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## Statistics 201 Notes For exam 1

by: Jessica Namesnik

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8

# Statistics 201 Notes For exam 1 STAT-201

Marketplace > Colorado State University > Statistics > STAT-201 > Statistics 201 Notes For exam 1
Jessica Namesnik
CSU

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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
COURSE
General Statistics
PROF.
Kirk Ketelsen
TYPE
Study Guide
PAGES
8
WORDS
CONCEPTS
Statistics
KARMA
50 ?

## Popular in Statistics

This 8 page Study Guide was uploaded by Jessica Namesnik on Monday September 19, 2016. The Study Guide belongs to STAT-201 at Colorado State University taught by Kirk Ketelsen in Fall 2016. Since its upload, it has received 25 views. For similar materials see General Statistics in Statistics at Colorado State University.

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Date Created: 09/19/16
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  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 Statistics 201 9/06/16  Dispersion­ spread ­Info about location (avg or median)   Range­ difference between max and min ­Range=max­min  Positive/right skew ­ mean is pulled to the right, and is larger than the median  Negative/left skew ­mean is pulled to the left, and is smaller than the median  Symmetric/bell shaped ­ mean is approximately the same as median  IQR= Q ­Q3 1  ­ is not effected by extreme values b/c os ca;culated using values that lie  close to the center of the data set. IQR is not used in inferential statistics  but are useful as descriptive statistics  Variance­ another measure of dispersion  ­Closely related to standard deviation.  ­Computed using all the data values in the dataset ­Sensitive to outliers, but not as effected if there are a large number of values  (observations) in the data set.   Sum of Standard deviations (“sum of squares” or “ss”) to calc ss for a single  observation subtract mean from observation and square the result. Do this for all  of the observations and sum the result. ss= Σ(x­  xx)2 i  Sample variance (s )= average squared distance that a group of “n” points lies  from the mean of the group(n is # of observations)  s =   Σ(xi­ xx)^2         (n­1)  Sample standard deviation (s)­ square root of the sample variance. It’s the average distance a group of points lies from the mean.  ­ if large, the data is highly dispersed, high level of uncertainty. This is  mainly used for statistical inferences. ­ What counts as “large” or “ small” depends on the magnitude of the data  itself.  s     =  note( from last week’s homework) ­ variables:  quantitative­ numbers ­­­ continuous­ ie weight, any # value ­­­ discrete­ ie # of visits to the doctor usually integers, specific # values, no  decimals.  Qualitative­non­numbers ­­­nominal­ no inherent order. Ie eye color. ­­­ ordinal­ inherent order. Ie rank based on preference. ON Thursday no real lecture. Mainly talked about football. Did one practice  problem 9/13/16 Statistics 201: Probability and normal distribution  Random event­ something that may or may not occur, and that we can assign a probability to.  Examples: ­ a coin might fall on heads ­ tomorrow might snow ­ the broncos might win the super bowl ­ Gryffindor might win the house cup  Random variable­ “x” , i.e. the roll of a die  Complement­ is the nonoccurrence or opposite of an event.  Examples: ­ coin might fall on tails ­ tomorrow might not snow ­ broncos could lose the superbowl ­ Gryffindor might lose the house cup  Probability – quantifying the likelihood of a random event occurring, usually percentages(i.e.   50%),   formally   proportions   (i.e.0.5).   Must   be   between   0 (impossible) and 1 (certain) ( both are rare)  Relative frequency­ how often an event occurs as a proportion of how often it could potentially occur ­ states that probability of an outcome is the proportion of times the outcome would occur over the long run ( if we were to keep repeating a random process indefinitely).  ­ Doesn’t need to have a sample size or a denominator  Probability notation­ p(x) is probability that event x occurs. 1­p(x) is the probability event x doesn’t occur ( the complement)  Standardization­ finding distance from mean in terms of standard deviations (given a common unit)   Z­score­ value that has been standardized in this manner. ­ shows if data point lies above or below the mean.  ­ (+) z­score= above ­ (­) z­score= below ­ Magnitude­ shows how far the data point is from the mean in terms of standard deviations. We say that they are unit­less, but they are expressed in terms of distance from the mean. Z=(x­xx)/s ­ is the value to be standardized. xx is the population mean. S is the sample standard deviation. xx and s are sample statistics, denoted w/English letters 9/15/16  Chebyshev’s  rule ­ at least 2 (1­(1/k ) x 100%     of values in a standard distribution bust lie w/in k of standard deviations of the mean. Applies to any distribution.  Bell curve/Gaussian distribution­ most common, any variable that follows a normal distribution is said to be normally distributed.  Empirical rule­ what % of values of a normal distribution of variables fall within 1,2,3 standard deviations of the mean.  Basically the same as Chebyshev’s  rule, but only applies to normal distributions.

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