Week 1 and 2 Notes
Week 1 and 2 Notes STAT 200
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This 3 page Bundle was uploaded by Kelsey Marr on Thursday February 5, 2015. The Bundle belongs to STAT 200 at Pennsylvania State University taught by Andrew Wiesner in Winter2015. Since its upload, it has received 165 views. For similar materials see Elementary Statistics in Statistics at Pennsylvania State University.
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Date Created: 02/05/15
Stat 200 Wiesner Week 1amp2 Notes Basics of Statistics 0 Variable Types 0 Categorical if you d answer with a quotwordquot Binary two levels to the category 0 Ex yesno sexgender Ordinal some hierarchy or order to the levels 0 EX class standing Nominal no order or hierarchy Ex republicandemocratindependent race etc 0 Quantitative answer is a number Discrete something counted 0 Ex the number of females in class Continuous something measured 0 Ex heights of students age of students Graphing Variables o Categorical graph choices Pie chart Bar graph 0 Quantitative graph choices Histogram Stem plot Dot plot Box plot 0 Numerical Summaries 0 Measures of center Mode most frequent observation in a set of data Median middle point where about 50 of observed values in a set of data fall at or below Mean math average of all observations 0 for class discussions average will mean 0 Spread of Data 0 Range maximum minus minimum 0 Standard deviation amp variance variance SDquot2 or SD square root of variance 0 lnterquartile Range IQR third quartile minus rst quartile Data Shape o Symmetrical or bell shape O o httpcondordepauedusjostit223documentscentrahtm d 0 Right Skewed or Positively Skewed O httptheagiepiratenetaEhives403 0 Left Skewed or Negatively Skewed r39 fxffl x EXff Errf I39L FTG fine Ff O 0 mean median made 0 httpdriverlayercomimgskewed20eft20any Variance o verbal formula Take the difference between each observation and the mean These are called quotdeviationsquot We square the deviations sum them then divide by n1 0 Math expression r j Variance 5 0 Sigma summation X with line on top sample mean Xi all values of x we observe X the data Outliers 0 We can use quartiles to identify extreme observations that can be considered Outliers o A common method is to build a fence around your data Lower fence 01 15 x IQR Upper fence QB 15 x IQR Any points outside the fence are considered outliers Empirical Rule 0 A special application to data that is be shaped or approximately be shaped 0 Also known as 68 95997 Rule 0 What it means For data that is bell shaped about 68 of observations fall within one standard deviation of mean 95 of observations fall within 2 standard deviations of the mean 997 will fall within 3 standard deviations Ex heights of US adult males are approximately bell shaped with a mean of 69 inches and standard deviation of 35 inches 0 Approximately 68 of all male adults fall within heights of 69 35 in 655725 0 Approximately 95 fall within 69 7in 6276 0 Approximately 997 fall within 69 105in 585795 Zscore 0 Best used for bell shaped data 0 Gives the number of standard deviations an observation is form the mean 0 Found by Zscore observed value mean Standard deviation
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