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# Shousing housinghousingcity in somecities housingprice

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Unformatted text preview: nts. •  In a way, the density is a smooth abstrac9on of the distribu9on. Boxplot: boxplot(infants\$bwt) boxplot(infants\$bwt, ! xlab="Birth Weight (oz)")! Looking for Structure: Quan9ta9ve Distribu9on Distribu5on: paUern of values for a variable Mode: high density region Long Tail: many observa9ons far from center Symmetry/Skewness: distribu9on of values the lei and right of the center. •  Gaps: places where there are no observa9ons. •  Outliers: unusually large or small values that falls well beyond the overall paUern of data •  •  •  •  What Structure Do You See? What Structure Do You See? Parity: Number of siblings • This quan9ta9ve variable is diﬀerent from birth weight – there are only a few possible values, i.e. it’s not possible to have 2.3 siblings, and it’s highly unlikely to have 17 > table(infants\$parity)! 0 1 2 3 4 5 6 7 8 9 10 11 13 315 310 238 168 83 52 32 16 8 7 4 2 1 0 50 150 250 Number of Siblings 0 1 2 3 4 5 6 7 8 9 Number of siblings barplot(table(infants\$parity)) 11 0.20 0.10 0.00 Proportion Alterna9ve – bar width has no meaning 0123456789 11 13 Number of siblings plot(table(infants\$parity), ! type ="h", lwd = 4, ! ylab ="Proportion", col="darkgrey")! Case: College Students load(url("hUp://www.stanford.edu/~vcs/ StatData/videogame.rda")) > objects()! [1] "infants" "video” > names(video)! [1] "9me" "like" "where" "freq" "busy" "educ" [7] "sex" "age" "home" "math" "work" "own" [13] "cdrom" "email" "grade” > dim(video) [1] 91 15 STAT 2 Survey •  Random Sample of 91 of 314 Berkeley students enrolled in Stat 2 •  Survey collected the following info: –  sex – Male/Female –  grade – grade expected in the course (“A”, “B”, “C”, “D”, “F”) •  What type of data are these? –  sex is qualita9ve (nominal) –  grade is qualita9ve with an ordering Make tables of qualita9ve data > table(video\$grade)! Anything unusual about the expected grade? F D C B A 0 0 8 52 31 > table(video\$grade, video\$sex) Female Male! F D C B A 0 0 8 21 9 0! 0! 0! 31! 22! Does expected grade depend on gender? Expected Grade Pie chart pie(table(video\$grade)) 40 50 Bar chart 30 B 10 20 C 0 A F D C B WIDTH of bars have no meaning A AREAS can be hard to compare Expected Grade Dot chart dotchart(table(video\$grade), pch = 19)! Focus on comparison of the values A B C D F ● ● ● ● ● 0 10 20 30 40 50 Method of Comparison •  Oie...
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## This document was uploaded on 02/16/2014 for the course STATISTICS 3026 at Columbia.

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