R Markdown

Dat<-read.csv("https://raw.githubusercontent.com/tmatis12/datafiles/main/normtemp.csv")
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    77
Male<-Dat[Dat$Sex==1,]
Male
##    Temp Sex Beats
## 1  96.3   1    70
## 2  96.7   1    71
## 3  96.9   1    74
## 4  97.0   1    80
## 5  97.1   1    73
## 6  97.1   1    75
## 7  97.1   1    82
## 8  97.2   1    64
## 9  97.3   1    69
## 10 97.4   1    70
## 11 97.4   1    68
## 12 97.4   1    72
## 13 97.4   1    78
## 14 97.5   1    70
## 15 97.5   1    75
## 16 97.6   1    74
## 17 97.6   1    69
## 18 97.6   1    73
## 19 97.7   1    77
## 20 97.8   1    58
## 21 97.8   1    73
## 22 97.8   1    65
## 23 97.8   1    74
## 24 97.9   1    76
## 25 97.9   1    72
## 26 98.0   1    78
## 27 98.0   1    71
## 28 98.0   1    74
## 29 98.0   1    67
## 30 98.0   1    64
## 31 98.0   1    78
## 32 98.1   1    73
## 33 98.1   1    67
## 34 98.2   1    66
## 35 98.2   1    64
## 36 98.2   1    71
## 37 98.2   1    72
## 38 98.3   1    86
## 39 98.3   1    72
## 40 98.4   1    68
## 41 98.4   1    70
## 42 98.4   1    82
## 43 98.4   1    84
## 44 98.5   1    68
## 45 98.5   1    71
## 46 98.6   1    77
## 47 98.6   1    78
## 48 98.6   1    83
## 49 98.6   1    66
## 50 98.6   1    70
## 51 98.6   1    82
## 52 98.7   1    73
## 53 98.7   1    78
## 54 98.8   1    78
## 55 98.8   1    81
## 56 98.8   1    78
## 57 98.9   1    80
## 58 99.0   1    75
## 59 99.0   1    79
## 60 99.0   1    81
## 61 99.1   1    71
## 62 99.2   1    83
## 63 99.3   1    63
## 64 99.4   1    70
## 65 99.5   1    75
Female<-Dat[Dat$Sex==2,]
Female
##      Temp Sex Beats
## 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    77
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    77
hist(Male$Beats, col="blue")

hist(Female$Beats, col="pink")

Comment: Central tendency: Females > Males (Females have higher typical resting heart rates).

Variation: Females have slightly more variation.

Shape: Male distribution is smoother and symmetric, while the female distribution is more uneven.

qqnorm(Male$Beats)

qqnorm(Female$Beats)

Comments:

Male heart rate data is approximately normally distributed, with only minor tail deviations.

Female heart rate data shows more variation and positive skewness, driven by several higher heart rate values.

This matches the earlier histogram findings where females had more spread and higher extreme heart rates compared to males.

boxplot(Male$Beats, Female$Beats, maine= "Side by side Boxplot", names=c("Male", "Female"),xlab="gender", ylab="heartbeat",col=c("blue", "pink"))

Females generally have higher typical resting heart rates compared to males.

Female heart rates are more spread out, suggesting more diversity in heart rate patterns.

Males: The box is symmetric, showing a fairly normal, balanced distribution.

Females: The box leans slightly upward (longer top whisker), indicating a mild positive skew with a few individuals having very high heart rates.

SUMMARY:

Dat<-read.csv("https://raw.githubusercontent.com/tmatis12/datafiles/main/normtemp.csv")
Male<-Dat[Dat$Sex==1,]
Male
Female<-Dat[Dat$Sex==2,]
Female
hist(Male$Beats, col="blue")
hist(Female$Beats, col="pink")
qqnorm(Male$Beats)
qqnorm(Female$Beats)
boxplot(Male$Beats, Female$Beats, main="Side by Side Boxplot",names=c("Male","Female"),
        xlab="gender",ylab="heartbeat",col=c("blue","pink"))