Bio-Statistics Report: Statistical Analysis of Health-Related Data

This report presents a bio-statistical analysis of provided data. The report begins by examining the smoking status data, detailing the percentages of current, former, and never smokers. The analysis reveals that the data is negatively skewed, a concept explained in terms of the distribution's asymmetry. The report further explores the age data, determining the median, minimum, and maximum values. It concludes that the age data follows a normal distribution, despite variations in the histogram. Finally, the report distinguishes between quantitative and categorical variables, highlighting how each type is described and analyzed statistically, with a focus on the normal curve and Q-Q plots, respectively. The report is supported by references to relevant literature.

Running Head: BIO-STATISTICS
Bio-Statistics
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Q1. The missing word is Normal.
Q2. As represented by the smoking status analysis data, 41 individuals from the sample
collection are current smokers which is 20.50% while 53 individuals were former smokers which
makes 26.50% and finally 106 individuals who vowed to have never smoked makes 53.0%.
From the data frequency, the data tails off to the left which implies that the data is negatively
skewed. This factor applies true for the percentage of the respective frequencies as well as that of
the cumulative frequency and the cumulative percentage. Skewness in statistics can be defined as
a symmetry measure of a distribution about the mean of the real value of a random variable
(Bezavov et al., 2017).
Q.3
The age data depicts a distribution whereby the median lies almost in the middle of the
maximum value and the minimum value. The minimum is 20 with the median as 46, and the
maximum of the distribution is 88. This translates the same with the mean which lies between
the upper and the lower quartile. From this observation, we can say the age data is normally
distributed with a symmetric curve.
On the other hand, the histogram of the frequency distribution shows that the data is
normally distributed. A different case for the same data is depicted by the Q-Q normal plot

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which shows there is an element of normality for the data. This is true because of the forms a
distinct pattern along the normal curve. In conclusion, we can say that age data is normally
distributed despite the different histogram case.
Q.4
Quantitative variables have a numeric variable that describes their value while categorical
variables have no numeric variable to describe their characteristics. In description and analysis of
these two types of data is different. However, to statistically describe the characteristics of the
categorical variables, the data distribution is assigned numeric values which makes it easy for
analysis and description. Mostly, the normality for the quantitative data is described by the
normal curve while that for the categorical variables is described by the Q-Q plot (Hamzeh et al.,
2016).

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References
Bazavov, A., Ding, H. T., Hegde, P., Kaczmarek, O., Karsch, F., Laermann, E., & Schmidt, C.
(2017). Skewness and kurtosis of net baryon-number distributions at small values of the
baryon chemical potential. arXiv preprint arXiv:1708.04897.
Hamzeh, S., Naseri, A. A., AlaviPanah, S. K., Bartholomeus, H., & Herold, M. (2016).
Assessing the accuracy of hyperspectral and multispectral satellite imagery for
categorical and quantitative mapping of salinity stress in sugarcane fields. International
Journal of Applied
Earth Observation and Geoinformation, 52, 412-421.
Davis, L. J. (2016). Introduction: Disability, normality, and power. In The Disability Studies
Reader, Fifth Edition. Taylor and Francis.

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