Problem 4 [5pts]. For problem 4, please include your Python code and any generated graphs. Note that some of the answers are qualitiative analyses, with more than one right answer, and they will be marked on the overall quality of your reasoning. I also encourage you to additionally submit a link to your code within your PDF report if you used a Google colab Upon more carefully examining your phytoplankton data, you find that the distributions of phytoplankton are highly non-uniform in your sector. You decide to collect spatial data instead for just the year 3035 to understand what's soing on. (a) [1pt] Build a multilinear model of biomass as a function of position. (b) [1pt] Predict the amount of biomass at the coordinate (17,5) (c) [1pt] Predict the amount of biomass at the coordinate (170,50) (d) [1pt] Predict the amount of biomass at the coordinate (1700,500) (e) [1pt] Which of your predictions do you trust the most? Which do you trust the least? Why? Problem 4 [5pts]. For problem 4, please include your Python code and any generated graphs. Note that some of the answers are qualitiative analyses, with more than one right answer, and they will be marked on the overall quality of your reasoning. I also encourage you to additionally submit a link to your code within your PDF report if you used a Google colab Upon more carefully examining your phytoplankton data, you find that the distributions of phytoplankton are highly non-uniform in your sector. You decide to collect spatial data instead for just the year 3035 to understand what's soing on. (a) [1pt] Build a multilinear model of biomass as a function of position. (b) [1pt] Predict the amount of biomass at the coordinate (17,5) (c) [1pt] Predict the amount of biomass at the coordinate (170,50) (d) [1pt] Predict the amount of biomass at the coordinate (1700,500) (e) [1pt] Which of your predictions do you trust the most? Which do you trust the least? Why