Linear regressions of fluorescence and Discrete Chlorophyll

Question

I'm pretty much clueless here. I'm doing a project in Springboard's Data Science Career Track. Most of the data science makes sense; the ML part, not so much.

I have a data set from USGS - water quality measurements for San Francisco Bay. Four of the columns are defined as being calculated based on linear regressions of other data in the set.

I thought, "OK, I'll try to replicate their results. How hard can that be?"

It turns out I'm more clueless than I thought.

Example: "Calculated Chlorophyll [mg/m3] - Estimated concentration of chlorophyll-a in water samples from in-vivo fluorescence measured with a ship-board fluorometer. The calculations are based on linear regressions of Fluorescence and Discrete Chlorophyll."

I have Fluorescence and Discrete Chlorophyll data.

f = sns.regplot(x="Fluorescence", y="Discrete Chlorophyll", data=wq_df, 
                fit_reg = True, marker=".",
                scatter_kws={'color': 'C0', 'alpha':0.3, 's':60}, 
                line_kws={'color': 'C1'}
               )
f.figure.set_size_inches(12, 6)
plt.ylabel("Discrete Chlorophyll", fontsize=16)
plt.xlabel("Fluorescence", fontsize=16)
plt.title("Relationship between Discrete Chlorophyll and Fluorescence", fontsize=16 )
plt.show()

That's very pretty, but now what? Did what I plotted even make sense? How do I get data back out of that pretty line to compare to the "Calculated Chlorophyll" column in the dataset?

Should I be using a different library? I have some examples with statsmodels.api, statsmodels.formula.api.ols and sklearn.linear_model.LinearRegression but the examples are based on "Boston Housing Data Prices" and I don't know how to twist them into use for two variables, or even if I should be comparing these two variables this way.

Assume much technical savviness, very little statistical background, and no ML understanding to speak of.

Subset of dataset readable with wq_df = pd.read_csv('data.csv')

