Calculating & Analyzing Healthcare Data

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Calculating & Analyzing Healthcare Data

(Calculating & Analyzing Healthcare Data)

Healthcare Statistics

  1. In this problem, you will calculate mean values for four independent samples and then explain the observed differences (please read the entire problem before beginning). Using the SWC data (SWC tab) in dataset DS2.xls, do the following:
    1. Select a 10% random sample of all cases and calculate the Mean for the IMR (infant mortality rate) for this sample. Record the mean. (2 points)
    2. Select a second 10% random sample and calculate the Mean for the IMR for this sample. Record the mean. (2 points)
    3. Select a third 10% random sample and calculate the Mean for the IMR for this sample. Record the mean. (2 points)
    4. Select a fourth 10% random sample and calculate the Mean for the IMR for this sample. Record the mean. (2 points)
    5. Create a table that displays the four means calculated above in parts a-d. Calculate the average of the four means (Mean-1, Mean-2, Mean-3, Mean-4) and add this figure to the table. Describe the mean values calculated and offer an explanation for any differences observed. That is, explain why the four means calculated in parts a-d are not exactly the same. Include a comment about the “fifth mean” (the average of the means calculated in parts a-d). (2 points)
  1. In the next two problems, you will calculate frequency distributions and express them in different kinds of charts. Using the Hospital Charges data in dataset DS2.xls, do the following:
    1. Compute the minimum and maximum age for the cases in the spreadsheet. (1 point)
    2. Create a frequency distribution of age using the following categories of age (<50, 50-64, 65-79, 80+). Compute percent and cumulative percent for each age category. (4 points)
    3. Label all elements in the frequency table. (2 points)
    4. Create a column chart of age (showing the number of cases in each age category). (1 point)
    5. Create a line chart of age (showing the number of cases in each age category). (1 point)
    6. Create a pie chart of age (showing the percent of cases in each age category). (1 point)
  1. Using Late Delivery data in dataset DS2.xls , do the following:
    1. Use the pivot table to create a frequency distribution for the reasons for the late delivery of the meal. (4 points)
    2. Sort the frequency distribution so that the reason with the most occurrences is first, the second next, and so on, and create a column chart showing the reasons from most to least. (3 points)
    3. Compute the cumulative frequency (%) for the data in (a) and construct a Pareto chart of the result. That is, create a line chart for cumulative frequency (%) and add this to the chart created in part 6.b. (3 points)

(Calculating & Analyzing Healthcare Data)

Responce.

Step 1: Calculate Means for Four 10% Random Samples of IMR (Infant Mortality Rate)

  1. Select a 10% random sample from the SWC data in DS2.xls.
    • In a tool like Excel or statistical software, randomly select 10% of the cases from the dataset. For example, if there are 1000 rows of data, you would select 100 random rows.
  2. Calculate the Mean for the IMR for the first sample.
    • Use the formula for the mean: Mean=∑IMRNumber of cases in the sample\text{Mean} = \frac{\sum \text{IMR}}{\text{Number of cases in the sample}}
    • Record this mean.
  3. Repeat the process for the second, third, and fourth random samples.
    • For each sample, select a different random set of 10% of cases and calculate the mean for the IMR.
  4. Create a Table:
    • The table should display the four means calculated. Below the four individual means, calculate the overall average of the four means.
    • The table might look like this:
    Sample Number Mean of IMR
    Mean-1 [Calculated Mean 1]
    Mean-2 [Calculated Mean 2]
    Mean-3 [Calculated Mean 3]
    Mean-4 [Calculated Mean 4]
    Average [Average of Means]
  5. Explanation of the Mean Values and Differences:
    • The four means are not exactly the same due to the nature of random sampling. Each sample is likely to have slight variations in the IMR values, which can affect the calculated mean. Random sampling can lead to differences in the subset of data selected, causing fluctuations in the results.

Step 2: Hospital Charges Data Analysis

  1. Compute the Minimum and Maximum Age:

Using the Hospital Charges data, find the minimum and maximum values for the age variable. This can be done using Excel functions like =MIN() and =MAX().

  1. Create a Frequency Distribution of Age:
    • The categories for age are:
      • <50
      • 50-64
      • 65-79
      • 80+
    • Count how many cases fall into each category and calculate the percentage and cumulative percentage. The formula for percent for each category is: Percent=Number of cases in categoryTotal number of cases×100\text{Percent} = \frac{\text{Number of cases in category}}{\text{Total number of cases}} \times 100
    • Cumulative percent can be calculated by adding the percentages progressively.

    Example frequency table:

    Age Category Frequency Percent Cumulative Percent
    <50 [Count] [Percent] [Cumulative Percent]
    50-64 [Count] [Percent] [Cumulative Percent]
    65-79 [Count] [Percent] [Cumulative Percent]
    80+ [Count] [Percent] [Cumulative Percent]
  2. Create Charts:
    • Column Chart: Display the number of cases in each age category.
    • Line Chart: Display the number of cases in each age category (useful for trends over categories).
    • Pie Chart: Show the percentage distribution of cases in each age category.

Step 3: Late Delivery Data Analysis

  1. Create a Pivot Table for Late Delivery Reasons:
    • In Excel, use the Pivot Table feature to group the reasons for late delivery and display the frequency of each reason.
  2. Sort the Frequency Distribution:
    • Sort the reasons in descending order by frequency (most occurrences first). This can be done in Excel by sorting the Pivot Table.
  3. Cumulative Frequency and Pareto Chart:
    • Compute the cumulative frequency percentage for the reasons.
    • Use a line chart to show the cumulative frequency percentages and combine it with the column chart of frequencies, creating a Pareto chart (a combination of a bar chart and a line chart).

    Example Pareto Chart Structure:

    • The bar chart will show frequencies for each reason (from most to least).
    • The line chart will overlay the cumulative percentage of these frequencies.

Summary:

For each part of this assignment, you need to follow the steps above to calculate the means, create frequency distributions, and build appropriate charts to visualize the data. Tools like Excel or any statistical software (such as SPSS, R, or Python) will be helpful to compute the values and generate the charts.

 
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