library(datasets)
library(psych)
library(kableExtra)
require(graphics)
## Swiss Data
swiss
## Fertility Agriculture Examination Education Catholic
## Courtelary 80.2 17.0 15 12 9.96
## Delemont 83.1 45.1 6 9 84.84
## Franches-Mnt 92.5 39.7 5 5 93.40
## Moutier 85.8 36.5 12 7 33.77
## Neuveville 76.9 43.5 17 15 5.16
## Porrentruy 76.1 35.3 9 7 90.57
## Broye 83.8 70.2 16 7 92.85
## Glane 92.4 67.8 14 8 97.16
## Gruyere 82.4 53.3 12 7 97.67
## Sarine 82.9 45.2 16 13 91.38
## Veveyse 87.1 64.5 14 6 98.61
## Aigle 64.1 62.0 21 12 8.52
## Aubonne 66.9 67.5 14 7 2.27
## Avenches 68.9 60.7 19 12 4.43
## Cossonay 61.7 69.3 22 5 2.82
## Echallens 68.3 72.6 18 2 24.20
## Grandson 71.7 34.0 17 8 3.30
## Lausanne 55.7 19.4 26 28 12.11
## La Vallee 54.3 15.2 31 20 2.15
## Lavaux 65.1 73.0 19 9 2.84
## Morges 65.5 59.8 22 10 5.23
## Moudon 65.0 55.1 14 3 4.52
## Nyone 56.6 50.9 22 12 15.14
## Orbe 57.4 54.1 20 6 4.20
## Oron 72.5 71.2 12 1 2.40
## Payerne 74.2 58.1 14 8 5.23
## Paysd'enhaut 72.0 63.5 6 3 2.56
## Rolle 60.5 60.8 16 10 7.72
## Vevey 58.3 26.8 25 19 18.46
## Yverdon 65.4 49.5 15 8 6.10
## Conthey 75.5 85.9 3 2 99.71
## Entremont 69.3 84.9 7 6 99.68
## Herens 77.3 89.7 5 2 100.00
## Martigwy 70.5 78.2 12 6 98.96
## Monthey 79.4 64.9 7 3 98.22
## St Maurice 65.0 75.9 9 9 99.06
## Sierre 92.2 84.6 3 3 99.46
## Sion 79.3 63.1 13 13 96.83
## Boudry 70.4 38.4 26 12 5.62
## La Chauxdfnd 65.7 7.7 29 11 13.79
## Le Locle 72.7 16.7 22 13 11.22
## Neuchatel 64.4 17.6 35 32 16.92
## Val de Ruz 77.6 37.6 15 7 4.97
## ValdeTravers 67.6 18.7 25 7 8.65
## V. De Geneve 35.0 1.2 37 53 42.34
## Rive Droite 44.7 46.6 16 29 50.43
## Rive Gauche 42.8 27.7 22 29 58.33
## Infant.Mortality
## Courtelary 22.2
## Delemont 22.2
## Franches-Mnt 20.2
## Moutier 20.3
## Neuveville 20.6
## Porrentruy 26.6
## Broye 23.6
## Glane 24.9
## Gruyere 21.0
## Sarine 24.4
## Veveyse 24.5
## Aigle 16.5
## Aubonne 19.1
## Avenches 22.7
## Cossonay 18.7
## Echallens 21.2
## Grandson 20.0
## Lausanne 20.2
## La Vallee 10.8
## Lavaux 20.0
## Morges 18.0
## Moudon 22.4
## Nyone 16.7
## Orbe 15.3
## Oron 21.0
## Payerne 23.8
## Paysd'enhaut 18.0
## Rolle 16.3
## Vevey 20.9
## Yverdon 22.5
## Conthey 15.1
## Entremont 19.8
## Herens 18.3
## Martigwy 19.4
## Monthey 20.2
## St Maurice 17.8
## Sierre 16.3
## Sion 18.1
## Boudry 20.3
## La Chauxdfnd 20.5
## Le Locle 18.9
## Neuchatel 23.0
## Val de Ruz 20.0
## ValdeTravers 19.5
## V. De Geneve 18.0
## Rive Droite 18.2
## Rive Gauche 19.3
str(swiss)