Discrete_Chlorophyll,Fluorescence,Calculated_Chlorophyll
2.3,0.04,2.0
3.8,0.04,2.2
8.1,0.06,7.5
10.2,0.07,9.5
4.5,0.05,4.5
2.0,0.04,3.5
4.1,0.05,6.0
11.5,0.07,11.6
10.4,0.06,10.4
6.9,0.04,6.2
5.8,0.04,6.2
4.9,0.04,4.9
3.7,0.03,3.9
3.1,0.03,2.5
2.4,0.02,2.4
4.4,0.03,3.5
2.4,0.03,3.5
3.1,0.03,3.6
8.8,0.06,8.6
3.5,0.04,3.4
2.8,0.04,3.2
2.7,0.04,2.8
3.3,0.03,2.5
1.7,0.03,1.8
1.5,0.03,1.8
1.1,0.02,1.1
1.4,0.02,1.3
2.0,0.03,2.0
1.8,0.03,1.8
1.7,0.03,1.8
2.8,0.03,2.9
4.5,0.05,4.4
2.6,0.04,3.9
3.2,0.04,3.8
4.0,0.03,3.8
4.9,0.04,3.9
4.8,0.04,3.8
3.4,0.03,3.6
2.8,0.03,2.9
4.1,0.04,4.6
1.4,0.03,2.3
9.4,0.05,9.2
4.6,0.04,4.4
3.7,0.04,4.1
3.3,0.03,3.7
4.4,0.03,3.5
3.0,0.03,3.3
3.1,0.03,2.7
2.9,0.03,2.7
2.4,0.03,2.1
1.8,0.02,1.8
2.2,0.03,2.4
6.0,0.04,6.9
13.0,0.06,12.6
5.5,0.06,7.5
4.6,0.05,6.0
5.1,0.06,6.7
10.5,0.07,8.7
7.1,0.05,5.9
7.5,0.05,5.6
8.9,0.05,6.3
9.2,0.04,6.3
3.6,0.03,6.2
3.6,0.03,6.2
3.8,0.03,6.2
5.2,0.04,7.1
7.9,0.06,6.4
8.7,0.07,6.5
6.4,0.11,6.6
4.8,0.08,5.0
3.8,0.07,4.2
5.0,0.06,3.5
2.8,0.04,2.9
1.6,0.03,1.9
1.0,0.02,1.8
1.6,0.02,1.7
1.9,0.03,1.9
1.9,0.03,2.0
2.2,0.03,2.1
2.4,0.03,2.1
2.2,0.03,1.1
2.4,0.03,1.1
2.2,0.03,1.6
2.2,0.04,2.4
2.6,0.04,2.6
2.1,0.03,2.2
1.5,0.03,2.0
2.6,0.04,3.3
3.2,0.04,4.6
6.1,0.06,7.8
11.2,0.07,9.7
11.2,0.07,9.7
4.5,0.05,5.9
2.3,0.04,3.4
2.5,0.03,2.9
2.6,0.03,2.8
3.0,0.03,2.7
2.5,0.03,2.5
2.5,0.02,1.9
2.9,0.02,2.0
2.6,0.03,2.4
3.5,0.03,2.8
3.3,0.03,2.5
2.7,0.03,2.2
2.2,0.03,2.2
2.1,0.03,2.4
1.8,0.03,2.4
1.3,0.03,2.4
2.2,0.04,3.1
2.1,0.02,1.6
2.2,0.02,1.8
2.6,0.02,1.8
2.2,0.02,1.8
2.2,0.02,1.9
1.9,0.03,2.0
1.4,0.03,2.0
2.1,0.03,2.0
1.9,0.03,2.1
1.4,0.03,2.3
1.7,0.04,2.7
2.2,0.04,3.1
3.8,0.05,3.8
5.3,0.06,4.2
3.6,0.05,3.8
3.5,0.05,3.8
1.1,0.02,1.1
0.9,0.02,1.1
1.6,0.02,1.1
1.4,0.02,1.1
1.1,0.02,1.1
0.4,0.02,1.1
1.5,0.02,1.1
1.0,0.01,1.1
1.1,0.02,1.1
1.3,0.02,1.3
1.3,0.02,1.3
1.1,0.03,1.3
1.6,0.04,1.3
1.1,0.05,1.2
1.3,0.05,1.2
6.0,0.07,6.9
5.6,0.07,5.6
5.2,0.07,6.9
7.6,0.07,6.7
4.5,0.06,4.2
7.2,0.07,6.9
4.5,0.06,3.4
5.1,0.06,4.9
2.8,0.05,2.2
6.9,0.07,5.7
