**Normal Distributions**

You will define how normal distributions are used in biostatistics. This internet exercise assignment should be at least 450 words and no more than 1-1/2 pages long. You will need to cite within the text and have a reference section in APA format (current edition) as well as adhere to standard writing for graduate level.

You must attach a paper containing your submission to this assignment so it can be checked via Turnitin.

Step 1: You will define normal distributions from your textbook and/or various sources first. You are demonstrating that you understood the concept .You are going to define this in 2-3 sentences by using your textbook and/or other sources.

Step 2: You are going to pick an article that utilized the concept of normal distributions. You want to take a look at the following sites to select your article:

https://academic.oup.com/cid infectious diseases

Step 3: Thoroughly READ the article and TAKE notes.

Step 4: Complete the writing of your summary with a review of this article. In your assessment of this article, you MUST provide how the researchers used the statistical concept in their research by doing the following: 1) Explain the sample (i.e., population used), 2) Summarize the statistical analysis and what software or analysis package was used and 3) Evaluate and describe the results and conclusions in your own words. It is very important that you are able to paraphrase and apply critical thinking in each of these assignments. You MUST provide the statistics used in the research article and cannot simply state the researchers used the statistical concept. This can be done by providing the results or figures, graphs, tables, etc.

**Sample Paper**

**Normal Distributions**

Normal distribution refers to any representation of data graphically where the values are symmetrical about a central value, usually the mean. Alternative names for normal distribution are the Gaussian distributions and bell curves (Bhandari, 2020). While perfect normal distributions are rare in real life, they provide ideal models in which one can interpret statistical data.

**Selected Study**

Suntronwong et al. (2020) investigated the relationship between influenza infections and climatic elements in Bangkok. They collected data for nine years (January 2010 to December 2018) on both the weather patterns and the influenza incidence rate in Bangkok. The data set for the weather elements is robust, and hence it is sensible for the researchers to use the normal distribution to rationalize it.

**Sample Description**

The researchers obtained samples from 30852 nasal or throat swabs from patients if flu-like symptoms (fever, sore throat, and a cough). While the study received approval from the relevant institutional review board, the researchers did not need to obtain consent since they received the samples without names. However, they had information on the subjects’ age and gender. Additionally, the researchers obtained meteorological data from the Wolfram Alpha database for the corresponding month of each set of swab samples.

**Analysis**

The paper does not mention the analytical software that the researchers used. However, it describes the statistical analysis process, including computing the percentage of influenza-positive samples each month. The researchers also conducted a univariate analysis of weather elements (temperature, rainfall, and humidity) using the Mann-Whitney U test. Lastly, the researchers applied a normal distribution to a generalized linear model to compare the monthly weather variables and the influenza positivity rate. Thus, the analysis allowed the researchers to generate a year-long seasonal model to predict influenza cases.

**Results and Conclusion**

21.6% of the samples were positive for influenza, with type A constituting 19.85 and the B virus comprising the majority (80.2%). Figure 1 below, taken from the paper, summarizes the results graphically for the nine years. The peak positivity rate varies per year. However, the high rates in individual years tend to occur between May and September unless there is a surge in the incidence of H1N1 A virus variety in that year (which mainly occurs between January and March).

Figure 1: Monthly Influenza positivity rates from Jan 2010 to December 2018

Figure 2 then displays an aggregated positivity rate and how it interacts with temperature, relative humidity, and rainfall levels. Thus, the influenza incidence rate increases as rainfall, temperature, and the relative humidity rise.

Figure 2: Generalized model showing how influenza incidence rates vary according to climatic conditions

Therefore, while the study had some inherent limitations (such as using data from only one facility), it successfully demonstrated how the weather affects influenza activity. Further research may explain this trend. Hence, clinicians would be able to relate the weather conditions to epidemiological and pathophysiological factors and processes of influenza.

**References**

Bhandari, P. (2020, Oct. 23). Normal Distribution: Examples, Formulas, & Uses. https://www.scribbr.com/statistics/normal-distribution/

Suntronwong, N., Vichaiwattana, P., Klinfueng, S., Korkong, S., Thongmee, T., Vongpunsawad, S., & Poovorawan, Y. (2020). Climate factors influence seasonal influenza activity in Bangkok, Thailand. *PloS one, 15*(9), e0239729. https://doi.org/10.1371/journal.pone.0239729