complete both the CO2 activity and O3 activity doc using the excel data sheet
CO2 Activity Worksheet Background Information: This data was collected in Paonia, CO, one data set from January and one from April. Both data sets include four days of minute-averaged data—in other words, each data point is the average of all data recorded during that minute. 1. Develop a hypothesis that explains how you expect the CO2 data to differ in the two months, and why. (For example, do you expect one month to be higher?) Record your hypothesis below. (Hint: Think about different sources and sinks of carbon dioxide and how the time of year might affect these.) Data Statistics 1. Calculate the following using Excel (use Table 1 on the Excel sheet): January April Mean (ppm) Median (ppm) Standard deviation (ppm) 2. Create a bar graph of the data from Table 1. (Hint: Highlight the entire table, then click “insert,” then choose the “column chart.”) Place the chart in the allocated space; label the axes and chart title. 3. Below, list two observations regarding these statistics. Visualizing the Data in Time 1. Make a time series of the data. Use the “Index Minute” values for the x-axis values and the CO2 data from the y-axis data, which lets you overlay both data sets on one graph. Also, choose the “scatter with smooth lines” option. Place the chart in the allocated space; label the axes and chart title. 2. List one similarity between the two data sets. (Hint: Are there any patterns?) 3. List one difference between the two data sets. Examining Relationships in the Data 1. Make 2 scatter plots of temperature vs. CO2, one for the January data and one for the April data. Plot temperature on the x-axis and CO2 on the y-axis. Place the charts in the allocated space; label the axes and chart titles. 2. Do you see a relationship between CO2 concentrations and temperature? Is the relationship stronger for one month or the other? If so, which one? 3. Fit a linear relationship to each scatterplot and find the R2 value. Do the R2 values confirm your answer to the previous question? YES NoIf not, check your values. R2 for January = _____________ R2 for April = _____________ Final Conclusions 1. Complete the following: CO2 concentrations in January are generally _________________ than April. This means that CO2 appears to be greater in the ___________________ season. 2. Based on your analysis, was your hypothesis correct? If not, explain where your analysis and your hypothesis conflict. 3. Putting it all together: Temperature does not control CO2, so why do we see a correlation? Give two explanations for seasonal differences in CO2 concentrations (Hint: Again, think about CO2 sources and sinks, how these vary seasonally, and how they explain your analysis.) O3 Activity Worksheet Background Information: This data was collected in Delta, CO, one data set from May and one from October. Both data sets include four days of minute-averaged data—in other words, each data point is the average of all data recorded during that minute. 1. Develop a hypothesis that explains how you expect the O3 data to differ in the two months, and why. Record your hypothesis below. (Hint: Think about how ozone is formed and how the required “ingredients” vary seasonally.) Data Statistics 1. Calculate the following using Excel (use Table 1 on the Excel sheet): May October Mean (ppb) Median (ppb) Standard deviation (ppb) 1. Create a bar graph of the data from Table 1. (Hint: Highlight the entire table, then click “insert,” then choose the “column chart.”) Place the chart in the allocated space; label the axes and chart title. 