# data[].attributes.body
1. On 9/17 at 11:17- 70.69907800%
2. on 9/17 at 3:17-  70.69859603%
3. Change over four hours-.000482%
4. Change over one hour- .0001205%
5. Class average-

# data[].attributes.body
1. On 9/17 at 10.40am: 70.69915075%
2. On 9/17 at 2:47pm: 70.69865480%
3. Change over four hours: -0.00049595
4. Change over one hour: 0.0001239875

# data[].attributes.body
1. On 9/17 at 10:46 AM- 70.69914170%
2. On 9/17 at 2:46 PM- 70.69865532%
3. Change over four hours:  0.000486%
4. Change over one hour:  0.000122%
5. Class Average=

# data[].attributes.body
1) On 9/16 at 7:49 PM- 70.70095090%
2) On 9/17 at 2:10 PM-  70.69873065%
3) Change over time period= -.00222
4) Change in one hour: -.000123

# data[].attributes.body
1. On 9/17 at 9:33: 70.69933546%
2. On 9/17 at 1:33: 70.69880320%
3. Change over four hours: -0.000532%
4. Change over one hour: -0.000133%

# data[].attributes.body
1. On 9/15/17 at 1:46 PM: 70.7045865%
2. On 9/15/17 at 5:06 PM: 70.7042091%
3. Change over four hours: -.0003774
4. Change over one hour: -.00009435

# data[].attributes.body
1. On 9/16 at 1:27 PM- 70.70172070%
2. on 9/16 at 5:27 PM- 70.70171340
3. Change over four hour period- 0.000438%
4. change over ONE hour period is 0.00012045%.

# data[].attributes.body
1. On 9/16 at 7:11 PM - 70.7010700%
2. On 9/16 at 11:29 PM - 70.70051467%
3. Change over four hour period - -0.000555%
4. Change in one hour - -0.00013875%

# data[].attributes.body
1. On 9/16 at 7:09 PM- 70.70103050%
2. On 9/16 at 11:09 PM- 70.70054750%
3. Change over four hour period= 0.000483%
4. Change in one hour- 0.00012075%

# data[].attributes.body
1. On 9/16 at 11:23 AM-  70.70197106%.
2. On 9/16 at 3:25 PM-  70.70148252%.
3. Change over time period=  0.000489%
4. Change in one hour=
0.000122%

# data[].attributes.body
1. On 9/15 at 4:53 pm 70.70420973
2. On 9/15 at 8:44 pm.  70.70374240
3. Change over time period
-0.0004673
4. Change in one hour= -0.0001168

# data[].attributes.body
1. On 9/15 at 7:20 PM
- 70.70391295%
2. On 9/15 at 11:20 PM
-70.70342912%
3. Change over time period= -.000484%
4. Change in ONE hour=
-.000121%

# data[].attributes.body
1. On 9/15 at 10:58 AM- 70.70492526%
2. On 9/15 at 6:22 PM- 70.70402712%
3. Change over time period= -.000898%
4. Change in ONE hour= -.00012

# data[].attributes.body
1. On 9/15 at 11:33 AM- 70.70485454%
2. On 9/15 at 3:33 PM- 70.70436787
3. Change over time period=-0.00048667
4. Change in ONE hour= 0.00012153999%

# data[].attributes.body
1. On 9/15 at 2:50 PM- 70.70445766
2. On 9/15 at 6:50 PM-

# data[].attributes.body
1. On 9/15 at 2:49 PM- 70.70445766
2. On 9/17 at 3:55 PM-
70.69851993
3.Change over time period= -0.005938%
4. Change in ONE hour= -0.00012056

# data[].attributes.body
1. On 9/15 at 10:05 AM- 70.70503035%
2. On 9/15 at 3:05 PM- 70.70442751%
3. Change over time period= -0.00060284%
4. Change in ONE hour= -0.000120568%
5. Class Average=

# data[].attributes.body
1. On 9/15 at 10:06 am- 70.70502813
2. On 9/15 at 2:06 pm-70.70454126
3. Change over time period= -0.00048687
4. Change in ONE hour=0.0001217175

# data[].attributes.body
1. On 9/15 at 8:57 am- 70.70516880
2. On 9/15 at 12:57 pm- 70.7046850
3. Change over time period= -0.000484
4. Change in one hour= 0.000121

# data[].attributes.body
1. On 9/13 at 4:40 pm-- 70.710048361 %
2. On 9/13 at 8:40 pm--70.709515691%
3. Change over time period= -0.000532670%
4. Change in ONE hour= 0.001331675 %
5. Class Average=

# data[].attributes.body
1. On 9/16 at 5:30 PM-- (# here)
2. On 9/16 at 9:30 PM-- (# here)
3. Change over time period=
4. Change in ONE hour=
5. Class Average=