Assignment 1
Sumaiya Shefa
library(readr)Q.8
college <- read.csv("College.csv")
rownames (college )=college [,1]
fix(college)college =college [,-1]
fix(college)summary(college)## Private Apps Accept Enroll
## Length:777 Min. : 81 Min. : 72 Min. : 35
## Class :character 1st Qu.: 776 1st Qu.: 604 1st Qu.: 242
## Mode :character Median : 1558 Median : 1110 Median : 434
## Mean : 3002 Mean : 2019 Mean : 780
## 3rd Qu.: 3624 3rd Qu.: 2424 3rd Qu.: 902
## Max. :48094 Max. :26330 Max. :6392
## Top10perc Top25perc F.Undergrad P.Undergrad
## Min. : 1.00 Min. : 9.0 Min. : 139 Min. : 1.0
## 1st Qu.:15.00 1st Qu.: 41.0 1st Qu.: 992 1st Qu.: 95.0
## Median :23.00 Median : 54.0 Median : 1707 Median : 353.0
## Mean :27.56 Mean : 55.8 Mean : 3700 Mean : 855.3
## 3rd Qu.:35.00 3rd Qu.: 69.0 3rd Qu.: 4005 3rd Qu.: 967.0
## Max. :96.00 Max. :100.0 Max. :31643 Max. :21836.0
## Outstate Room.Board Books Personal
## Min. : 2340 Min. :1780 Min. : 96.0 Min. : 250
## 1st Qu.: 7320 1st Qu.:3597 1st Qu.: 470.0 1st Qu.: 850
## Median : 9990 Median :4200 Median : 500.0 Median :1200
## Mean :10441 Mean :4358 Mean : 549.4 Mean :1341
## 3rd Qu.:12925 3rd Qu.:5050 3rd Qu.: 600.0 3rd Qu.:1700
## Max. :21700 Max. :8124 Max. :2340.0 Max. :6800
## PhD Terminal S.F.Ratio perc.alumni
## Min. : 8.00 Min. : 24.0 Min. : 2.50 Min. : 0.00
## 1st Qu.: 62.00 1st Qu.: 71.0 1st Qu.:11.50 1st Qu.:13.00
## Median : 75.00 Median : 82.0 Median :13.60 Median :21.00
## Mean : 72.66 Mean : 79.7 Mean :14.09 Mean :22.74
## 3rd Qu.: 85.00 3rd Qu.: 92.0 3rd Qu.:16.50 3rd Qu.:31.00
## Max. :103.00 Max. :100.0 Max. :39.80 Max. :64.00
## Expend Grad.Rate
## Min. : 3186 Min. : 10.00
## 1st Qu.: 6751 1st Qu.: 53.00
## Median : 8377 Median : 65.00
## Mean : 9660 Mean : 65.46
## 3rd Qu.:10830 3rd Qu.: 78.00
## Max. :56233 Max. :118.00college$Private = as.factor(college$Private)
pairs(college[,1:10])
attach(college)
Private = as.factor(Private)
boxplot(Outstate ~ Private, col="skyblue")
Elite=rep("No",nrow(college))
Elite[college$Top10perc >50]=" Yes"
Elite=as.factor(Elite)
college=data.frame(college , Elite)
summary(Elite)## Yes No
## 78 699boxplot(Outstate ~ Elite, col="lavenderblush3")
After exploring this data I noticed that not everyone that gets accepted enrolls in the college. Out of 777 people only 78 are elites.So chances of getting into the college even if they are not an elite is high.
Q.9
Auto <- read.csv("Auto.csv", header=T,na.strings="?")
Auto = na.omit(Auto)The quantitative predictors in this data are mpg, cylinders, displacement, horsepower, weight, acceleration, year, and origin.The qualitative predictor is name.
