50% of data in box, 25% below, 25%  above

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