# Analyzing And Mapping Historic Weather Data Lab Analyzing and Mapping Historic Weather Data Lab (30 points)

For this lab, you will analyze weather data for three cities over a 50-year period. You will use Microsoft Excel to create a spreadsheet and graph. Note: If you do not have Microsoft Office, click here to access OPEN OFFICE software. To learn how to use Microsoft Excel, click here or here.

1. Select three cities with available weather data back to the year 1965. Remember that you will need to cite your data sources. You will be collecting weather data for five data points:
1. The average annual rainfall (inches)
2. The average annual snowfall (inches)
3. The average monthly high temperature
4. The average monthly low temperature
5. Cooling degree days
2. For each city, collect the weather data listed in Step 1, and enter the data into the Excel spreadsheet.
3. Plot your data for each city using the Excel Chart Wizard, choosing the x-y scatter plot. Include all of the weather data that you collected for each city. Save the spreadsheet with your plot to your computer.

Note: To learn how to create a chart (graph) in Excel, click here.

4. Analyze the data by calculating the standard error. The standard error is a statistical method used to determine the statistical accuracy of the data compared to the mean of the data. This technique is often used to analyze changes in data.

Note: To learn how to perform statistical analyses using Excel, click here . To learn how to calculate the standard error of your data in Excel, click here or click here. (Note that these examples are for Excel 2013.)

5. Add the error bars to your graph for each of the five data points. Standard error bars can indicate if changes in your data are significant.

Note: To learn how to do this in Excel, click here. (This example is for Excel 2013.) For older versions of Excel, click here.

• Start with Average Monthly High Temperature. Look at the standard error bars of the data for each of your cities. For our purposes, we will consider the difference in the data to be significant if the vertical separation between the error bars is greater than one-half the height of the error bar.  [checkout]