Researchers Collected Data On The Ages Of People Who Died

Researchers Collected Data On The Ages Of People Who Died Due To Ca

Researchers collected data on the ages of people who died due to cardiac arrest over a 1-year period. The distribution of the ages is normal, and the mean age was 60, with a standard deviation of 6.

A) What percentage of people who died was between the ages of 57 and 64?

B) What percentage of people who died were older than 69?

A) To determine the percentage of individuals aged between 57 and 64, we use the properties of the normal distribution. The mean (μ) is 60, and the standard deviation (σ) is 6. First, we convert the ages to z-scores:

For age 57:

z = (57 - 60) / 6 = -0.5

For age 64:

z = (64 - 60) / 6 = 0.6667

Using standard normal distribution tables or a calculator, the cumulative probability for z = -0.5 is approximately 0.3085, and for z = 0.6667, it is approximately 0.7475. The proportion of individuals between these z-scores is:

0.7475 - 0.3085 = 0.4390

Converting to percentage:

0.4390 × 100 ≈ 43.9%

Therefore, approximately 43.9% of people who died were between 57 and 64 years old.

B) To find the percentage of individuals older than 69, we calculate the z-score for age 69:

z = (69 - 60) / 6 = 1.5

The cumulative probability for z = 1.5 is approximately 0.9332. The probability of being older than 69 is:

1 - 0.9332 = 0.0668

Converted to percentage:

0.0668 × 100 ≈ 6.68%

Thus, about 6.68% of individuals who died were older than 69.

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The above analysis demonstrates how to apply basic normal distribution principles to real-world data, facilitating understanding of age-related mortality patterns associated with cardiac arrest.

Paper For Above instruction

The study of age distribution in mortality data provides vital insights into public health patterns and risks associated with specific causes of death, such as cardiac arrest. The initial dataset indicating a mean age of 60 years with a standard deviation of 6 years offers a foundation for statistical analysis, especially in understanding the proportion of deaths within specific age brackets. Employing the properties of the normal distribution allows researchers and health officials to quantify the likelihood of death within certain age ranges, thereby aiding in targeted interventions and resource allocation.

Calculating the percentage of individuals aged between 57 and 64 years illustrates how most mortality in cardiac arrest occurs around middle age. The use of z-scores and standard normal distribution tables deciphers the proportion of the population falling within this age span. This understanding can direct preventive strategies toward populations at higher risk within this age group, especially considering the 43.9% representation. It indicates that nearly half of the cardiac arrest deaths occur in this middle-aged bracket, emphasizing the importance of health interventions in this demographic.

Furthermore, examining the percentage of individuals older than 69 years reveals demographic patterns vital for healthcare planning. The calculation shows that approximately 6.68% of deaths occur in the older age group, highlighting that while mortality risk increases with age, a smaller proportion of such deaths belong to this group relative to the middle-aged population. This data helps in allocating emergency services and cardiac health resources more effectively, especially as the population ages.

The implications of this distribution are significant in developing age-specific health policies and identifying high-risk groups for proactive health management. It also underscores the importance of continuous data collection and analysis in understanding evolving health trends and adjusting intervention strategies accordingly. Such statistical insights are invaluable for medical stakeholders aiming to reduce mortality rates and improve quality of life across different demographics.

In summary, statistical analysis rooted in the normal distribution enhances our understanding of mortality data, fostering informed decision-making in public health sectors. By quantifying the proportions of deaths within specific age ranges, health officials can better allocate resources and design preventive strategies tailored to vulnerable populations. Consequently, the integration of statistical tools in health data analysis continues to be a cornerstone of modern epidemiology and healthcare planning.

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