data <- read.csv("https://raw.githubusercontent.com/tmatis12/datafiles/main/normtemp.csv")
Males<-data[1:65,3]
Summary_males <- summary(Males)
variance_males <- var(Males)
standard_deviation_males <- sd(Males)
IQR_males <- IQR (Males)
Summary_males
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 58.00 70.00 73.00 73.37 78.00 86.00
variance_males
## [1] 34.51779
standard_deviation_males
## [1] 5.875184
IQR_males
## [1] 8
hist_male <- hist(Males,main="Histogram of Male Heartbeat",col="blue", xlab = "Resting heartbeat")
1) The above Histogram looks fairly similar to Normal Distribution, with cluster at center and with thin tails as the both end.
2) Most males resting heartbeat falls in bin of 70-75.
Normal_male_plot <- qqnorm(Males,main ="Normal Probability Plot for Resting Heartbeat of Males" , col="blue")
1) The Probability plot looks fairly straight, at least when the outliers are ignored, hence we can say that its a normally distributed data. 2) As the plot is normally distributed, hence the skewness will be Zero.
females<-data[66:130,3]
summary_females <- summary(females)
variance_females <- var(females)
standard_deviation_females <- sd(females)
IQR_females <- IQR (females)
summary_females
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 57.00 68.00 76.00 74.15 80.00 89.00
variance_females
## [1] 65.69471
standard_deviation_females
## [1] 8.105227
IQR_females
## [1] 12
From above statistics we can see that minimum is 57, maximum is 89, standard deviation is 8.1052274 and Sample mean is 74.1538462
hist_female <-hist(females,main="Histogram of Female Heartbeat",col="pink",xlab = "Resting Heartbeat")
1) The above Histogram is Left Skewed, with cluster of data towards the right side.
2) Most Females resting heartbeat falls in bin of 75-80.
Normal_female_plot <- qqnorm(females,main ="Normal Probability plot for heartbeat of females" , col="pink")
The Probability plot looks left skewed.
c <- boxplot(Males,females,main ="Resting Heartrates for Males and Females respectively",names = c("Males", "Females"),ylab="Resting Heartbeat")
1) Above box plot shows median for female is greater than that of Males.
2) InterQuartile Range of Female is greater than that of Males.
3) Maximum for Males and Females from above Box plot is 86 and 89 respectively.
4) Minimum for Males and Females from above Box plot falls between 70-75 and 75-80 respectively.
data <- read.csv("https://raw.githubusercontent.com/tmatis12/datafiles/main/normtemp.csv")
## males ------------------------
Males<-data[1:65,3]
#Descriptive Statistics for Male
Summary_males <- summary(Males)
summary_male_list <- as.list(Summary_males)
variance_males <- var(Males)
std_males <- sd(Males)
IQR_males <- IQR (Males)
# Printing stats for Male
Summary_males
variance_males
standard_deviation_males
IQR_males
# Histogram for male
hist_male <- hist(Males,main="Histogram of Male Heartbeat",col="blue", xlab = "Resting heartbeat")
#Normal Probility plot
Normal_male_plot <- qqnorm(Males,main ="Normal Probability Plot for Resting Heartbeat of Males" , col="blue")
# Females ----------------------
females<-data[66:130,3]
#Descriptive Statistics for female
summary_females <- summary(females)
summary_females_list <- as.list(summary_female)
variance_females <- var(females)
std_females <- sd(females)
IQR_females <- IQR (females)
# printing
summary_females
variance_females
standard_deviation_females
IQR_females
# Histogram for female
hist_female <-hist(females,main="Histogram of Female Heartbeat",col="pink",xlab = "Resting Heartbeat")
#Normal Probability plot for female
Normal_female_plot <- qqnorm(females,main ="Normal Probability plot for heartbeat of females" , col="pink")
#-------------------------------------------------------------------------------
c <- boxplot(Males,females,main ="Resting Heartrates for Males and Females respectively",names = c("Males", "Females"),ylab="Resting Heartbeat")
Comments :-
From above statistics we can see that minimum is 58, maximum is 86, standard deviation is 5.8751841 and Sample mean is 73.3692308