1. Use a wall-size map of Ecuador to show the students the locations in the table or distribute individual maps to each of the students or groups of students. You might mention that Ecuador was selected to serve as the example country to demonstrate the relationship between elevation and temperature because it has many cities with vastly different elevations all located around the same latitude and location. This is very important because students should learn that the only way to confirm the influence that elevation has on temperature is to keep all of the other variables constant. Therefore, any differences in the temperature patterns can be attributed to elevation since it is the only difference in the locations.
   • NOTE: For the purposes of this activity, the effect that latitude will have on the temperature for each the locations is negligible because they are all within 2º of the equator.
2. After locating the cities, ask the students if they can make any predictions about the weather for any of the locations. You can organize the students in pairs or small groups so they can share and discuss their predictions with each other, however each student should be held responsible for answering each question.
   • As an optional activity, lead a whole class discussion after the pairs/small groups have answered the questions. This can be play an important part in assisting the students elaborate their thoughts.
3. Since these are real time weather readings, the weather stations for each of the locations may submit the current temperatures to the weather web site at different times during the day, and therefore you should only compare the high temperature readings for today's forecast.

Part 2: Analyze the Data
The effect that elevation has on temperature can be analyzed by using a scatter plot to graph the two measurements. Scatter plots demonstrate a trend in the data and are similar to line graphs in that they begin by plotting different data points. However, the difference is that each of the individual points are not connected together with a line but rather a trend line is added where approximately the same number of points occur below the line as above it.

1. The students can either use a spreadsheet program (recommended) or create a graph to manually plot the points. Students should label the x-axis in meters from from 0 to 7,000m and the y-axis in Celsius from -15ºC to 35ºC.
   • Sample temperature vs. elevation scatter plot
2. You may need to demonstrate how to add a linear trend line to the scatter plot if many of the students have never made one before. It is recommended that you do not use the same elevation temperature data and you should mention that a trend line will not cross every point and that they should not connect the dots but rather estimate where the line should fall so that approximately the same number of points below the line as above it.
   • When explaining what a trend line is, it will be helpful to mention that when most of the data points are on or close to the trend line, this generally means there is a close relationship between the data points. On the other hand, if the data is all over the graph and it is difficult to draw the trend line, this most likely means that there is little correlation between the two variables.
3. Students should be able to determine the approximate change in temperature for every increase of 1,000m in elevation based on the graph. Their answers will vary depending on the individual high temperatures for the day but should be within the range of four to seven ºC for every 1,000m in elevation.
4. The correlation coefficient corresponds to how much the different data points are correlate. For example, data points that have little or no correlation will have a correlation coefficient close to zero while data points that have a higher correlation will be closer to one. In this example, the lineal regression equation can be used to compute the approximate temperature of a particular elevation - value x.
   • NOTE: If students used a spreadsheet program, it should be able to automatically determine both the correlation coefficient and linear regression equation.
5. Students can either estimate the approximate temperatures at zero latitude for each of the elevations or compute the temperatures using the linear regression equation. As above, the answers will vary slightly depending on the high temperatures of the day.
6. &   7. Students should be able to calculate the temperature estimate however in both cases their calculation will not be equal to the real values for either Mt. Everest or the Bentley Subglacial Trench. The reason for this is that they are both located at different latitudes which has a significant effect on their temperatures. The purpose for these two questions is to provide the students an opportunity to apply their knowledge from the previous activity.