You are  currently working at NCLEX Memorial Hospital in the  Infectious Diseases Unit. Over the past few days, you have noticed an  increase in patients admitted with a particular infectious disease. You  believe that the ages of these patients play a critical role in the  method used to treat the patients. You decide to speak to your manager,  and together you work to use statistical analysis to look more closely  at the ages of these patients. You do some research and put together a of the data that contains the following information: Client number Infection disease status Age of the patient You are to put together a PowerPoint presentation that explains the  analysis of your findings which you will submit to your manager. The  presentation should contain all components of your  findings. For  review, the components of the report should include: Brief overview of the scenario and variables in the data set Discussion, calculation, and interpretation of the mean, median, mode, range, standard deviation, and variance Discussion, construction, and interpretation of the 95% confidence interval Explanation of the full hypothesis test Conclusion The  calculations should be performed in your spreadsheet that you  will also submit  to your manager. You can find additional information  on what to add to your PowerPoint  presentation in this . Use the  questions in the worksheet as your guide for the contents of your presentation. For your final deliverable, submit your PowerPoint presentation and  the Excel  workbook showing your work. Do

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Title: Analysis of Age as a Determinant in the Treatment of Infectious Diseases

Introduction:
In this presentation, we will analyze the ages of patients admitted with a specific infectious disease at NCLEX Memorial Hospital. We aim to determine if age plays a critical role in the method used to treat these patients. By conducting statistical analysis, we can gain insights into the relationship between age and treatment approach.

Variables in the Dataset:
Before delving into the analysis, let’s discuss the variables present in the dataset:

1. Client number: A unique identifier for each patient admitted.
2. Infection disease status: Indicates whether the patient has the specific infectious disease.
3. Age of the patient: The age of each patient at the time of admission.

Mean, Median, and Mode:
Let’s calculate and interpret the mean, median, and mode of the ages of the patients in our dataset. These measures will help us understand the central tendency of the age distribution.

The mean (μ) represents the average age. It is calculated by summing up all the ages and dividing by the total number of patients. The median represents the middle value when the ages are arranged in ascending order. The mode represents the age that occurs most frequently.

Range and Standard Deviation:
We will also calculate the range and standard deviation of the ages to further analyze the spread or dispersion within the age distribution.

The range is the difference between the maximum and minimum age values. It provides insight into the variability of ages in our dataset. The standard deviation (SD) quantifies the average distance between each age and the mean age. It indicates how much the ages deviate from the mean, providing a measure of dispersion.

95% Confidence Interval:
Next, let’s construct and interpret a 95% confidence interval for the mean age of patients with the infectious disease. The confidence interval will help us estimate the range within which the true population mean age lies, with a specified level of confidence.

Hypothesis Test:
To further investigate the impact of age on treatment methods, we will perform a hypothesis test. Our null hypothesis (H0) assumes no difference in treatment approaches based on age. The alternative hypothesis (Ha) states that age influences the method used to treat patients.

We will analyze the data to determine whether there is sufficient evidence to reject the null hypothesis in favor of the alternative hypothesis. The results of the hypothesis test will provide insights into the significance of age as a determinant in the treatment of infectious diseases.

Conclusion:
In conclusion, our analysis of the ages of patients admitted with the infectious disease at NCLEX Memorial Hospital provides valuable insights for treatment strategies. By calculating measures of central tendency, dispersion, and constructing a confidence interval, we can better understand the age distribution and its impact on treatment selection. Additionally, the hypothesis test allows us to determine whether age significantly influences treatment decisions. These findings contribute to evidence-based medical decision-making, enabling healthcare professionals to optimize patient care.

Please note that the calculations and further details can be found in the accompanying Excel workbook.