6.9,0.07,6.1
1.9,0.05,2.2
3.4,0.06,3.3
3.4,0.06,3.8
3.6,0.06,4.2
2.1,0.05,2.8
2.1,0.05,2.8
1.9,0.05,2.0
2.9,0.05,4.0
3.5,0.05,3.9
4.8,0.06,4.5
5.1,0.05,3.9
9.0,0.1,9.0
3.2,0.07,3.5
4.6,0.05,3.9
3.7,0.03,4.1
2.1,0.05,3.3
3.8,0.04,3.3
1.9,0.03,2.9
3.0,0.03,3.0
4.7,0.03,3.6
1.9,0.04,4.4
7.0,0.05,6.7
5.1,0.06,6.9
7.7,0.06,6.9
7.7,0.05,5.6
1.9,0.04,3.2
2.9,0.04,2.8
3.0,0.03,2.3
1.5,0.02,1.5
1.1,0.02,1.4
4.2,0.05,3.5
2.1,0.03,3.1
2.1,0.02,2.0
5.3,0.03,4.7
8.7,0.04,7.6
17.0,0.06,13.2
8.0,0.06,12.7
3.2,0.06,3.5
1.8,0.04,2.1
1.4,0.03,1.7
1.5,0.03,1.5
3.4,0.04,2.2
2.9,0.03,1.0
11.7,0.07,12.4
44.8,0.18,44.1
8.9,0.06,10.4
2.1,0.03,1.0
2.7,0.03,1.8
3.1,0.04,3.6
2.6,0.04,4.4
3.2,0.03,3.0
2.5,0.03,2.7
2.8,0.03,2.4
1.9,0.02,2.2
2.3,0.03,2.4
3.8,0.04,3.3
3.0,0.04,3.5
2.9,0.02,2.1
9.7,0.06,10.2
8.6,0.05,7.1
2.2,0.02,2.1
2.7,0.03,2.8
3.1,0.03,3.6
2.8,0.03,4.1
2.4,0.04,2.3
1.9,0.03,1.9
1.2,0.03,1.4
2.2,0.03,1.8
1.4,0.02,1.2
1.5,0.03,1.9
0.9,0.02,0.9
3.0,0.04,3.0
2.3,0.03,2.3
1.9,0.03,2.0
0.9,0.02,1.0
1.1,0.02,0.9
9.6,0.07,9.4
3.6,0.04,4.1
1.7,0.03,1.9
1.5,0.03,1.7
2.3,0.03,1.9
1.8,0.02,1.5
1.1,0.02,1.2
2.1,0.04,2.5
3.1,0.04,2.5
1.7,0.04,2.4
2.0,0.03,2.3
2.6,0.03,2.3
2.0,0.02,2.2
3.2,0.03,2.3
2.5,0.04,3.0
2.3,0.03,2.5
1.8,0.03,2.1
3.0,0.03,2.5
3.3,0.04,2.7
2.6,0.04,2.7
1.8,0.03,1.8
1.9,0.04,2.1
2.0,0.04,2.1
1.6,0.04,2.0
1.7,0.03,1.9
2.6,0.04,2.0
2.1,0.03,1.9
8.5,0.1,9.3
8.7,0.09,8.2
8.0,0.08,7.6
4.4,0.05,4.2
2.5,0.03,2.3
2.1,0.03,2.4
1.1,0.02,1.3
3.2,0.04,3.3
4.3,0.05,4.4
3.9,0.05,4.1
3.8,0.05,4.2
5.5,0.06,5.0
2.4,0.04,2.2
1.7,0.04,2.1
2.4,0.04,2.2
2.0,0.03,2.3
2.2,0.03,2.2
2.8,0.03,2.2
2.5,0.04,2.1
1.9,0.03,2.3
9.8,0.08,9.9
11.4,0.08,10.7
7.1,0.06,7.4
4.3,0.05,5.4
2.7,0.04,3.0
2.7,0.04,2.8
3.5,0.04,2.3
3.1,0.05,4.2
2.9,0.04,2.6
2.9,0.04,2.6
5.0,0.06,6.0
7.2,0.06,5.4
9.3,0.08,8.2
5.8,0.07,7.2
6.0,0.07,12.6
35.8,0.14,33.3
7.5,0.05,4.4
14.8,0.08,15.9
3.4,0.04,1.6
3.2,0.04,2.6
5.1,0.05,5.2
3.1,0.05,5.3
1.6,0.03,1.8
1.0,0.03,1.0
1.8,0.03,1.7
3.1,0.04,2.8
4.4,0.05,4.5
7.6,0.06,5.5
13.8,0.1,15.5
34.7,0.19,33.5
