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So, now that I have my data, what do I do with it?

Means and Extremes

The methods and number of steps used in developing scientific knowledge may vary from one investigator to the next, but scientific methods usually involve the alternation of two types of activities, the observational and the explanatory. The work we have done so far has allowed us to make observations about ground-level ozone, but we have not yet participated in the explanatory part of science. At this point we need to continue to explore the data we have collected so far to increase the accuracy, the "grain", of our observations. The data set you downloaded does not really mean very much yet because it is just a bunch of numbers. We do not know whether this data is typical, high, low, or how it compares to "normal".
Statistics refers to a set of procedures and rules for reducing large sets of numbers in to manageable proportions and allowing us to take the next step and draw conclusions from our data. The purpose is to increase the meaning of our observations reflected in our data so we will employ descriptive statistics. The most common numerical way to look at data is by means and extremes. The mean is the sum of the data values divided by the number of data points. In everyday language, this is the average. For example, if you took ozone readings for seven days in a row and your readings were: 65, 75, 85, 45, 45, 45, 60 then to find the mean you would add the values together and divide by seven. The mean for this data set would be 53.6.

See if you can do a mean for the data that you have. What is the average ozone level for your site? Find the extremes in your data set. Look for the highest and the lowest ozone levels. How do the highs and lows compare to the average?

Construct a two-variable line graph ( ozone over date ) to graph the data from your site.

Using Geographic Information Systems to Look for Patterns

A geographic information system (GIS) is a computer-based tool for mapping and analyzing things that exist and events that happen on earth. The data that we have collected as a part of this project is well suited to GIS technology because it has a critical geographic dimension. GIS integrates common database operations such as query and statistical analysis with the unique visualization and geographic analysis benefits offered by maps. These abilities distinguish GIS from other types of analysis.
Mapmaking and geographic analysis are not new, but a GIS performs these tasks better and faster than do the old manual methods. And, before GIS technology, only a few people had the skills necessary to use geographic information to help with decision making and problem solving. A GIS stores information about the world as a collection of thematic layers that can be linked together by geography. This simple but extremely powerful and versatile many real-world problems from tracking delivery vehicles, to recording details of planning applications, to modeling global atmospheric circulation.

Geographic information systems work with two fundamentally different types of geographic models - the "vector" model and the "raster" model. In the vector model, information about points, lines, and polygons is encoded and stored as a collection of x,y coordinates. The location of a point feature, such as a bore hole, can be described by a single x,y coordinate. Linear features, such as roads and rivers, can be stored as a collection of point coordinates. Polygonal features, such as sales territories and river catchments, can be stored as a closed loop of coordinates.
The vector model is extremely useful for describing discrete features, but less useful for describing continuously varying features such as soil type or accessibility costs for hospitals. The raster model has evolved to model such continuous features. A raster image comprises a collection of grid cells rather like a scanned map or picture. Both the vector and raster models for storing geographic data have unique advantages and disadvantages. Modern GISs are able to handle both models.

I would like to work with the data in a map-based format
Ozone Badge Data and Milkweed.

Using Systems Thinking - Modeling to work with data.

Models are an important part of the explanatory part of science. Science is a practical study of what can be observed, and the prediction from that, of what will be observed. Models support moving beyond assimilating content to actually building understanding and effectively sharing this understanding with others. Using modeling software for analysis will build your capacity for, evaluating your models' congruence with reality and seeing complex interdependent relationships. Modeling is another tool of the practicing scientist. The STELLA software used by this project, uses a building block language that allows you to model the system of interest. The structure of virtually any system can be represented using just four icons! Sophisticated mathematics is not required to capture sophisticated relationships, as the STELLA software automatically creates the framework of equations needed to simulate the model. Once the model is constructed, simulations provide the opportunity to test the theories, observe results, and modify assumptions, thereby increasing your understanding of how things really work and how to make them work better. Can you create a model, using STELLA that shows how you believe ozone forms and enters the atmosphere?

As you explore your data a number of questions will no doubt come to mind. Many of them begin with "Why".......which is good because it means you are ready to really begin the explanatory part of science. This process begins with establishing and refining your questions as a research question . If you do not have much experience with this process the Guided Research will help you begin working in the explanatory part of science. If you ready to jump in on your own go ahead and begin your work. If you need some helpful suggestions for how to proceed, or if you are ready to Publish My Research, this area will help you share the information you develop.

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ground level ozone GLO EPA Ozone O3 atmospheric gas greenhouse green house gas ozone