1. Below, list two observations regarding these statistics. Visualizing the Data in Time 1. Make a time series of the data. Use the “Index Minute” values for the x-axis values and the O3 data from the y-axis data, which lets you overlay both data sets on one graph. Also, choose the “scatter with smooth lines” option. Place the chart in the allocated space; label the axes and chart title. 1. List one similarity between the two data sets. (Hint: Are there any patterns?) 1. List one difference between the two data sets. Examining Relationships in the Data 1. Make 2 scatter plots of temperature vs. O3, one for the May data and one for the October data. Plot temperature on the x-axis and O3 on the y-axis. Place the charts in the allocated space; label the axes and chart titles. 1. Do you see a relationship between O3 concentrations and temperature? Is the relationship stronger for one month or the other? If so, which one? 1. Fit a linear relationship to each scatterplot and find the R2 value. Do the R2 values confirm your answer to the previous question? Yes NoIf not, check your values. R2 for May =__________R2 for October =___________ Final Conclusions 1. Complete the following: O3 concentrations in May are generally _________________ than October. This means that O3 appears to be greater in the _________________ season. 1. Based on your analysis, was your hypothesis correct? If not, explain where your analysis and your hypothesis conflict. 1. Putting it all together: Temperature does not control O3, so why do we see a correlation? Give two explanations for seasonal differences in O3 concentrations (Hint: Again, think what is required to form ozone, how these vary seasonally, and how they explain your analysis.) AQ Data Seasonal Variation in Carbon Dioxide (Paonia, CO, data collected and organzied by the Hannigan Lab, College of Engineering, University of Colorado Boulder) January DataApril DataTable 1 Index MinuteDate and TimeCO2 (January, ppm)Temperature (°C)Relative Humidity (%)Index MinuteDate and TimeCO2 (April, ppm)Temperature (°C)Relative Humidity (%)January April 11/18/14 0:29458.4-0.939.514/18/14 0:30438.110.632.9Mean 21/18/14 1:29463.1-1.139.624/18/14 1:30437.610.133.9Median 31/18/14 2:29465.7-1.740.434/18/14 2:30441.010.033.6Std Dev 41/18/14 3:29466.9-2.541.844/18/14 3:30441.79.933.3 51/18/14 4:29469.0-2.641.354/18/14 4:30438.79.833.1Place bar graph here: 61/18/14 5:29463.1-2.039.364/18/14 5:30441.39.533.9 71/18/14 6:29452.80.532.974/18/14 6:29442.59.733.5 81/18/14 7:29432.94.027.884/18/14 7:29435.910.931.9 91/18/14 8:29417.68.622.994/18/14 8:29423.714.527.5 101/18/14 9:29407.112.021.3104/18/14 9:29417.418.923.6 111/18/14 10:29403.114.118.4114/18/14 10:29413.725.917.2 121/18/14 11:29399.415.518.2124/18/14 11:29418.032.312.1 131/18/14 12:29400.316.218.3134/18/14 12:29419.832.712.0 141/18/14 13:29400.915.718.1144/18/14 13:30417.833.411.7 151/18/14 14:29411.912.421.7154/18/14 14:30418.032.912.1 161/18/14 15:29438.15.036.2164/18/14 15:30415.631.312.2 171/18/14 16:29451.51.546.0174/18/14 16:30414.027.113.3 181/18/14 17:29447.01.445.0184/18/14 17:30409.526.413.4 191/18/14 18:30448.41.244.4194/18/14 18:30414.424.516.1 201/18/14 19:30453.50.843.9204/18/14 19:30418.120.724.5 211/18/14 20:30452.70.443.5214/18/14 20:30415.420.920.7 221/18/14 21:30453.00.441.3224/18/14 21:30423.419.624.6 231/18/14 22:30455.50.141.1234/18/14 22:30414.020.023.9 241/18/14 23:29455.7-0.241.1244/18/14 23:30412.318.528.1Place timeseries here: 251/19/14 0:30457.7-0.841.6254/19/14 0:30415.517.830.3 261/19/14 1:29457.4-0.439.9264/19/14 1:30420.316.335.2 271/19/14 2:30459.1-1.140.7274/19/14 2:30420.216.135.5 281/19/14 3:30460.7-1.240.2284/19/14 3:30422.215.935.5 291/19/14 4:30465.3-1.841.2294/19/14 4:29430.014.740.2 301/19/14 5:30464.0-1.941.0304/19/14 5:29435.713.643.2 311/19/14 6:30449.20.734.5314/19/14 6:29438.913.144.2 321/19/14 