17.7,0.11,18.6
3.2,0.04,2.4
2.5,0.03,1.7
5.5,0.05,5.5
6.7,0.06,6.9
34.2,0.3,34.3
27.7,0.24,27.3
3.3,0.05,4.0
1.2,0.02,0.3
2.1,0.03,1.2
2.5,0.05,3.9
34.3,0.13,32.8
7.1,0.05,7.7
2.7,0.03,1.6
2.1,0.03,0.2
2.3,0.03,2.1
2.8,0.05,8.4
3.9,0.07,4.2
4.6,0.08,4.5
6.3,0.1,6.0
6.3,0.1,6.0
4.6,0.08,4.4
5.5,0.1,6.0
4.1,0.07,4.1
2.1,0.12,2.0
1.3,0.08,1.6
1.6,0.07,1.4
4.1,0.05,4.2
2.4,0.04,2.9
1.5,0.03,2.0
1.8,0.04,2.4
1.9,0.03,1.4
1.9,0.03,2.1
3.1,0.04,2.7
3.9,0.04,3.0
23.5,0.12,22.1
36.8,0.19,37.4
8.4,0.06,9.0
2.3,0.03,1.7
4.6,0.05,5.2
4.3,0.04,4.3
1.2,0.04,2.4
0.6,0.03,1.2
2.2,0.04,1.9
1.9,0.04,1.6
1.6,0.03,0.8
5.0,0.06,4.5
9.0,0.07,8.2
12.8,0.1,12.7
2.1,0.03,1.6
0.7,0.03,1.2
2.7,0.04,2.8
10.6,0.09,11.3
1.3,0.03,1.3
0.9,0.03,1.2
1.1,0.03,1.1
0.9,0.03,1.1
1.5,0.03,1.1
1.5,0.03,1.3
1.6,0.04,1.6
4.9,0.05,3.3
1.9,0.04,2.1
1.5,0.03,0.9
0.9,0.03,0.6
1.2,0.04,2.0
1.5,0.04,2.8
1.4,0.06,1.6
1.7,0.06,1.6
1.5,0.06,1.4
1.1,0.05,1.2
1.1,0.04,1.1
1.2,0.04,1.1
2.3,0.1,1.5
1.9,0.09,1.4
1.9,0.09,1.4
2.0,0.13,1.7
0.9,0.1,1.5
1.2,0.11,1.5
1.5,0.12,1.6
0.9,0.1,1.4
0.9,0.1,1.5
0.6,0.06,0.7
0.4,0.1,0.8
0.8,0.12,0.6
0.7,0.11,0.8
1.0,0.16,0.9
0.8,0.17,0.9
1.2,0.15,0.9
2.3,0.12,2.0
2.2,0.12,2.1
1.6,0.12,1.9
1.3,0.13,1.4
1.3,0.14,1.0
0.6,0.14,0.8
0.5,0.12,0.5
0.5,0.15,0.5
0.6,0.16,0.6
0.6,0.15,0.6
5.7,0.2,5.6
4.7,0.17,4.6
2.2,0.12,2.6
1.8,0.1,2.1
1.2,0.1,1.8
1.3,0.08,1.1
2.6,0.1,1.8
2.6,0.11,2.3
1.8,0.09,1.1
1.8,0.08,1.1
0.8,0.08,1.1
0.6,0.08,1.1
0.7,0.08,1.1
1.1,0.08,1.1
1.2,0.11,1.1
0.9,0.1,1.1
0.9,0.11,1.1
1.4,0.09,1.1
2.6,0.29,2.6
1.4,0.16,1.4
4.2,0.15,4.0
2.7,0.13,3.0
2.6,0.11,2.3
1.9,0.09,1.8
1.1,0.09,1.6
0.8,0.07,0.7
0.7,0.07,0.9
1.3,0.07,0.9
0.9,0.07,0.8
0.8,0.07,0.8
1.0,0.07,0.8
1.0,0.11,1.1
0.8,0.09,0.9
0.7,0.08,0.8
1.7,0.11,1.1
1.0,0.1,1.0
1.1,0.11,1.0
0.5,0.1,1.0
15.0,0.38,14.0
6.8,0.24,8.2
3.7,0.16,4.3
2.1,0.11,2.3
2.0,0.11,2.4
1.7,0.09,1.3
1.5,0.09,1.1
1.5,0.08,0.7
1.1,0.08,1.1
1.4,0.09,1.4
1.6,0.1,1.9
2.7,0.16,3.3
3.0,0.14,3.0
3.8,0.17,3.8
2.9,0.15,3.0
2.3,0.13,2.5
4.0,0.14,2.8
50.2,1.42,41.9
38.1,1.14,33.7
