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   Particulate Monitoring Guided Research: Data Analysis     
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  Research Question
  Background Info
  Research Methodologies
  Data Submission
  Results of Study
  Data Analysis
  Further Research
  Research Values



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

Visualizing Data

Graphs are one way to visualize data and to help the researcher look for patterns. A graph is used to show the relationships of data collected from the expreiment. Graphs must be constructed accurately and accoring to accepted rules. Usually, a graph shows the relationship between two kinds of data. These data are called variables. In this research the variables are day time high temperature and the level of ground-level ozone. These are two types of variables.

The independent variable is data that influences the outcome of the experiment. Often the experimenter has control over this variable. Time is a very common independent variable. This data is plotted in the horizontal axis, x axis. In our research we are going to explore the relationship of temperature and ozone. Day time high temperature is our independent variable.

The dependent variable depends on the conditions of the investigation, and frequently on the independent variable. The dependent variable is sometimes refered to as the outcome variable. The dependent data is plotted on the vertical axis, the y axis. In our research the level of ground-level ozone is our dependent variable.

Remeber when you make graphs;
1) Select scales for the horizontal and vertical axes which will reflect the precision of the measurements. Display the dat in a proportional way. Remember, each square on the graph is equal to an assigned quantity but the scale of either axis may be changed if the graph is too compact or needs to be expanded.
2) It is important to label both the vertical and horizontal axes with the variables being graphed and also to indicated the units being used.

Spreadsheets will offer you a graphing options for your data but it is very important that you understand the graph you have made and that the graph accurately represents your data.

Construct a bar graph of the data you have downloaded.
The following is a bar graph of ground-level ozone and day time high temperature.

While bar graphs are interesting and a good way to visualize data, they have some problems. This graph does not allow us to really explore the relationship between temperature and ozone.

Construct a two-variable line graph of the data you have downloaded.
Line graphs show the relationship between two kinds of data in which the independent variable is continous. After the proper points are plotted on the graph, they should be connected by a line. To line more about graphing and how to make various kinds of graphs, the DIGSTATS site should be helpful. The following is a line graph showing ozone level over and daily high temperature and represents the same data presented in the bar graph.

Making a Box Plot

John Tukey has developed which gives greater prominence to the dispersion, the spread of the data. This method is known as a boxplot, or a box-and-whisker plot. To learn how to construct a boxplot. The following is a boxplot of the data represented in the bar graph and in the line graph.

Hypothesis Testing

This research was set up with two hypotheses. The null hypothesis, H0, states that events will not change, not differ and alternative hypothesis, H1, states that events will differ from some baseline standard or control conditions. This change (dependent variable) predicted by H1 will be due to the occurrence of an experimentally controlled variable (independent variable).
The hypothesis for this Guided Research were:

H0: There is no measurable relationship between the high temperature of the day and the level of ground-level ozone.
H1: There is a measurable relationship between the high temperature of the day and the level of ground-level ozone. Actual hypothesis testing consists of several stages:

  1. We want to test the alternative hypothesis, that there is a measurable relationship between the high temperature of the day and the level of ground-level ozone. This is our research hypothesis.
  2. We obtained a random sample of levels of ground-level ozone and day time high temperatures.
  3. We set up the null hyupothesis
  4. We then obtained a sampling distirbution of the mean under the assumption that the null hypothesis is true.
  5. Given the sampling distribution, we calculated the probablility of a mean at least as large as our actual sample mean.
  6. On the basis of that probablility, we make a decision to either reject or fail to reject the null hypothesis.
If you are not familar with satistical reasoning you can get more specifics on how hypothesis testing is done and what test to use at DIGSTATS. If you are familar with statistics, use the results of your hyothesis testing in your conclusion.

Using Geographic Information Systems for Analysis

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.

I would like to work with the data in a map-based format.

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