Types of Variables and Graphs
Data can exist as ordinal, nominal, ratio data, and other states. Nominal and ordinal variables were recorded in the practical involving human heart rate. Nominal variable refers to data existing in unnatural or random order (García, Luengo, & Herrera, 2015). Nominal variable includes gender, ethnicity, and eye color, among others. The data on heart rates is nominal data as the readings are random. Pie charts can be used to display nominal variables such as gender used in the study. Ordinal data refers to a type of data that occurs “when there is a natural order among the categories, such as, ranking scales or letter grades." (Willems, Fiocco, & Meulman (2017). Ordinal variables in the study were body temperature and heart rate. These variables can be plotted best on a line graph.
Graphed Data
Nominal Data
The practical's objective was to determine the impact of increasing body temperature on heart rate. The study's hypothesis involved the postulation that increases in body temperature would result in increased heart rate up to an optimum level. The study involved 130 participants. Of the total participants, 65 were female and 65 males as represented in the pie chart.
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Ordinal Data
Figure 1 the graph of heart rate against temperature
The graph above (line graph), represents the relationship between body temperature and heart rate. According to the graph, the heart rate is highest when the body temperature is approximately 98 0 F. This is the optimum temperature below and above which the heart rate reduces significantly. Body temperature must, therefore, be maintained at 98 0 F for normal heart function.
Descriptive Statistics
Summary Statistics for the Male at Rest Heart Rate
| Mean | 80.38519 |
| Standard Error | 0.678623 |
| Median | 80.1 |
| Mode | 74.8 |
| Standard Deviation | 7.052458 |
| Sample Variance | 49.73716 |
| Kurtosis | -0.20499 |
| Skewness | -0.1393 |
| Range | 37.1 |
| Minimum | 59 |
| Maximum | 96.1 |
| Sum | 8681.6 |
| Count | 108 |
The heart rate of 108 male at rest was recorded. The arithmetic mean of the 108 participants was 80.38519. This indicates that most of the participants had their heart rate around 80.38519. The standard deviation is 7.052458. The standard deviation shows how spread out the values are in the data set from the arithmetic mean ( Stamler et al., 2003) . The highest value of 96.1 is around the standard deviation from the arithmetic mean. Additionally, the participants’ heart rates had a Sample variance of 49.737116 from the mean ( Bickel& Lehmann, 2012) .
Summary Statistics for Female at-rest Heart Rate
| Mean | 81.76957 |
| Standard Error | 0.653569 |
| Median | 80.8 |
| Mode | 80.6 |
| Standard Deviation | 6.268809 |
| Sample Variance | 39.29796 |
| Kurtosis | 0.390932 |
| Skewness | 0.181588 |
| Range | 32 |
| Minimum | 65.3 |
| Maximum | 97.3 |
| Sum | 7522.8 |
| Count | 92 |
A sample of 92 females was collected, and their heart rate recorded, at rest. The arithmetic mean of the sample was 81.76957. This indicates that the majority of the participants had a heart rate of 81.76957. The sample had a standard deviation of 6.268809. The maximum heart rate is three standard deviations from the mean. The female participants had a variance of 39.29796, and it was found by squaring the standard deviation.
Sample variance and standard deviation of the female at rest heart rate is less than the sample variance and standard deviation of males at rest heart rate. It, therefore, shows that the values of female at rest heart rate are closer to the mean than for male at rest heart rate.
Summary Statistics for Male Heart Rate after Exercise
| Mean | 90.28004 |
| Standard Error | 0.752483 |
| Median | 90.352 |
| Mode | 99.692 |
| Standard Deviation | 7.820035 |
| Sample Variance | 61.15295 |
| Kurtosis | -0.1234 |
| Skewness | -0.28858 |
| Range | 38.504 |
| Minimum | 68.16 |
| Maximum | 106.664 |
| Sum | 9750.244 |
| Count | 108 |
The sample of the 108 males was involved in the second phase of the study. Their heart rates were taken after exercise. The arithmetic mean of the sample was 90.28004. The sample had a standard deviation of 7.820035. The maximum value of 106.664 was within three standard deviations from the mean. The sample mean for the male at rest was 80.38519. The mean at rest (80.38519) is lower than the mean after exercise (90.28004). That indicates that exercise increases heartbeat rates.
