Assignment 2
Mahfuzul
2025-11-11
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniondat <- read.csv("https://raw.githubusercontent.com/tmatis12/datafiles/main/normtemp.csv", header = TRUE)
dat## Temp Sex Beats
## 1 96.3 1 70
## 2 96.7 1 71
## 3 96.9 1 74
## 4 97.0 1 80
## 5 97.1 1 73
## 6 97.1 1 75
## 7 97.1 1 82
## 8 97.2 1 64
## 9 97.3 1 69
## 10 97.4 1 70
## 11 97.4 1 68
## 12 97.4 1 72
## 13 97.4 1 78
## 14 97.5 1 70
## 15 97.5 1 75
## 16 97.6 1 74
## 17 97.6 1 69
## 18 97.6 1 73
## 19 97.7 1 77
## 20 97.8 1 58
## 21 97.8 1 73
## 22 97.8 1 65
## 23 97.8 1 74
## 24 97.9 1 76
## 25 97.9 1 72
## 26 98.0 1 78
## 27 98.0 1 71
## 28 98.0 1 74
## 29 98.0 1 67
## 30 98.0 1 64
## 31 98.0 1 78
## 32 98.1 1 73
## 33 98.1 1 67
## 34 98.2 1 66
## 35 98.2 1 64
## 36 98.2 1 71
## 37 98.2 1 72
## 38 98.3 1 86
## 39 98.3 1 72
## 40 98.4 1 68
## 41 98.4 1 70
## 42 98.4 1 82
## 43 98.4 1 84
## 44 98.5 1 68
## 45 98.5 1 71
## 46 98.6 1 77
## 47 98.6 1 78
## 48 98.6 1 83
## 49 98.6 1 66
## 50 98.6 1 70
## 51 98.6 1 82
## 52 98.7 1 73
## 53 98.7 1 78
## 54 98.8 1 78
## 55 98.8 1 81
## 56 98.8 1 78
## 57 98.9 1 80
## 58 99.0 1 75
## 59 99.0 1 79
## 60 99.0 1 81
## 61 99.1 1 71
## 62 99.2 1 83
## 63 99.3 1 63
## 64 99.4 1 70
## 65 99.5 1 75
## 66 96.4 2 69
## 67 96.7 2 62
## 68 96.8 2 75
## 69 97.2 2 66
## 70 97.2 2 68
## 71 97.4 2 57
## 72 97.6 2 61
## 73 97.7 2 84
## 74 97.7 2 61
## 75 97.8 2 77
## 76 97.8 2 62
## 77 97.8 2 71
## 78 97.9 2 68
## 79 97.9 2 69
## 80 97.9 2 79
## 81 98.0 2 76
## 82 98.0 2 87
## 83 98.0 2 78
## 84 98.0 2 73
## 85 98.0 2 89
## 86 98.1 2 81
## 87 98.2 2 73
## 88 98.2 2 64
## 89 98.2 2 65
## 90 98.2 2 73
## 91 98.2 2 69
## 92 98.2 2 57
## 93 98.3 2 79
## 94 98.3 2 78
## 95 98.3 2 80
## 96 98.4 2 79
## 97 98.4 2 81
## 98 98.4 2 73
## 99 98.4 2 74
## 100 98.4 2 84
## 101 98.5 2 83
## 102 98.6 2 82
## 103 98.6 2 85
## 104 98.6 2 86
## 105 98.6 2 77
## 106 98.7 2 72
## 107 98.7 2 79
## 108 98.7 2 59
## 109 98.7 2 64
## 110 98.7 2 65
## 111 98.7 2 82
## 112 98.8 2 64
## 113 98.8 2 70
## 114 98.8 2 83
## 115 98.8 2 89
## 116 98.8 2 69
## 117 98.8 2 73
## 118 98.8 2 84
## 119 98.9 2 76
## 120 99.0 2 79
## 121 99.0 2 81
## 122 99.1 2 80
## 123 99.1 2 74
## 124 99.2 2 77
## 125 99.2 2 66
## 126 99.3 2 68
## 127 99.4 2 77
## 128 99.9 2 79
## 129 100.0 2 78
## 130 100.8 2 77dat_male <- dat %>% filter(Sex==1)
dat_male## Temp Sex Beats
## 1 96.3 1 70
## 2 96.7 1 71
## 3 96.9 1 74
## 4 97.0 1 80
## 5 97.1 1 73
## 6 97.1 1 75
## 7 97.1 1 82
## 8 97.2 1 64
## 9 97.3 1 69
## 10 97.4 1 70
## 11 97.4 1 68
## 12 97.4 1 72
## 13 97.4 1 78
## 14 97.5 1 70
## 15 97.5 1 75
## 16 97.6 1 74
## 17 97.6 1 69
## 18 97.6 1 73
## 19 97.7 1 77
## 20 97.8 1 58
## 21 97.8 1 73
## 22 97.8 1 65
## 23 97.8 1 74
## 24 97.9 1 76
## 25 97.9 1 72
## 26 98.0 1 78
## 27 98.0 1 71
## 28 98.0 1 74
## 29 98.0 1 67
## 30 98.0 1 64
## 31 98.0 1 78
## 32 98.1 1 73
## 33 98.1 1 67
## 34 98.2 1 66
## 35 98.2 1 64
## 36 98.2 1 71
## 37 98.2 1 72
## 38 98.3 1 86
## 39 98.3 1 72
## 40 98.4 1 68
## 41 98.4 1 70
## 42 98.4 1 82
## 43 98.4 1 84
## 44 98.5 1 68
## 45 98.5 1 71
## 46 98.6 1 77
## 47 98.6 1 78
## 48 98.6 1 83
## 49 98.6 1 66
## 50 98.6 1 70
## 51 98.6 1 82
## 52 98.7 1 73
## 53 98.7 1 78
## 54 98.8 1 78
## 55 98.8 1 81
## 56 98.8 1 78
## 57 98.9 1 80
## 58 99.0 1 75
## 59 99.0 1 79
## 60 99.0 1 81
## 61 99.1 1 71
## 62 99.2 1 83
## 63 99.3 1 63
## 64 99.4 1 70
## 65 99.5 1 75dat_female <- dat %>% filter(Sex==2)
dat_female## Temp Sex Beats