21.5,0.56,16.3
3.8,0.15,4.2
3.9,0.16,4.3
9.2,0.28,7.9
1.3,0.13,3.7
2.7,0.14,3.4
2.3,0.12,2.8
1.9,0.1,1.8
2.6,0.12,2.6
2.8,0.13,3.1
3.8,0.13,3.0
2.3,0.11,2.4
2.5,0.12,2.6
2.4,0.11,2.4
2.7,0.12,2.7
6.9,0.17,5.4
5.0,0.18,6.2
4.1,0.19,5.9
5.3,0.17,5.2
2.5,0.11,3.5
6.5,0.2,6.1
5.7,0.16,5.0
6.8,0.18,5.6
3.4,0.13,4.3
5.0,0.13,4.0
4.6,0.17,6.4
15.4,0.24,8.8
6.3,0.16,5.8
6.5,0.13,5.1
4.2,0.13,4.9
8.6,0.23,10.5
23.6,0.19,6.8
5.3,0.14,4.2
23.6,0.47,22.2
9.0,0.19,6.7
8.1,0.22,8.4
3.2,0.14,3.9
5.3,0.18,6.0
7.5,0.2,7.5
7.3,0.21,7.6
4.7,0.15,4.6
16.2,0.4,18.1
1.7,0.1,2.3
2.2,0.1,2.3
4.3,0.13,3.8
2.5,0.09,2.0
0.9,0.07,1.1
1.6,0.08,1.4
2.3,0.1,2.2
3.2,0.13,3.7
4.7,0.14,4.9
2.8,0.12,3.7
4.3,0.11,3.0
6.0,0.15,5.5
4.0,0.21,9.0
11.0,0.17,6.7
11.7,0.2,8.6
12.8,0.25,11.6
12.8,0.27,12.7
2.7,0.15,5.3
6.5,0.2,8.2
2.6,0.12,2.7
2.8,0.13,3.2
2.3,0.11,2.6
1.5,0.13,3.0
1.7,0.09,1.5
1.9,0.09,1.8
5.3,0.17,4.6
3.4,0.1,2.2
3.9,0.11,4.5
5.8,0.14,5.7
10.2,0.17,7.5
6.3,0.12,4.7
16.5,0.26,12.2
9.9,0.25,11.7
9.2,0.24,11.1
4.8,0.16,7.0
7.3,0.21,9.5
1.5,0.13,2.0
1.9,0.12,2.0
1.8,0.12,2.0
1.8,0.13,2.0
1.5,0.1,1.7
1.3,0.11,1.9
1.2,0.07,1.4
1.5,0.08,1.5
3.2,0.07,1.4
2.1,0.09,1.6
1.6,0.08,1.4
2.7,0.12,4.1
8.7,0.15,5.8
7.9,0.14,5.5
15.8,0.25,12.5
9.1,0.22,10.4
7.9,0.22,10.4
5.0,0.17,7.4
5.5,0.16,6.5
2.1,0.12,2.7
2.1,0.11,2.1
2.2,0.1,1.8
1.5,0.09,1.4
0.7,0.09,1.4
0.6,0.06,0.5
1.6,0.09,1.3
3.7,0.13,2.8
2.1,0.12,2.5
2.2,0.09,1.5
1.6,0.09,1.6
1.5,0.09,1.6
2.5,0.1,2.2
1.5,0.09,1.6
8.7,0.19,7.4
15.3,0.33,15.1
16.0,0.34,15.7
5.1,0.18,6.8
8.0,0.22,9.0
2.2,0.13,2.1
1.5,0.1,1.5
2.2,0.09,1.4
1.0,0.08,1.2
0.6,0.07,1.0
1.5,0.09,1.3
1.4,0.12,1.9
5.8,0.14,4.9
22.5,0.44,23.6
22.4,0.4,21.3
4.8,0.12,3.9
3.0,0.13,4.3
1.5,0.1,2.2
2.2,0.12,3.4
2.4,0.09,1.3
2.1,0.1,2.1
1.5,0.09,1.5
2.6,0.14,2.7
2.2,0.16,2.9
3.1,0.11,2.1
1.2,0.08,1.6
0.8,0.08,1.6
1.0,0.08,1.6
2.8,0.1,1.9
2.3,0.1,2.0
2.3,0.1,1.9
1.5,0.08,0.9
2.2,0.09,1.2
2.9,0.1,2.3
2.1,0.09,1.6
4.5,0.14,5.2
33.9,0.54,33.0
16.1,0.3,16.4
11.8,0.26,13.5
3.3,0.12,3.8
1.8,0.12,3.0
1.2,0.09,1.9
1.1,0.07,1.3