## 'data.frame': 47 obs. of 6 variables:
## $ Fertility : num 80.2 83.1 92.5 85.8 76.9 76.1 83.8 92.4 82.4 82.9 ...
## $ Agriculture : num 17 45.1 39.7 36.5 43.5 35.3 70.2 67.8 53.3 45.2 ...
## $ Examination : int 15 6 5 12 17 9 16 14 12 16 ...
## $ Education : int 12 9 5 7 15 7 7 8 7 13 ...
## $ Catholic : num 9.96 84.84 93.4 33.77 5.16 ...
## $ Infant.Mortality: num 22.2 22.2 20.2 20.3 20.6 26.6 23.6 24.9 21 24.4 ...
describe(swiss)
## vars n mean sd median trimmed mad min max range
## Fertility 1 47 70.14 12.49 70.40 70.66 10.23 35.00 92.5 57.50
## Agriculture 2 47 50.66 22.71 54.10 51.16 23.87 1.20 89.7 88.50
## Examination 3 47 16.49 7.98 16.00 16.08 7.41 3.00 37.0 34.00
## Education 4 47 10.98 9.62 8.00 9.38 5.93 1.00 53.0 52.00
## Catholic 5 47 41.14 41.70 15.14 39.12 18.65 2.15 100.0 97.85
## Infant.Mortality 6 47 19.94 2.91 20.00 19.98 2.82 10.80 26.6 15.80
## skew kurtosis se
## Fertility -0.46 0.26 1.82
## Agriculture -0.32 -0.89 3.31
## Examination 0.45 -0.14 1.16
## Education 2.27 6.14 1.40
## Catholic 0.48 -1.67 6.08
## Infant.Mortality -0.33 0.78 0.42
mydescribe=round(describe(swiss),3)
mydescribe%>%kbl()%>%kable_classic(html_font = "Courier New")
|
|
vars
|
n
|
mean
|
sd
|
median
|
trimmed
|
mad
|
min
|
max
|
range
|
skew
|
kurtosis
|
se
|
|
Fertility
|
1
|
47
|
70.143
|
12.492
|
70.40
|
70.659
|
10.230
|
35.00
|
92.5
|
57.50
|
-0.456
|
0.260
|
1.822
|
|
Agriculture
|
2
|
47
|
50.660
|
22.711
|
54.10
|
51.156
|
23.870
|
1.20
|
89.7
|
88.50
|
-0.320
|
-0.886
|
3.313
|
|
Examination
|
3
|
47
|
16.489
|
7.978
|
16.00
|
16.077
|
7.413
|
3.00
|
37.0
|
34.00
|
0.446
|
-0.137
|
1.164
|
|
Education
|
4
|
47
|
10.979
|
9.615
|
8.00
|
9.385
|
5.930
|
1.00
|
53.0
|
52.00
|
2.268
|
6.140
|
1.403
|
|
Catholic
|
5
|
47
|
41.144
|
41.705
|
15.14
|
39.116
|
18.651
|
2.15
|
100.0
|
97.85
|
0.479
|
-1.665
|
6.083
|
|
Infant.Mortality
|
6
|
47
|
19.943
|
2.913
|
20.00
|
19.985
|
2.817
|
10.80
|
26.6
|
15.80
|
-0.331
|
0.777
|
0.425
|
## Histograms
#Fertility
hist(swiss[,1], main= "Fertility", xlab= "Fertility")
#Agriculture
hist(swiss[,2], main= "Agriculture", xlab= "Agriculture")
#Examination
hist(swiss[,3], main= "Examination", xlab= "Examination")
#Education
hist(swiss[,4], main= "Education", xlab= "Education")
#Catholic
hist(swiss[,5], main= "Catholic", xlab= "Catholic")
#Infant Mortality
hist(swiss[,6], main= "Infant.Mortality", xlab= "Infant.Mortality")
## Creating 95% Confidence Intervals
#Fertility
t.test(swiss[,1])
##
## One Sample t-test
##
## data: swiss[, 1]
## t = 38.495, df = 46, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 66.47485 73.81025
## sample estimates:
## mean of x
## 70.14255
#Agriculture
t.test(swiss[,2])
##
## One Sample t-test
##
## data: swiss[, 2]
## t = 15.292, df = 46, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 43.99131 57.32784
## sample estimates:
## mean of x
## 50.65957
#Examination
t.test(swiss[,3])
##
## One Sample t-test
##
## data: swiss[, 3]
## t = 14.17, df = 46, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 14.14697 18.83176
## sample estimates:
## mean of x
## 16.48936
#Education
t.test(swiss[,4])
##
## One Sample t-test
##
## data: swiss[, 4]
## t = 7.8277, df = 46, p-value = 5.314e-10
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 8.155534 13.801913
## sample estimates:
## mean of x
## 10.97872
#Catholic
t.test(swiss[,5])
##
## One Sample t-test
##
## data: swiss[, 5]
## t = 6.7634, df = 46, p-value = 2.064e-08
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 28.89883 53.38883
## sample estimates:
## mean of x
## 41.14383
#Infant Mortality
t.test(swiss[,6])
