Births,Deaths,Infant Deaths,Male Life,Female Life,GNP,Group,Country 24.7,5.7,30.8,69.6,75.5,600,1,Albania 12.5,11.9,14.4,68.3,74.7,2250,1,Bulgaria 13.4,11.7,11.3,71.8,77.7,2980,1,Czechoslovakia...

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https://docs.google.com/spreadsheets/d/1OTZeUM8w2jHlMe2vtxo4EXrUPyH3XSCLMT-CZLpfUBE/edit?usp=sharing
For unesco data
Data Statistics.


Births,Deaths,Infant Deaths,Male Life,Female Life,GNP,Group,Country 24.7,5.7,30.8,69.6,75.5,600,1,Albania 12.5,11.9,14.4,68.3,74.7,2250,1,Bulgaria 13.4,11.7,11.3,71.8,77.7,2980,1,Czechoslovakia 11.6,13.4,14.8,65.4,73.8,2780,1,Hungary 14.3,10.2,16,67.2,75.7,1690,1,Poland 13.6,10.7,26.9,66.5,72.4,1640,1,Romania 17.7,10,23,64.6,74,2240,1,USSR 20.7,8.4,25.7,65.5,72.7,2370,2,Argentina 46.6,18,111,51,55.4,630,2,Bolivia 28.6,7.9,63,62.3,67.6,2680,2,Brazil 23.4,5.8,17.1,68.1,75.1,1940,2,Chile 27.4,6.1,40,63.4,69.2,1260,2,Columbia 32.9,7.4,63,63.4,67.6,980,2,Ecuador 28.3,7.3,56,60.4,66.1,330,2,Guyana 34.8,6.6,42,64.4,68.5,1110,2,Paraguay 32.9,8.3,110,56.8,66.5,1160,2,Peru 18,9.6,21.9,68.4,74.9,2560,2,Uruguay 27.5,4.4,23.3,66.7,72.8,2560,2,Venezuela 29,23.2,43,62.1,66,2490,2,Mexico 12,10.6,7.9,70,76.8,15500,3,Belgium 13.2,10.1,5.8,70.7,78.7,26000,3,Finland 12.4,11.9,7.5,71.8,77.7,22100,3,Denmark 13.6,9.4,7.4,72.3,80.5,19500,3,France 11.4,11.2,7.4,71.8,78.4,22300,3,Germany 10.1,9.2,11,65.4,74,5990,3,Greece 15.1,9.1,7.5,71,76.7,9550,3,Ireland 9.7,9.1,8.8,72,78.6,16800,3,Italy 13.2,8.6,7.1,73.3,79.9,17300,3,Netherlands 14.3,10.7,7.8,67.2,75.7,23100,3,Norway 11.9,9.5,13.1,66.5,72.4,7600,3,Portugal 10.7,8.2,8.1,72.5,78.6,11000,3,Spain 14.5,11.1,5.6,74.2,80,23700,3,Sweden 12.5,9.5,7.1,73.9,80,34100,3,Switzerland 13.6,11.5,8.4,72.2,77.9,16100,3,U.K. 14.9,7.4,8,73.3,79.6,17000,3,Austria 9.9,6.7,4.5,75.9,81.8,25400,3,Japan 14.5,7.3,7.2,73,79.8,20500,3,Canada 16.7,8.1,9.1,71.5,78.3,21800,3,U.S.A. 28.4,3.8,16,66.8,69.4,6340,4,Bahrain 42.5,11.5,108,55.8,55,2490,4,Iran 42.6,7.8,69,63,64.8,3020,4,Iraq 22.3,6.3,9.7,73.9,77.4,10900,4,Israel 38.9,6.4,44,64.2,67.8,1240,4,Jordan 26.8,2.2,15.6,71.2,75.4,16200,4,Kuwait 45.6,7.8,40,62.2,65.8,5220,4,Oman 42.1,7.6,71,61.7,65.2,7050,4,Saudi Arabia 29.2,8.4,76,62.5,65.8,1630,4,Turkey 22.8,3.8,26,68.6,72.9,19900,4,United Arab Emirates 40.4,18.7,182,41,42,168,5,Afghanistan 42.2,15.5,119,56.9,56,210,5,Bangladesh 21.2,6.7,32,68,70.9,380,5,China 11.7,4.9,6.1,74.3,80.1,14200,5,Hong Kong 30.5,10.2,91,52.5,52.1,350,5,India 28.6,9.4,75,58.5,62,570,5,Indonesia 31.6,5.6,24,67.5,71.6,2320,5,Malaysia 36.1,8.8,68,60,62.5,110,5,Mongolia 39.6,14.8,128,50.9,48.1,170,5,Nepal 30.3,8.1,108,59,59.2,380,5,Pakistan 33.2,7.7,45,62.5,66.1,730,5,Philippines 17.8,5.2,7.5,68.7,74,11200,5,Singapore 21.3,6.2,19.4,67.8,71.7,470,5,Sri Lanka 22.3,7.7,28,63.8,68.9,1420,5,Thailand 35.5,8.3,74,61.6,63.3,2060,6,Algeria 47.2,20.2,137,42.9,46.1,610,6,Angola 48.5,11.6,67,52.3,59.7,2040,6,Botswana 46.1,14.6,73,50.1,55.3,1010,6,Congo 38.8,9.5,49.4,57.8,60.3,600,6,Egypt 48.6,20.7,137,42.4,45.6,120,6,Ethiopia 39.4,16.8,103,49.9,53.2,390,6,Gabon 47.4,21.4,143,41.4,44.6,260,6,Gambia 44.4,13.1,90,52.2,55.8,390,6,Ghana 47,11.3,72,56.5,60.5,370,6,Kenya 44,9.4,82,59.1,62.6,5310,6,Libya 48.3,25,130,38.1,41.2,200,6,Malawi 35.5,9.8,82,59.1,62.5,960,6,Morocco 45,18.5,141,44.9,48.1,80,6,Mozambique 44,12.1,135,55,57.5,1030,6,Namibia 48.5,15.6,105,48.8,52.2,360,6,Nigeria 48.2,23.4,154,39.4,42.6,240,6,Sierra Leone 50.1,20.2,132,43.4,46.6,120,6,Somalia 32.1,9.9,72,57.5,63.5,2530,6,South Africa 44.6,15.8,108,48.6,51,480,6,Sudan 46.8,12.5,118,42.9,49.5,810,6,Swaziland 31.1,7.3,52,64.9,66.4,1440,6,Tunisia 52.2,15.6,103,49.9,52.7,220,6,Uganda 50.5,14,106,51.3,54.7,110,6,Tanzania 45.6,14.2,83,50.3,53.7,220,6,Zaire 51.1,13.7,80,50.4,52.5,420,6,Zambia 41.7,10.3,66,56.5,60.1,640,6,Zimbabwe
Answered Same DayApr 25, 2021