Summary Statistics for Female Heart Rate after Exercise
| Mean | 91.22991 |
| Standard Error | 0.616421 |
| Median | 90.576 |
| Mode | #N/A |
| Standard Deviation | 5.912501 |
| Sample Variance | 34.95767 |
| Kurtosis | 0.274909 |
| Skewness | -0.03382 |
| Range | 31.656 |
| Minimum | 72.824 |
| Maximum | 104.48 |
| Sum | 8393.152 |
| Count | 92 |
In the final phase of the study, the heart rate of 92 women was recorded after exercise. The arithmetic mean was 91.22991. The arithmetic mean for the female participants at rest was 81.76957. The mean at rest (81.76957) is lower than the mean after exercise (91.22991). The standard deviation and sample variance are 5.912501 and 34.95767, respectively. The standard deviation is small, and this indicates that the values in the data set are close to the mean.
Conclusion
Heart rate is directly proportional to body temperature up to an optimum level. That is, an increase in body temperature results in increased heart rate up to a point, 98F 0 , beyond which the heart rate falls. The sample means for women after exercise and after exercise, 91.22991and 81.76957, respectively, are higher than those of male participants, 90.28004 and 80.38519, respectively. The variations in the heart rate indicate that women have a higher heart rate than male. It also indicates that the heart rate is low at rest and increases during exercise. The conclusion is that exercise results in an increased heart rate; therefore, there is a relationship between exercise and body temperature. A study by Reimers, Knapp & Reimers (2018) links heart rate to body exercise. Therefore, it is expected that involvement in any form of exercise will produce a different reading, low or high, from the reading during rest. Body temperature increases with exercise (Tsuzuki et al. 2018). This explains why the heart rates for both genders increased with participation in exercise. Geneva, Cuzzo, Fazili & Javaid (2019) notes that female body temperature can be higher than their male counterparts. However, they do not provide reasons for the findings. The phenomenon provides paths for further studies.
References
Bickel, P. J., & Lehmann, E. L. (2012). Descriptive statistics for nonparametric models I. Introduction. In Selected Works of EL Lehmann (pp. 465-471). Springer, Boston, MA.
García, S., Luengo, J., & Herrera, F. (2015). Data preprocessing in data mining (pp. 59-139). New York: Springer.
Geneva, I. I., Cuzzo, B., Fazili, T., & Javaid, W. (2019, April). Normal body temperature: a systematic review. In Open Forum Infectious Diseases (Vol. 6, No. 4, p. ofz032). US: Oxford University Press.
Reimers, A., Knapp, G., & Reimers, C. D. (2018). Effects of exercise on the resting heart rate: a systematic review and meta-analysis of interventional studies. Journal of clinical medicine, 7(12), 503.
Stamler, J., Elliott, P., Dennis, B., Dyer, A. R., Kesteloot, H., Liu, K., ... & Zhou, B. F. (2003). INTERMAP: background, aims, design, methods, and descriptive statistics (nondietary). Journal of human hypertension, 17(9), 591.
Tsuzuki, T., Yoshihara, T., Ichinoseki-Sekine, N., Kakigi, R., Takamine, Y., Kobayashi, H., & Naito, H. (2018). Body temperature elevation during exercise is essential for activating the Akt signaling pathway in the skeletal muscle of type 2 diabetic rats. PloS one, 13(10), e0205456.
Willems, S. J. W., Fiocco, M., & Meulman, J. J. (2017). Optimal scaling for survival analysis with ordinal data. Computational Statistics & Data Analysis, 115, 155-171.