##
## One Sample t-test
##
## data: swiss[, 6]
## t = 46.939, df = 46, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 19.08735 20.79775
## sample estimates:
## mean of x
## 19.94255
## Creating 99% Confidence Intervals
#Fertility
t.test(swiss[,1], conf.level=0.99)
##
## One Sample t-test
##
## data: swiss[, 1]
## t = 38.495, df = 46, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 99 percent confidence interval:
## 65.24654 75.03856
## sample estimates:
## mean of x
## 70.14255
#Agriculture
t.test(swiss[,2], conf.level=0.99)
##
## One Sample t-test
##
## data: swiss[, 2]
## t = 15.292, df = 46, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 99 percent confidence interval:
## 41.75811 59.56104
## sample estimates:
## mean of x
## 50.65957
#Examination
t.test(swiss[,3], conf.level=0.99)
##
## One Sample t-test
##
## data: swiss[, 3]
## t = 14.17, df = 46, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 99 percent confidence interval:
## 13.36250 19.61622
## sample estimates:
## mean of x
## 16.48936
#Education
t.test(swiss[,4], conf.level=0.99)
##
## One Sample t-test
##
## data: swiss[, 4]
## t = 7.8277, df = 46, p-value = 5.314e-10
## alternative hypothesis: true mean is not equal to 0
## 99 percent confidence interval:
## 7.210049 14.747398
## sample estimates:
## mean of x
## 10.97872
#Catholic
t.test(swiss[,5], conf.level=0.99)
##
## One Sample t-test
##
## data: swiss[, 5]
## t = 6.7634, df = 46, p-value = 2.064e-08
## alternative hypothesis: true mean is not equal to 0
## 99 percent confidence interval:
## 24.79798 57.48968
## sample estimates:
## mean of x
## 41.14383
#Infant Mortality
t.test(swiss[,6], conf.level=0.99)
##
## One Sample t-test
##
## data: swiss[, 6]
## t = 46.939, df = 46, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 99 percent confidence interval:
## 18.80095 21.08416
## sample estimates:
## mean of x
## 19.94255
## Creating 97% Confidence Intervals
#Fertility
t.test(swiss[,1], conf.level=0.97)
##
## One Sample t-test
##
## data: swiss[, 1]
## t = 38.495, df = 46, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 97 percent confidence interval:
## 66.06197 74.22314
## sample estimates:
## mean of x
## 70.14255
#Agriculture
t.test(swiss[,2], conf.level=0.97)
##
## One Sample t-test
##
## data: swiss[, 2]
## t = 15.292, df = 46, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 97 percent confidence interval:
## 43.24064 58.07851
## sample estimates:
## mean of x
## 50.65957
#Examination
t.test(swiss[,3], conf.level=0.97)
##
## One Sample t-test
##
## data: swiss[, 3]
## t = 14.17, df = 46, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 97 percent confidence interval:
## 13.88328 19.09545
## sample estimates:
## mean of x
## 16.48936
#Education
t.test(swiss[,4], conf.level=0.97)
##
## One Sample t-test
##
## data: swiss[, 4]
## t = 7.8277, df = 46, p-value = 5.314e-10
## alternative hypothesis: true mean is not equal to 0
## 97 percent confidence interval:
## 7.837718 14.119729
## sample estimates:
## mean of x
## 10.97872
#Catholic
t.test(swiss[,5], conf.level=0.97)
##
## One Sample t-test
##
## data: swiss[, 5]
## t = 6.7634, df = 46, p-value = 2.064e-08
## alternative hypothesis: true mean is not equal to 0
## 97 percent confidence interval:
## 27.52036 54.76730
## sample estimates:
## mean of x
## 41.14383
#Infant Mortality
t.test(swiss[,6], conf.level=0.97)
##
## One Sample t-test
##
## data: swiss[, 6]
## t = 46.939, df = 46, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 97 percent confidence interval:
## 18.99108 20.89403
## sample estimates:
## mean of x
## 19.94255