Students retrieve necessary real time data and perform conversions to complete their OZONE TABLE. A minimum of six cities must be used.
OZONE DATA TABLE - Real Time
Data from August 3, 1999
| Latitude | Longitude | Latitude | Longitude | |||||||
| August 3, 1999 | degrees | minutes | degrees | minutes | decimal degrees | Dobson Units | Temperature | UV-Index | Weather | |
| Moscow | 55 | 45 | 37.00 | 37 | 55.75 | 37.62 | 299 | 76 | 5.6 | p/cloudy |
| Cairo | 30 | 0 | 31 | 17 | 30.00 | 31.28 | 290 | 95 | 11.2 | sunny |
| Miami | 25 | 46 | 80 | 12 | 25.77 | -80.20 | 312 | 89 | 11.4 | cloudy |
| Bangkok | 13 | 50 | 100 | 29 | 13.83 | 100.48 | 281 | 85 | 13.0 | t-storms |
| Quito | 0 | 7 | 78 | 28 | -0.12 | -78.47 | 267 | 78 | 14.6 | p/cloudy |
| Sydney | 33 | 55 | 151 | 17 | -33.92 | 151.28 | 338 | 62 | 3.6 | p/cloudy |
Students graph data to discover relationships that occur between variables. (These graphs were created with Microsoft Excel '97)
Graph #1
Apply a straight trend line.Express line as an algebraic equation.
Graph #2
Apply a polynomial trend line.Compare to Graph #3
What would this graph look like in January?
Graph #3
Apply a polynomial trend line.Compare to Graph #2
What would this graph look like in January?
Graph #4
Describe any apparent trends. These are not obvious trends but may be present.More data may be helpful.
Graph #5
Predict expected trends. Do you see your predictions in the graph.Can you explain outliers with your current weather data?
Predicting your UV-Index Hoboken, New Jersey (August 3, 1999)
Lat/Long = 40.73 North, 74.02 West
Ozone = 324 Dobson Units
UV-Index = -0.1428(Dobson units) + 52.418
UV-Index = 6.15
Real Time Data UV = 7
Return to Real Time Data Lessons
