What is "k-means clustering," and how might it help healthcare administration leaders in their health services organization? One of the more common methods of clustering is the use of k-means, where "k" is the number of clusters that are meant to describe the population of interest. For example, what if you were interested in segmenting patients based on satisfaction levels? K-means clustering could be used in the same way to help describe this given population. This technique might assist leaders in determining what characteristics need to be considered in order to study crime and unemployment. In striving to address the problem, k-means clustering is an effective tool for leadership to consider. For this Assignment, review the resources for this week, and examine the k-means clustering approach. Reflect on how you might apply this approach to an issue or challenge in a health services organization. Then, complete the problems for the Assignment. The Assignment: (3–5 pages) Complete Problem 21 on page 938 of your course text. Note: You will need to complete a logistic regression, as well as a k-means cluster analysis, using SPSS. By Day 7 Submit your answers and embedded analysis as a Microsoft Word management report.

Name an example of a real-world data set, not yet mentioned in the course, where you would typically expect to see a normal distribution. Be sure to describe what the labels would be for the horizontal and vertical axis of the display. Then explain why you would expect a normal distribution. What range of data values would you expect to represent 1 standard deviation from the mean? (15 points; 10 for your answer, 5 for response to others' comments.)This lesson asked you to investigate what it means for data to fall within 1, 2, and 3 standard deviations from the mean in a variety of normal distribution curves. Imagine that you hold a position on your city council and are in charge of creating new programs to benefit members of your community. In some cases, you must also decide which parts of the population are eligible for enrollment in a given program. How might the concept of standard deviations from the mean be interpreted in your decisions? For example, is it fair to make most programs only available to populations within 1 standard deviation from the mean? Can you imagine cases where you might want to create new programs for people who fall toward the extreme ends of the distribution? Give your opinion as well as some examples. (15 points; 10 for your answer, 5 for response to others' comments.)