Statistical Reasoning for the Health Sciences
Graphing and Describing Data in Everyday Life
Required Resources
Read/review the following resources for this activity:
In your reference for this assignment, be sure to includeboth your text/class materials AND your outside reading(s).
Instructions
Suppose that you have two sets of data to work with. Thefirst set is a list of all the injuries that were seen in a clinic in a month’stime. The second set contains data on the number of minutes that each patientspent in the waiting room of a doctor’s office. You can make assumptions aboutother information or variables that are included in each data set.
For each data set, propose your idea of how best torepresent the key information. To organize your data would you choose to use afrequency table, a cumulative frequency table, or a relative frequency table?Why?
What type of graph would you use to display the organizeddata from each frequency distribution? What would be shown on each of the axesfor each graph?
Now that we have data, we need tools to let us interpret it.A list of 100 numbers doesn’t tell anyone anything. Descriptive statisticsgives us the tools that we need to condense a huge volume of data into a singlestatement. Sometimes this will be a number, sometimes it will be a picture.This is the point in the process where you become a storyteller. Don’t justcalculate a number or create a graph. Ask yourself questions like what doesthis number tell me about the data or what does this graph show me about thesample?
Supplemental Materials / Lectures
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Lecture 1 |
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Now that we’ve seen how to summarize data visually, we’re going to change direction and look at ways to do it numerically – starting with methods for locating the “center” of a data set. |
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Lecture 2 |
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The standard deviation can be interpreted geometrically as the distance a set of data values is from the mean of the data set. It also has a relationship with the range that we’ll use later as a way to get an approximate value for it. |
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Lecture 3 |
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Another way to think about the position of a data value, that we’ve actually talked about before, is a z-score. This measures how far a value is from the mean, i.e. the center of the data, in terms of standard deviations. |
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Lecture 4 |
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The standard deviation can be interpreted geometrically as the distance a set of data values is from the mean of the data set. It also has a relationship with the range that we’ll use later as a way to get an approximate value for it. |
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Lecture 5 |
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You won’t always have access to the data, even for a sample. If all you have, for example, is a frequency distribution then it’s still possible to get an approximation of both the mean and the standard deviation. |
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Lecture 6 |
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Now that we’ve covered center and spread, we’re going to drill down to an individual data value and think about how we can describe its position within the larger data set. |
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Lecture 7 |
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Another way to think about the position of a data value, that we’ve actually talked about before, is a z-score. This measures how far a value is from the mean, i.e. the center of the data, in terms of standard deviations. |
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Lecture 8 |
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When we first started talking about data, we talked briefly about the idea of an outlier – a value that, in some sense, is significantly separated from all the other values. Percentiles and z-scores are tools that we can use to make that idea of “significantly separated” precise. |
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Lecture 9 |
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In this last lecture in our discussion of summarizing data, we’re going to look at something called a box plot as a way to summarize the summaries, i.e. to bring multiple numeric measurements together in a single graph. |
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