Ludvig Londos och Kalle Palm (laddy_ludde@msn.com, kalle.palm@gmail.com)
Undre: 2400 Övre: 2900
20%
97.5
(3300 + 3700)/2
## [1] 3500
2400 - (1.5 * 500)
## [1] 1650
head(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
Alltså: Species
nrow(iris)
## [1] 150
Alltså: 150
length(iris)
## [1] 5
Alltså: 5
iris$Sepal.Length[3]
## [1] 4.7
Alltså: 4.7
head(cars)
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
Alltså: carb
nrow(cars)
## [1] 32
Alltså: 32
length(cars)
## [1] 11
Alltså: 11
cars$wt[2]
## [1] 2.875
Alltså: 2.875
qdata(0.95, height, data = galton)
## p quantile
## 0.95 72.50
Svar: b
qdata(c(0.05, 0.95), height, data = galton)
## quantile p
## 5% 61.0 0.05
## 95% 72.5 0.95
Svar: a
qdata(c(0.25, 0.75), father, data = galton)
## quantile p
## 25% 68 0.25
## 75% 71 0.75
qdata(c(0.25, 0.75), mother, data = galton)
## quantile p
## 25% 63.0 0.25
## 75% 65.5 0.75
Svar: b resp c
qdata(c(0.025, 0.975), father, data = galton)
## quantile p
## 2.5% 65 0.025
## 97.5% 74 0.975
qdata(c(0.025, 0.975), mother, data = galton)
## quantile p
## 2.5% 59 0.025
## 97.5% 69 0.975
Svar: b resp d
male = subset(galton, sex == "M")
diff = male$height - male$father
mean(diff)
## [1] 0.06065
Svar: 0.06
sd(diff)
## [1] 2.735
Svar: 2.73
qdata(c(0.025, 0.975), (height - father), data = male)
## quantile p
## 2.5% -5.18 0.025
## 97.5% 5.58 0.975
Svar: c
bwplot(~height, data = galton)
Svar: b
Svar: 1
bwplot(~mother, data = galton)
tally(~mother, data = galton)
##
## 58 58.5 59 60 60.2 60.5 61 61.5 62 62.5 62.7 63 63.5 63.7 64
## 7 9 26 36 1 1 25 1 73 22 7 103 42 8 112
## 64.2 64.5 64.7 65 65.5 66 66.2 66.5 66.7 67 68 68.5 69 70.5
## 5 26 7 133 36 69 5 47 6 45 11 10 23 2
qdata(0.75, mother, data = galton) + 1.5 * IQR(~mother, data = galton)
## p quantile
## 4.50 69.25
7 + 9 + 26 + 2
## [1] 44
Svar: 44
bwplot(~father, data = galton)
tally(~father, data = galton)
##
## 62 62.5 64 65 65.5 66 66.5 67 67.5 68 68.2 68.5 68.7 69 69.2
## 3 2 17 37 9 57 21 40 16 102 5 35 4 115 4
## 69.5 69.7 70 70.3 70.5 71 71.2 71.5 71.7 72 72.5 72.7 73 73.2 74
## 26 1 131 7 34 89 2 11 5 42 9 8 23 1 20
## 74.5 75 75.5 78.5
## 1 13 4 4
qdata(0.25, father, data = galton) - 1.5 * IQR(~father, data = galton)
## p quantile
## -4.25 63.50
qdata(0.75, father, data = galton) + 1.5 * IQR(~father, data = galton)
## p quantile
## 5.25 75.50
Svar: 9
tally(~mother + sex, data = galton)
## sex
## mother F M
## 58 3 4
## 58.5 4 5
## 59 12 14
## 60 18 18
## 60.2 0 1
## 60.5 0 1
## 61 10 15
## 61.5 0 1
## 62 30 43
## 62.5 13 9
## 62.7 3 4
## 63 45 58
## 63.5 19 23
## 63.7 5 3
## 64 59 53
## 64.2 3 2
## 64.5 13 13
## 64.7 4 3
## 65 69 64
## 65.5 17 19
## 66 36 33
## 66.2 2 3
## 66.5 22 25
## 66.7 0 6
## 67 22 23
## 68 5 6
## 68.5 7 3
## 69 11 12
## 70.5 1 1
Svar: enl. kod ovan plus tidigare får vi 20 st.
Svar: d
FALSE
Det ser ut att vara lika tätt mellan fransarna i matt-plotten, men mer av en exponentiell ökning i densitetsplotten.
Svar: b
TRUE
Där fransarna är tätast, verkar ökningen vara störst.
Mean: 0 Range: 2 Variance: 1 Std-dev: 1
Mean: 2 Range: 2 Variance: 2 Std-dev: sqrt(2)
Mean: 2 Range: 2 Variance: 1 Std-dev: 1
e
b
c
d
Ja, ungefär
d
a
b
c
Mean: 20 Std dev: 5 95%: 10 - 30 Var: 25
Mean: 180 Std dev: 30 95%: 135 - 230 Var: 900
6.8
4.4 to 8.7
16
3.1 kg
2.0 to 3.9
8
0.5
0.3 to 0.6
1.1
box.plot var enklast