# data[].attributes.body
For this project, I choose to look at the data of the prison incarnation rates in every state in the US.
The mean and median are almost the same and the mode is very close to them as well so this can tell me that the distribution should be almost symmetrical.
I believe that the first graph illustrates the data the best in a way you can read it the easiest. Right away I noticed the peak towards the middle of the graph. The distribution looks almost normal with the peak just to the left a little bit.
I don't really like the pie graph for this data. It seems a bit confusing at first glance to me. The histogram and the line graph both show you where the peak is and shows you a better illustration of the distribution. You can even kind of see a gap in the histogram and line graph at 500-599 but you cannot see this on the pie chart.
Doing this project actually taught me a lot about analyzing data sets and graphs. I think what surprised me the most is how symmetrical the distribution was for this data. I did not expect such symmetry for a random data set I gathered online.

# data[].attributes.body
Mean  - 365.96
Median - 364
Mode - 353
Midrange - 415
Standard deviation - 136.42
Range - 560

# data[].attributes.headline
Incarceration Rates by State (per 100,000 people)

# data[].attributes.subject
Incarceration Rates by State (per 100,000 people)

# data[].attributes.body
AK  | 263
AL  | 418
AR  | 589
AZ  | 559
CA  | 321
CO | 353
CT  | 252
DE  | 410
FL  | 454
GA | 501
HI  | 230
IA  | 297
ID  | 444
IL  | 314
IN  | 400
KS  | 342
KY  | 523
LA  | 695
MA  | 145
MD  | 310
ME  | 135
MI  | 387
MN  | 179
MO  | 495
MS  | 626
MT  | 353
NC  | 324
ND  | 221
NE  | 280
NH  | 202
NJ   | 217
NM  | 331
NV   | 445
NY  | 239
OH  | 431
OK  | 693
OR  | 362
PA  | 366
RI   | 158
SC  | 366
SD  | 444
TN  | 387
TX  | 549
UT  | 208
VA   | 429
VT   | 187
WA  | 256
WI   | 391
WV  | 376
WY  | 441