ststics week 5 final

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Chamberlain University College of Nursing *

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225

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Statistics

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Jan 9, 2024

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docx

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2

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.                      The normal distribution is defined by the mean and standard deviation of a quantitative/numerical data set. According to (Holmes, et al., 2017) "The mean determines the curve's position on the x-axis of a graph, and the standard deviation determines the height of the curve on the y-axis" (Holmes, Illowsky, & Dean (2018) explains that Statistical data is essential to physicians, and the knowledge of statistics is critical for research and also for recognizing and explaining information related to the practice of medical science. One of the statistical data elements is normality, which is mainly described by a distribution that resembles a symmetrical bell curve. Share of the data will drop to the mean's left, and half will fall to the right.  Two examples of variables that would have a normal distribution are systolic blood pressure and height. Systolic blood pressure numbers are normally distributed, especially since the mean and standard deviation are normally within the same range for a selected age group. The systolic blood pressure of 19-year-old women is typically distributed; for example, the mean is about 120 mmHg and the standard deviation of 12 mmHg (Sarkar, 2014; Rivera et al., 2014). Most healthy adults have systolic blood pressure between 120 and 125 mmHg. However, blood pressure readings are changed by a variety of stress, diet, and exercise frequency. Height would be another example of a variable that matches a normal distribution. If we measured the height of 100 of 30-year-old women, created a histogram and plotted height on the x-axis, and the frequency where each of the heights happened on the y-axis, we would get a normal distribution.Height is one of the most common variables with normal distribution since not most people are very tall, or most o they are very short. Height has an average height zone, and less of the population lies outside the zone, such as Short or much taller than others. Also, the height variance is not that large because the difference within individuals' height is small.Height reflects a normal distribution because most of the data values tend to center around the mean. References Holmes, A., Illowsky, B. & Dean, S. (2018).  Introductory business statistics.  Houston, TX: OpenStax Rivera, A. L., Estanol, B., Senties-Madrid, H., Fossion, R., Toledo-Roy, J. C., Mendoza-Temis, J., ... & Frank, A. (2016). Heart rate and systolic blood pressure variability in the time domain in patients with recent and long-standing diabetes mellitus.  PloS one 11 (2), e0148378. Sarkar, S. (2014). Understanding data for medical statistics.  International Journal of Advanced Medical and Health Research 1 (1), 30.  
Few of the vriables which follow normal distribution are height, haemoglobin concentration, haematocrits. Let's just consider height and haemoglobin concentration for now. Height is the most general variable with normal distribution as it is not like most people are very tall or most o them are very short. We have an average height zone and few of the population lies outside this zone short or much taller than others. And the variance of the height is not that large because the difference between the height of individuals is small. Next the haemoglobin concentration is also something which has an average values about which most of the data is concentrated. But there are cases when person suffering from a disease has high or low haemoglobin concentration. But as its measured in ppm, so the variance is relatively larger. So, standard deviation of haemoglobin concentration is lasrger than the standard deviation of heights.
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