1.3,0.07,1.3
1.2,0.07,1.2
1.8,0.08,1.5
3.3,0.11,2.7
3.3,0.11,2.6
3.4,0.12,3.0
2.8,0.09,6.4
5.6,0.14,6.8
3.3,0.11,6.6
5.2,0.13,6.8
28.1,0.36,9.1
13.5,0.28,8.3
6.8,0.16,7.1
1.4,0.11,3.0
1.8,0.09,1.9
2.5,0.1,2.6
0.7,0.06,0.1
1.7,0.08,1.3
7.8,0.17,6.8
7.1,0.18,7.1
13.9,0.18,14.1
4.5,0.11,3.5
5.6,0.14,5.4
2.3,0.1,2.0
2.4,0.09,1.8
3.0,0.11,3.5
24.3,0.42,27.6
14.8,0.28,16.4
5.7,0.17,8.0
2.1,0.09,1.3
2.9,0.13,2.8
2.3,0.11,2.4
2.4,0.11,2.2
1.6,0.1,1.9
0.9,0.07,1.2
1.5,0.07,1.3
2.0,0.11,2.2
1.7,0.08,1.4
1.5,0.08,1.5
1.7,0.08,1.5
1.7,0.08,1.8
1.3,0.07,1.1
1.7,0.08,1.7
1.0,0.07,0.7
5.3,0.13,4.9
21.0,0.35,19.0
14.6,0.28,14.8
9.7,0.19,8.5
2.1,0.08,1.7
1.8,0.1,1.8
1.3,0.08,1.4
1.0,0.07,1.2
1.2,0.08,1.4
1.3,0.07,1.2
1.5,0.08,1.4
1.4,0.07,1.3
1.8,0.09,1.7
1.3,0.07,1.0
1.2,0.07,0.7
1.5,0.07,0.9
1.5,0.07,0.6
2.7,0.09,2.4
15.8,0.29,15.1
17.3,0.3,16.0
4.4,0.16,6.9
1.5,0.08,1.5
2.6,0.12,2.7
2.3,0.1,2.2
2.1,0.09,2.0
1.9,0.08,1.7
1.4,0.07,1.5
1.5,0.07,1.4
1.5,0.07,1.5
1.4,0.08,1.7
1.7,0.07,1.4
1.1,0.07,0.9
1.1,0.08,1.4
1.3,0.07,1.3
0.6,0.06,0.3
6.4,0.17,6.4
6.6,0.16,6.0
1.9,0.12,3.6
0.8,0.06,0.4
1.4,0.09,1.4
1.5,0.09,1.4
1.3,0.08,1.3
1.0,0.08,1.3
1.1,0.08,1.3
1.3,0.07,1.3
1.3,0.07,1.3
1.5,0.08,1.3
1.4,0.07,1.3
1.4,0.07,1.0
1.3,0.07,1.0
1.1,0.07,1.1
1.4,0.08,1.3
2.3,0.09,1.7
1.5,0.09,1.5
2.4,0.12,2.3
0.6,0.07,1.0
1.1,2.79,0.8
0.8,2.67,0.8
0.4,2.63,0.8
0.7,2.66,0.8
0.8,2.2,0.7
19.8,2.78,20.9
23.2,2.82,21.1
13.9,2.06,14.7
17.4,2.36,17.3
2.6,0.77,3.8
3.1,0.84,4.4
2.8,0.43,0.9
2.2,0.43,0.9
2.3,0.59,2.3
2.4,0.65,2.8
1.8,0.5,1.5
2.0,0.56,2.0
1.4,0.55,2.0
1.7,0.49,1.4
1.4,0.47,1.3
1.9,0.58,2.2
1.6,0.53,1.7
39.5,5.31,41.3
51.8,6.33,49.9
26.7,2.75,19.8
35.8,4.75,36.6
19.5,2.61,18.6
22.4,3.17,23.3
15.2,2.4,16.9
13.4,2.34,16.3
9.1,1.93,12.9
11.5,1.73,11.2
5.7,1.16,6.5
5.3,0.92,4.4
1.8,0.5,0.9
1.4,0.43,0.4
7.8,1.15,8.0
6.1,0.82,6.0
8.2,1.02,7.2
6.0,0.79,5.8
9.8,1.1,7.7
6.5,0.83,6.1
6.6,0.88,6.4
5.6,0.76,5.7
4.2,0.6,4.7
4.3,1.08,7.6
13.5,1.84,14.8
11.0,1.46,11.4
15.5,1.87,15.1
7.7,1.26,9.6
9.9,1.26,9.6
5.4,0.97,7.0
6.8,0.98,7.1