7:30428.24.527.9324/19/14 7:29441.113.344.8 331/19/14 8:30412.98.323.8334/19/14 8:30429.115.141.2 341/19/14 9:30401.513.720.1344/19/14 9:30416.016.941.2 351/19/14 10:30397.316.418.3354/19/14 10:30407.019.238.0 361/19/14 11:30392.617.816.3364/19/14 11:30407.124.128.2 371/19/14 12:30391.218.815.5374/19/14 12:29411.127.123.3 381/19/14 13:30391.617.816.9384/19/14 13:29412.227.921.8 391/19/14 14:30400.214.620.7394/19/14 14:29414.124.325.9 401/19/14 15:30422.47.034.5404/19/14 15:29409.828.521.5 411/19/14 16:30441.22.147.2414/19/14 16:29415.128.120.9 421/19/14 17:30439.32.146.4424/19/14 17:29413.024.523.8Place scatter plots here (January & April): 431/19/14 18:29443.81.746.6434/19/14 18:30412.720.731.6 441/19/14 19:30450.61.047.5444/19/14 19:30419.418.540.3 451/19/14 20:29454.50.447.6454/19/14 20:30427.516.041.7 461/19/14 21:30447.90.644.7464/19/14 21:29451.013.749.0 471/19/14 22:29449.00.543.2474/19/14 22:29450.912.352.5 481/19/14 23:30451.70.342.5484/19/14 23:29445.112.350.7 491/20/14 0:29453.5-0.343.3494/20/14 0:29440.313.248.2 501/20/14 1:29452.1-0.041.7504/20/14 1:29454.011.054.6 511/20/14 2:30456.4-0.642.8514/20/14 2:29453.510.056.8 521/20/14 3:30458.5-1.143.6524/20/14 3:29453.09.855.8 531/20/14 4:30462.9-1.544.1534/20/14 4:29457.09.755.9 541/20/14 5:30464.2-1.543.5544/20/14 5:29456.69.256.4 551/20/14 6:30444.71.735.8554/20/14 6:29458.29.255.2 561/20/14 7:30424.65.529.1564/20/14 7:29448.511.947.4 571/20/14 8:30407.610.723.7574/20/14 8:30430.316.935.9 581/20/14 9:30399.713.920.9584/20/14 9:30426.223.827.1 591/20/14 10:30402.213.923.0594/20/14 10:30419.223.628.4 601/20/14 11:30396.915.021.3604/20/14 11:30415.623.829.2 611/20/14 12:30394.215.919.5614/20/14 12:30420.027.523.2 621/20/14 13:30396.315.220.2624/20/14 13:30420.324.729.6 631/20/14 14:30403.612.822.9634/20/14 14:30423.925.225.5 641/20/14 15:29432.75.936.9644/20/14 15:30421.329.518.5 651/20/14 16:30446.01.748.1654/20/14 16:30425.028.318.7 661/20/14 17:29446.70.950.1664/20/14 17:30423.321.726.1 671/20/14 18:29445.20.848.3674/20/14 18:30420.420.327.1 681/20/14 19:29448.20.845.0684/20/14 19:30424.919.128.6 691/20/14 20:29449.10.641.4694/20/14 20:30429.718.131.0 701/20/14 21:29447.60.141.0704/20/14 21:30434.115.936.6 711/20/14 22:29451.90.138.1714/20/14 22:30436.714.540.8 721/20/14 23:29451.9-0.136.6724/20/14 23:29442.013.044.6 731/21/14 0:29455.0-0.636.5734/21/14 0:30441.912.545.3 741/21/14 1:29457.9-1.437.6744/21/14 1:29449.911.647.8 751/21/14 2:29456.2-1.236.2754/21/14 2:29447.111.546.8 761/21/14 3:29456.6-1.134.9764/21/14 3:29453.910.349.4 771/21/14 4:29467.7-2.638.3774/21/14 4:29456.99.849.8 781/21/14 5:29464.6-2.837.8784/21/14 5:29456.79.848.6 791/21/14 6:29451.4-0.231.3794/21/14 6:29462.99.449.4 801/21/14 7:29428.63.426.7804/21/14 7:29447.611.742.9 811/21/14 8:29415.77.922.7814/21/14 8:29435.616.033.4 821/21/14 9:29402.513.916.8824/21/14 9:29430.021.526.0 831/21/14 10:29399.315.116.6834/21/14 10:29425.827.320.7 841/21/14 11:29394.016.316.2844/21/14 11:29423.429.119.6 851/21/14 12:29391.817.714.6854/21/14 12:29423.430.416.2 861/21/14 13:29395.016.915.1864/21/14 13:29425.031.913.7 871/21/14 14:29404.013.418.7874/21/14 14:29427.532.513.7 881/21/14 15:29428.45.131.9884/21/14 15:29420.926.718.0 891/21/14 16:29447.71.337.7894/21/14 16:29427.632.312.5 901/21/14 17:29447.31.035.7904/21/14 17:29428.030.712.7 911/21/14 18:29444.30.733.5914/21/14 18:29422.624.917.8 921/21/14 19:29451.50.033.0924/21/14 19:29433.320.028.1 931/21/14 20:29459.1-0.632.6934/21/14 20:29444.615.934.3 941/21/14 21:29458.3-1.633.9944/21/14 21:29436.615.829.4 951/21/14 22:29459.8-1.932.1954/21/14 22:29433.515.828.1 961/21/14 23:29461.9-2.732.7964/21/14 23:30436.115.028.9 971/22/14 0:00468.3-3.132.9974/22/14 0:00435.014.430.3 O3 Activity Worksheet Background Information: This data