| 5.232608753 | 51.33997 | 1 |
| 4.559347708 | 3.047033 | 0 |
| 4.550088246 | 11.71957 | 1 |
| 3.386566659 | 28.04548 | 1 |
| 0.989064618 | 0.202602 | 0 |
| 4.555668273 | 67.83218 | 1 |
| 4.186405129 | 53.06328 | 1 |
| 1.207150769 | 78.43352 | 0 |
| 3.445792543 | 14.46725 | 1 |
| 2.962975266 | 23.10411 | 0 |
| 0.173612404 | 65.70817 | 0 |
| 2.768815371 | 65.28198 | 1 |
| 2.747367434 | 97.82201 | 1 |
| 4.486882933 | 77.4523 | 1 |
| 4.824678695 | 0.743551 | 0 |
| 5.586206724 | 48.65186 | 1 |
| 2.755386381 | 73.45392 | 1 |
| 1.787901977 | 97.36504 | 1 |
| 5.951385802 | 90.85691 | 1 |
| 2.737556923 | 15.44293 | 0 |
| 5.408894983 | 4.157112 | 0 |
| 1.715859824 | 0.937882 | 0 |
| 1.278844906 | 74.59771 | 0 |
| 2.514277044 | 97.32341 | 1 |
| 3.187058008 | 38.67714 | 1 |
| 4.949777159 | 87.91089 | 1 |
| 5.948802076 | 99.45704 | 1 |
| 4.58854855 | 73.22006 | 1 |
| 4.944593251 | 2.002865 | 0 |
| 4.095092929 | 30.82503 | 1 |
| 1.580255616 | 81.42979 | 1 |
| 5.582168688 | 77.37155 | 1 |
| 1.409875297 | 73.8556 | 1 |
| 4.173571574 | 10.78412 | 0 |
| 3.405384527 | 76.08957 | 1 |
| 5.303746588 | 91.13028 | 1 |
| 2.646338619 | 30.76739 | 0 |
| 5.648448558 | 24.47563 | 0 |
| 5.460162608 | 6.448907 | 1 |
| 2.530400279 | 92.75311 | 1 |
| 5.282410782 | 26.05696 | 1 |
| 4.798709185 | 42.12116 | 1 |
| 4.300055705 | 57.20119 | 1 |
| 4.729502404 | 6.523547 | 0 |
| 2.476612604 | 55.6309 | 1 |
| 3.190133005 | 67.05927 | 1 |
| 1.021463153 | 77.07357 | 1 |
| 0.733750098 | 95.86227 | 1 |
| 2.724156232 | 4.533329 | 0 |
| 4.232730005 | 96.12467 | 1 |
For a column of data x and a column of data y, there is an equationthat relates the slope of the line of best fit (m) with thecorrelation coefficient (r). That equation is:
m = r * std(y)/std(x)
In the equation above, std(y)represents the standard deviation of the y column of data andstd(x) is the standard deviation of the x column of data.
Use the Pandas .corr() and .std()methods to compute the slope of the line of best fit betweenDiameter and Pigment?first & second col?.
Next, use compute the y-intecept ofthe line of best fit using:
b = ybar – m*xbar
Lastly, plotthe line of best fit using matplotlib.pyplot.