5.7,0.84,5.8
4.8,0.78,5.3
7.0,0.96,7.0
9.6,1.16,8.7
7.5,1.12,8.3
7.7,0.99,7.2
11.4,1.28,9.8
7.8,1.13,8.5
9.4,1.09,8.1
12.4,1.3,9.9
7.8,1.04,7.6
11.1,1.44,11.2
3.0,0.86,2.7
3.1,0.84,2.6
2.8,0.96,3.2
2.7,0.89,2.8
1.7,0.65,1.7
1.5,0.68,1.8
5.3,0.75,5.2
1.9,0.62,1.5
3.2,0.62,1.7
0.1,0.56,0.1
1.1,0.58,0.3
0.2,0.61,1.3
0.5,0.6,1.0
0.9,0.6,1.1
1.3,0.61,1.3
1.3,0.63,1.8
1.1,0.6,1.1
0.8,0.61,1.4
1.8,0.71,2.1
1.6,0.7,2.0
1.7,0.63,1.6
1.2,0.67,1.8
2.0,0.63,1.6
2.2,0.59,1.4
0.1,0.54,1.1
2.0,0.64,1.7
2.4,0.64,1.7
2.1,0.73,2.2
3.0,0.63,2.1
2.4,0.61,1.8
9.4,1.34,10.9
11.2,1.24,9.6
5.9,0.85,4.8
2.7,0.65,2.3
1.9,0.72,3.2
1.6,0.61,1.8
1.2,0.61,1.8
1.1,0.63,2.1
1.3,0.59,1.6
1.1,0.52,0.7
1.4,0.57,1.4
3.6,0.82,3.7
4.0,0.84,3.8
4.2,0.83,3.8
3.9,0.92,4.3
4.5,0.81,3.6
3.2,0.72,3.1
3.2,0.76,3.3
3.2,0.79,3.5
3.3,0.77,3.3
2.1,0.66,2.6
2.9,0.71,2.9
2.2,0.61,2.3
2.9,0.65,2.6
2.6,0.68,2.8
1.6,0.53,1.8
2.1,0.51,1.7
2.5,0.71,2.9
1.2,0.52,1.7
1.8,0.52,1.6
2.2,0.65,2.2
2.6,0.72,2.6
2.9,0.73,2.6
2.1,0.74,2.7
2.7,0.66,2.3
1.8,0.56,1.8
1.8,0.6,2.0
3.0,0.57,1.9
1.8,0.56,1.8
1.6,0.49,1.5
1.1,0.54,1.7
3.6,0.83,3.2
2.8,0.78,3.0
2.8,0.73,2.8
4.1,1.12,4.3
2.8,0.76,2.9
3.9,0.95,3.7
3.1,0.71,2.7
2.4,0.69,2.6
2.7,0.74,2.8
2.4,0.67,2.5
1.9,0.52,1.8
2.1,0.54,1.9
2.2,0.66,2.4
2.9,0.69,2.5
2.2,0.69,2.5
2.9,0.67,2.5
2.6,0.59,2.1
1.3,0.6,2.1
2.2,0.6,2.1
2.1,0.66,2.4
1.4,0.49,1.6
1.8,0.52,1.7
2.9,0.98,3.5
3.5,0.91,3.2
3.5,0.88,3.1
2.9,0.76,2.7
2.6,0.74,2.7
2.0,0.66,2.4
2.4,0.62,2.3
2.0,0.56,2.1
2.1,0.58,2.1
1.7,0.47,1.8
1.6,0.48,1.8
3.1,0.7,2.8
3.2,0.67,2.7
2.9,0.6,2.4
1.5,0.66,2.6
2.5,0.63,2.5
2.6,0.66,2.7
1.9,0.52,2.0
1.6,0.55,2.1
2.4,0.56,2.2
2.8,0.53,2.0
2.3,0.93,3.7
2.5,0.88,3.5
4.3,0.97,3.9
2.0,0.72,2.6
2.8,0.74,2.7
6.0,1.14,4.9
2.6,0.7,2.5
2.5,0.65,2.2
2.7,0.65,2.2
2.6,0.62,2.1
1.6,0.45,1.0
1.2,0.45,0.9
3.1,0.68,2.5
3.4,0.71,2.7
1.9,0.69,2.5
1.4,0.6,1.9
1.9,0.64,2.2
2.1,0.67,2.4
2.0,0.57,1.8
0.6,0.48,1.2
0.9,0.47,1.1
2.0,0.76,2.0
2.0,0.73,2.0
3.2,0.73,2.0
1.1,0.76,2.0
2.4,0.53,2.0
2.0,0.53,2.0
1.3,0.58,2.0
1.0,0.46,0.6
1.1,0.48,0.8