attach(Auto)
Auto2 = Auto[,c(1:8)]The range of each quantitative predictor is:
sapply(Auto2, range)## mpg cylinders displacement horsepower weight acceleration year origin
## [1,] 9.0 3 68 46 1613 8.0 70 1
## [2,] 46.6 8 455 230 5140 24.8 82 3The mean of each quantitative predictor is:
sapply(Auto2, mean)## mpg cylinders displacement horsepower weight acceleration
## 23.445918 5.471939 194.411990 104.469388 2977.584184 15.541327
## year origin
## 75.979592 1.576531The standard deviation of each quantitative predictor is:
sapply(Auto2, sd)## mpg cylinders displacement horsepower weight acceleration
## 7.8050075 1.7057832 104.6440039 38.4911599 849.4025600 2.7588641
## year origin
## 3.6837365 0.8055182Auto3 = Auto2[-c(10:85),]The range of each quantitative predictor of the new data is:
sapply(Auto3, range)## mpg cylinders displacement horsepower weight acceleration year origin
## [1,] 11.0 3 68 46 1649 8.5 70 1
## [2,] 46.6 8 455 230 4997 24.8 82 3The mean of each quantitative predictor of the new data is:
sapply(Auto3, mean)## mpg cylinders displacement horsepower weight acceleration
## 24.404430 5.373418 187.240506 100.721519 2935.971519 15.726899
## year origin
## 77.145570 1.601266The standard deviation of each quantitative predictor of the new data is:
sapply(Auto3, sd)## mpg cylinders displacement horsepower weight acceleration
## 7.867283 1.654179 99.678367 35.708853 811.300208 2.693721
## year origin
## 3.106217 0.819910pairs(~., Auto2)
Q.10
library(MASS)
fix(Boston)The Boston data frame has 506 rows and 14 columns. This data frame contains the following columns:
crim =per capita crime rate by town. zn = proportion of residential land zoned for lots over 25,000 sq.ft. indus = proportion of non-retail business acres per town. chas = Charles River dummy variable (= 1 if tract bounds river; 0 otherwise). nox = nitrogen oxides concentration (parts per 10 million). rm = average number of rooms per dwelling. age = proportion of owner-occupied units built prior to 1940. dis = weighted mean of distances to five Boston employment centers. rad = index of accessibility to radial highways. tax = full-value property-tax rate per $10,000. ptratio = pupil-teacher ratio by town. black = 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town. lstat = lower status of the population (percent). medv = median value of owner-occupied homes in $1000s.
attach(Boston)
pairs(~., Boston)
The range of each quantitative predictor is:
sapply(Boston, range)## crim zn indus chas nox rm age dis rad tax ptratio black
## [1,] 0.00632 0 0.46 0 0.385 3.561 2.9 1.1296 1 187 12.6 0.32
## [2,] 88.97620 100 27.74 1 0.871 8.780 100.0 12.1265 24 711 22.0 396.90
## lstat medv
## [1,] 1.73 5
## [2,] 37.97 50The mean of each quantitative predictor is:
sapply(Boston, mean)## crim zn indus chas nox rm
## 3.61352356 11.36363636 11.13677866 0.06916996 0.55469506 6.28463439
## age dis rad tax ptratio black
## 68.57490119 3.79504269 9.54940711 408.23715415 18.45553360 356.67403162
## lstat medv
## 12.65306324 22.53280632The median of each quantitative predictor is:
sapply(Boston, median)## crim zn indus chas nox rm age dis
## 0.25651 0.00000 9.69000 0.00000 0.53800 6.20850 77.50000 3.20745
## rad tax ptratio black lstat medv
## 5.00000 330.00000 19.05000 391.44000 11.36000 21.20000sum(chas)## [1] 3535 suburbs in this data set bound the Charles river.
median(ptratio)## [1] 19.05The median pupil-teacher ratio among the towns in this data set is 19.05.
Minimum= sapply(Boston, min)
Median=sapply(Boston, median)
data.frame(Minimum, Median)## Minimum Median
## crim 0.00632 0.25651
## zn 0.00000 0.00000
## indus 0.46000 9.69000
## chas 0.00000 0.00000
## nox 0.38500 0.53800
## rm 3.56100 6.20850
## age 2.90000 77.50000
## dis 1.12960 3.20745
## rad 1.00000 5.00000
## tax 187.00000 330.00000
## ptratio 12.60000 19.05000
## black 0.32000 391.44000
## lstat 1.73000 11.36000
## medv 5.00000 21.20000sum(rm>7)## [1] 6464 suburbs average more than seven rooms per dwelling.
sum(rm>8)## [1] 1313 suburbs average more than seven rooms per dwelling.