## 1 96.4 2 69
## 2 96.7 2 62
## 3 96.8 2 75
## 4 97.2 2 66
## 5 97.2 2 68
## 6 97.4 2 57
## 7 97.6 2 61
## 8 97.7 2 84
## 9 97.7 2 61
## 10 97.8 2 77
## 11 97.8 2 62
## 12 97.8 2 71
## 13 97.9 2 68
## 14 97.9 2 69
## 15 97.9 2 79
## 16 98.0 2 76
## 17 98.0 2 87
## 18 98.0 2 78
## 19 98.0 2 73
## 20 98.0 2 89
## 21 98.1 2 81
## 22 98.2 2 73
## 23 98.2 2 64
## 24 98.2 2 65
## 25 98.2 2 73
## 26 98.2 2 69
## 27 98.2 2 57
## 28 98.3 2 79
## 29 98.3 2 78
## 30 98.3 2 80
## 31 98.4 2 79
## 32 98.4 2 81
## 33 98.4 2 73
## 34 98.4 2 74
## 35 98.4 2 84
## 36 98.5 2 83
## 37 98.6 2 82
## 38 98.6 2 85
## 39 98.6 2 86
## 40 98.6 2 77
## 41 98.7 2 72
## 42 98.7 2 79
## 43 98.7 2 59
## 44 98.7 2 64
## 45 98.7 2 65
## 46 98.7 2 82
## 47 98.8 2 64
## 48 98.8 2 70
## 49 98.8 2 83
## 50 98.8 2 89
## 51 98.8 2 69
## 52 98.8 2 73
## 53 98.8 2 84
## 54 98.9 2 76
## 55 99.0 2 79
## 56 99.0 2 81
## 57 99.1 2 80
## 58 99.1 2 74
## 59 99.2 2 77
## 60 99.2 2 66
## 61 99.3 2 68
## 62 99.4 2 77
## 63 99.9 2 79
## 64 100.0 2 78
## 65 100.8 2 77Summary data of Males Temperature & resting heart rate (Min, max, mean, median and quartile value)
summary(dat_male$Temp)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 96.3 97.6 98.1 98.1 98.6 99.5summary(dat_male$Beats)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 58.00 70.00 73.00 73.37 78.00 86.00Male_heartrate_sd <- sd(dat_male$Beats)
Male_Temp_sd <- sd(dat_male$Temp)
Male_heartrate_sd## [1] 5.875184Male_Temp_sd## [1] 0.6987558From above we can see the Min, max, mean, median and quartile value and SD of male heart rate and Temperaure where and SD of male heartrate is (5.875184) and temperate is ( 0.6987558) , where we can se SD value of male heart is little less than female which we can also see in later box plot explanation
Summary data of Females Temperature & resting heart rate (Min, max, mean, median and quartile value)
summary(dat_female$Temp)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 96.40 98.00 98.40 98.39 98.80 100.80summary(dat_female$Beats)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 57.00 68.00 76.00 74.15 80.00 89.00Female_heartrate_sd <- sd(dat_female$Beats)
Female_heartrate_sd## [1] 8.105227Female_Temp_sd <- sd(dat_female$Temp)
Female_Temp_sd## [1] 0.7434878From above we can see the Min, max, mean, median and quartile value and SD of female heart rate (8.105227) and SD of female Temp (0.7434878), where we can se SD value of female heart is more.
#Histogram and Normal probability plot of Male’s Heartrate and Temperature data
hist(dat_male$Beats, main = "Histogram of Male heartrate data",col = "blue", xlab="respective heartrate", ylab="Number of male")
qqnorm(dat_male$Beats, main="Male heartrate Normal Q-Q Plot", xlab = "Theoretical heatrate data", ylab="sample hearrate male")
hist(dat_male$Temp, main = "Histogram of Male Temp data",col = "blue", xlab="respective Temp", ylab="Number of male")
qqnorm(dat_male$Temp, main="Male Temp Normal Q-Q Plot", xlab = "Theoretical Temp data", ylab="sample Temp male")
#Histogram and Normal probability plot of Male’s Heart-rate and Temperature data
hist(dat_female$Beats, main = "Histogram of Female heartrate data",col = "pink", xlab="respective heartrate", ylab="Number of Female")
qqnorm(dat_female$Beats, main="Female heartrate Normal Q-Q Plot", xlab = "Theoretical heatrate data", ylab="sample hearrate Female")
hist(dat_female$Temp, main = "Histogram of Female Temp data",col = "pink", xlab="respective Temp", ylab="Number of Female")
qqnorm(dat_female$Temp, main="Female Temp Normal Q-Q Plot", xlab = "Theoretical Temp data", ylab="sample Temp Female")
boxplot(dat_male$Beats, dat_female$Beats ,names = c("Male", "Female"), col= c("blue","pink"),main= c("Resting heart rate comparison") ,xlab = "Gender", ylab= "Resting Heart rate")
From above boxplot we can see that the males heartrate is comparitievly less spreaded from around 60-85 with the most of the values falls within 70-77 with midean around 72. On the other hand females heartrate is comparitievly more spreaded from around 50-98 with the most of the values falls within 69-80 with midean around 77.