3.0,0.68,2.8
2.9,0.71,3.1
2.2,0.61,2.2
2.5,0.61,2.1
1.3,0.52,1.2
1.1,0.52,1.2
0.6,0.5,1.0
0.8,0.51,1.1
0.8,0.45,0.5
0.8,0.48,0.9
0.5,0.47,0.7
1.1,0.73,3.0
1.2,0.69,2.8
2.0,0.76,3.2
2.2,0.71,2.9
4.0,0.82,3.5
5.2,0.99,4.5
4.0,0.69,2.8
4.8,0.68,2.7
3.2,0.65,2.5
2.5,0.58,2.1
1.6,0.54,1.2
1.3,0.57,1.3
1.8,0.58,1.4
1.4,0.59,1.5
1.1,0.59,1.5
1.3,0.58,1.4
1.2,0.58,1.4
1.2,0.56,1.3
1.4,0.54,1.1
0.3,0.41,0.3
1.0,0.47,0.7
0.1,0.45,0.6
0.4,0.42,0.4
1.0,0.57,1.6
1.0,0.63,1.5
1.8,0.69,1.3
1.8,0.63,1.5
1.6,0.56,1.7
1.4,0.58,1.6
1.8,0.55,1.7
1.1,0.54,1.7
2.6,0.51,1.8
2.1,0.49,1.9
1.5,0.5,1.0
1.4,0.52,1.0
1.2,0.55,1.1
0.9,0.55,1.1
0.9,0.55,1.1
1.3,0.52,1.0
0.9,0.52,1.0
0.5,0.53,1.1
1.0,0.46,1.0
0.7,0.41,0.9
1.7,0.6,3.0
2.3,0.58,3.0
2.4,0.66,3.1
3.0,0.71,3.2
3.7,0.72,3.3
3.4,0.56,3.0
3.6,0.54,2.9
3.5,0.53,2.9
4.2,0.71,3.2
1.9,0.46,1.2
1.8,0.48,1.3
1.5,0.59,1.9
2.4,0.61,2.1
2.2,0.57,1.8
2.3,0.59,1.9
1.2,0.54,1.7
1.2,0.52,1.5
1.1,0.53,1.6
1.3,0.51,1.5
0.6,0.39,0.9
1.0,0.41,1.0
1.2,0.44,1.1
1.7,0.5,1.9
1.7,0.46,1.3

Plot using data subset

Full data file: https://github.com/vlbrown/Tmp/blob/master/chlorophyll.csv

(correct if it's 12956 lines)

Answer 1

There are a couple of ways to do linear regression in python. I'm going to use statsmodels because it plays nice with pandas. Here is some code:

model = ols('Discrete_Chlorophyll ~ Fluorescence', data = df).fit()

df['predictions'] = model.predict(df)

Seaborn is not a library for doing regression, per se. In the code above, I've used stats models to make a regression of Discrete_Chlorophyll onto Fluorescence and assigned the predictions made from the model to a new column called predictions.

I'm curious if the Calculated_Chlorophyll column was actually created from a linear regression. The predictions from our model and the Calculated_Chlorophyll column differ substantially (see below). Are you sure that one column was calculated from the others?

EDIT:

Your data source does not match your photo. Here is a scatter plot of the data you've posted.