Names Of Infectious Disease Patients Aged 1 To 65 ✓ Solved

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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.

The data set consists of 60 patients that have the infectious disease with ages ranging from 35 years of age to 76 years of age for NCLEX Memorial Hospital.

Requirements: 1) Answer the questions below in a PowerPoint presentation. 2) Include only the summary calculations in your slides (not formulas). 3) Show calculations in your Excel spreadsheet (include formulas and do not round the numbers).

Slide 1: Title: Introduce your scenario and data set.

Slide 2: Provide a brief overview of the scenario you are given above and the data set that you will be analyzing.

Slide 3: Classify the variables in your data set.

  • Which variables are quantitative/qualitative?
  • Which variables are discrete/continuous?
  • Describe the level of measurement for each variable included in the data set (nominal, ordinal, interval, ratio)
  • Discuss the importance of the Measures of Center and the Measures of Variation.

Slide 4: What are the measures of center and why are they important?

Slide 5: What are the measures of variation and why are they important?

Slide 6: Calculate the measures of center and measures of variation. Interpret your results in context of the scenario.

  • Mean
  • Median
  • Mode
  • Midrange
  • Range
  • Variance
  • Standard deviation

Slide 7: Discuss the importance of constructing confidence intervals for the population mean by answering these questions:

  • What are confidence intervals?
  • What is a point estimate?
  • What is the best point estimate for the population mean? Explain.
  • Why do we need confidence intervals?

Slide 8: Construct a 95% confidence interval for the population mean. Assume that your data is normally distributed and σ is unknown. Include a statement that correctly interprets the confidence interval in context of the scenario.

Slide 9: Perform the following hypothesis test:

  • Original Claim: The average age of all patients admitted to the hospital with infectious diseases is less than 65 years of age.
  • Test the claim using α = 0.05 and assume your data is normally distributed and σ is unknown.

Slide 9: Write the null and alternative hypothesis symbolically and identify which hypothesis is the claim.

Slide 9: Is the test two-tailed, left-tailed, or right-tailed? Explain.

Slide 10: Which test statistic will you use for your hypothesis test; z-test or t-test? Explain.

Slide 10: What is the value of the test-statistic? What is the p-value? What is the critical value?

Slide 11: What is your decision; reject the null hypothesis, or do not reject the null hypothesis? Explain why you made your decision, including the results for your p-value and the critical value.

Slide 11: State the final conclusion in nontechnical terms.

Slide 12: Conclude by recapping your ideas by summarizing the information presented in context of the scenario. Include the mean, standard deviation, confidence interval with interpretation, and results of the hypothesis test. What conclusions, if any, do you believe you can draw as a result of your study? What did you learn from the project about the population based on this sample? What did you learn about the specific statistical tests you conducted?

Paper For Above Instructions

The analysis of patient data related to infectious diseases at NCLEX Memorial Hospital aims to unpack the implications of age on treatment methods for numerous patients within a specific age range. This presentation will detail the dataset, classify variables, summarize statistical findings, and interpret results that can guide treatment decisions.

Overview of the Dataset

The dataset consists of 60 patients diagnosed with an infectious disease, whose ages range from 35 to 76 years. This dataset is pivotal in understanding how age influences treatment strategies for infectious diseases. Recognizing patterns within this demographic can inform assessment protocols and modify care approaches based on statistical insights.

Classification of Variables

The primary variables in this dataset include:

  • Client number: Nominal, qualitative, and discrete. This serves merely as an identifier.
  • Infection disease status: Nominal, qualitative, and discrete, indicating the presence or absence of disease.
  • Age: Ratio, quantitative, and continuous since it can take on any value within the defined range.

Understanding these variables and their classifications is crucial for calculating measures of center and variation, which help identify trends crucial for medical practices.

Measures of Center

Measures of center, including mean, median, mode, and midrange, are fundamental for summarizing a dataset. The mean provides an average age that can signal treatment requirements. The median indicates the midpoint age, ensuring that skewed distributions do not misrepresent central tendencies. The mode reveals the most common age among patients, possibly correlating with specific treatment protocols.

Measures of Variation

Measures of variation such as range, variance, and standard deviation provide insight into the dispersion of age in the dataset. A narrow range and lower standard deviation suggest that ages cluster closely around the mean, whereas significant spread might indicate varied treatment needs based on age differences.

Calculations of Center and Variation

Calculating the following offers a clearer understanding of patient age demographics:

  • Mean: The average age adds significant insight into patient management.
  • Median: Reflects the central age in this cohort.
  • Mode: Determines the age that is most frequently observed.
  • Midrange: Offers a simplified average of maximum and minimum ages.
  • Range: The difference between the oldest and youngest patient.
  • Variance: Establishes how much ages deviate from the mean.
  • Standard deviation: Provides insights into the distribution of patient ages, informing treatment strategies.

Importance of Confidence Intervals

Confidence intervals offer a statistical range within which the population mean likely resides, facilitating informed treatment decisions. A point estimate represents the single best estimate of the population mean, primarily derived from sample data. The necessity of confidence intervals lies in their role in providing reliability and uncertainty margins for estimates, informing healthcare professionals in practice.

Constructing the 95% Confidence Interval

Assuming a normal distribution, the calculation of the 95% confidence interval would provide a statistical safeguard in estimating the average age of patients diagnosed with infectious diseases, placing the forecast in a statistically sound context.

Hypothesis Testing

The original claim asserts that the average age of patients admitted with infectious diseases is less than 65 years. Our hypothesis test, conducted with an alpha level of 0.05, will confirm or deny this claim.

  • Null Hypothesis (H0): The average age is greater than or equal to 65 years.
  • Alternative Hypothesis (H1): The average age is less than 65 years (the claim).

This is a left-tailed test, as we seek to determine if the mean age falls below a specified value.

Applying either a z-test or t-test depends on sample size and population variance. Given the sample size is below 30 and we assume variance is unknown, a t-test is appropriate. The resulting test statistic, p-value, and critical value will steer the conclusion towards either rejecting or not rejecting the null hypothesis.

Conclusion and Implications

In summarizing the findings, the presentation will conclude with interpretations of the mean, standard deviation, and confidence intervals relative to the hypothesis tests. Ultimately, determining if the average age of patients with infectious diseases is indeed below 65 years will guide treatment protocols and highlight necessary adjustments for patient care measures in the Infectious Diseases Unit.

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