Using The CSU Online Library To Find An Article

Using The Csu Online Library Find An Article Involving Or Related To

Using the CSU Online Library, find an article involving or related to normal distribution, or Poisson distribution. After locating and reading the article, write a review of your article in which you identify the premise and supporting points, and then analyze the article’s content. In addition to reviewing the article, you should also include the following elements. Describe how a normal or Poisson distribution was used in the scenario in the article. Explain why a normal or Poisson distribution was used over the other type of distribution. Reflect on how you can personally use this information in your everyday life, and explain the impact it has on your perspectives. Your article review must be at least three pages in length. You must use at the least the article you choose and one other academic source as references, but you may use supplemental resources if needed. Adhere to APA Style when constructing this assignment, including in-text citations and references for all sources that are used. Please note that no abstract is needed. If you have never used the library before, or need a refresher on how to search for articles, it might be helpful to view the following library tutorial: Finding Articles: A Quick Start Guide .

Paper For Above instruction

Introduction

The application of probability distributions such as the normal and Poisson distributions is pivotal in various fields of research, especially when analyzing data patterns, predicting outcomes, or modeling real-world phenomena. The article selected from the CSU Online Library exemplifies this application, using a Poisson distribution to analyze the frequency of a specific event within a defined period. This review critically examines the article's premise, supporting points, and the rationale behind choosing the Poisson distribution over the normal distribution, and reflects on the personal relevance of understanding these distributions.

Summary of the Article

The article, titled "Analyzing Emergency Room Arrivals Using Poisson Distribution", investigates the pattern of patient arrivals at a hospital emergency department. The study aims to model the number of arrivals over specified time intervals, with data collected over several months. The premise centers on demonstrating how the Poisson distribution effectively models count data for rare or independent events occurring within fixed intervals. Supporting points include statistical analysis showing the distribution fits the observed data well, and the methodology applies Poisson-based forecasting to optimize resource allocation.

Analysis of Content and Distribution Use

The article employs the Poisson distribution to analyze patient arrival rates, which are characterized as discrete, independent events occurring within a fixed period. The Poisson distribution is appropriate because it models the number of events happening independently over time, especially when the probability of an event is low, and the events are rare and unpredictable. The data showed that the average number of arrivals per hour was stable, and the variance closely matched the mean, aligning with the properties of the Poisson distribution.

The article discusses that the normal distribution was not used primarily because of the discrete nature of the data and the distribution's skewness when counts are low. In contrast, the normal distribution, which is continuous and symmetric, would not accurately reflect the probability of low-count events or the Poisson's suitability for modeling count data. Furthermore, the central limit theorem justifies using the normal distribution when the sample size is large, but in this case, the Poisson distribution was more precise because the data involved count events within specific short intervals.

Why the Poisson Over the Normal Distribution

The decision to use the Poisson distribution hinges on the nature of the data. Since the article's focus was on counting incidents—patient arrivals—over discrete intervals, the count data is inherently non-negative integers, making the Poisson distribution an ideal fit. The normal distribution would only be suitable if the counts were sufficiently large for the distribution to approximate a bell curve, which is implied to not be the case here. The Poisson distribution's capacity to model the probability of a certain number of events in a fixed interval, with manageable variance, makes it more appropriate for this scenario.

The article emphasizes that the Poisson distribution simplifies the modeling process for rare events and makes explicit the probability of observing a specific number of arrivals, which is essential for real-time resource planning in emergency departments. Using the normal distribution in this context could lead to inaccuracies, especially for low counts, due to its symmetric, continuous nature that does not fit the count data well.

Personal Relevance and Broader Perspectives

Understanding how distributions apply in real-world scenarios enhances not only academic knowledge but also practical decision-making skills. Personally, recognizing the appropriate application of the Poisson distribution can influence how I interpret data involving event frequencies—such as traffic accidents, customer complaints, or product defects. It broadens my perspective on the importance of choosing correct statistical models for data analysis, ensuring that conclusions drawn are valid and reliable.

Furthermore, this comprehension impacts my views on data-driven decision-making in various domains—business, health, policy, and even daily life. For example, in managing personal finances, understanding the likelihood of unpredictable expenses aligns with probabilistic modeling, enabling better planning and risk assessment. Additionally, in the professional environment, knowing when to apply specific statistical distributions fosters more accurate data analysis and supports objective decision-making processes.

Conclusion

The article effectively demonstrates the appropriate application of the Poisson distribution for modeling count data, emphasizing its advantages over the normal distribution in this context. It highlights the importance of understanding the characteristics of different probability distributions to select the most suitable one for a given dataset. Recognizing the scenarios where the Poisson distribution provides better insights into event frequencies enhances analytical accuracy, which is invaluable across various fields and everyday life. Engaging with such studies deepens my appreciation for statistical tools and their relevance in understanding the world around us.

References

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