Answer To: Births,Deaths,Infant Deaths,Male Life,Female Life,GNP,Group,Country...

Naveen answered on Apr 26 2021
139 Votes
# installing required packages
install.packages("dplyr")
install.packages("DescTools")
install.packages("lattice")
# Loading the required packages
library(dplyr)
library(lattice)
library(DescTools)
# Reading the dataset
df <- read.cs
v('unesco.csv')
# Displaying top six records
head(df)
#------------------------Q1. A ----------------------
# Summary statistics
summary(df$GNP)
# Plotting the Histogram of GNP
par(mfrow=c(1,1))
hist(df$GNP,main = 'Histogram of GNP',
freq = FALSE, xlab = ' ', ylab = 'Density',
col = rainbow(8))
# Adding density lines
x <- seq(min(df$GNP), max(df$GNP), length.out=length(df$GNP))
y <- with(df, dnorm(x, mean(GNP), sd(GNP)))
lines(x,y, col = "red",lwd=2)
# Plotting the boxplot of GNP
boxplot(df$GNP,col = 'skyblue',
main='Boxplot of GNP',
ylab='Frequency ')
#the obvious features is GNP
#------------------------Q1. B ----------------------
# Plotting the histogram of Infant Deaths
hist(df$Infant.Deaths,freq = FALSE,
main = 'Histogram of Infant Deaths',
col = rainbow(8))
# Plotting density lines
x <- seq(min(df$Infant.Deaths), max(df$Infant.Deaths), length.out=length(df$Infant.Deaths))
y <- with(df, dnorm(x, mean(Infant.Deaths), sd(Infant.Deaths)))
lines(x,y, col = "red",lwd=2)
# Plotting scatterplot of Infant Deaths
plot(df$Infant.Deaths,col ='green',
main='Scatter plot of Infant Deaths',
ylab='Number of infant deaths')
# There is a signifiacnt difference between number of infant deaths
#-----------------------Q1. C -----------------------
# Plotting a scatter plot to compare Infant deaths and GNP
plot(df$Infant.Deaths,df$GNP,
xlab = 'Infant Deaths',
ylab = 'GNP', col='blue')
# Correlation coefficient of Infant Deaths and GNP
cor(df$Infant.Deaths,df$GNP)
## Comment: There is no correlation between Infant deaths and GNP
#-----------------------Q1. D------------------------
# Plotting the relationship between life expectancy at birth for females and birth rate
plot(df$Female.Life,df$Births,
xlab = 'Birth rate',
ylab = 'Female life expectancy',
main = 'Birth rate vs Female Life',
col='red')
# There is negative correlation between female life expectancy and births
# Building regression model, births on female life expectancy
model1 = lm(Births~Female.Life, data = df)
summary(model1)
# Plotting the Residuals vs fitted...
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