Please provide all codes, outputs and steps to answer the following questions. No points will be given for a correct answer without adequate documentation of the steps. You are free to use R or python...

Will attach data for Q2,3,4&5 once we come to terms


Please provide all codes, outputs and steps to answer the following questions. No points will be given for a correct answer without adequate documentation of the steps. You are free to use R or python - Q1, Using the following coefficient estimates 01 = 1, 11 = 3, 21 = −2, 31 = -2, 02 = 4, 12= -5, 22= 2, 32 = 1, 03 = -2, 13 = 4, 23 = -1, 33 = -1, plot the estimated curve between X = −3 and X = 5 (4 points). - Q2, Based on the acoustic dataset provided and your ability to carry out 25 ground-truthing samples over the course of your survey, compare three survey methods in their ability to capture the bathymetric and backscatter range of values present. Please also show the influence of randomization, and make sure to have plots and/or metrics to support your written explanation *No points will be deducted for using ArcGIS to obtain a polygon. - Q3, Starting with the 25 points generated Q2, calculate the local spatial autocorrelation present in their depth values. Examine how the local spatial autocorrelation changes if you were to double and quadruple your survey efforts. Please make sure to have plots and/or metrics to support your written explanation - Q4, based on the steps taken in Q2 and Q3, demonstrate the effect of sample size and sample design on the performance of an IWD interpolation of sampled depth values. Please make sure to have plots and/or metrics to support your written explanation - Q5, Based on a fuzzy-kmeans classification of the acoustic dataset and best 25 ground-truthing samples designed from Q2, identify 3 locations for which the acquisition of additional ground truthing points would be especially valuable. Please make sure to explain your reasoning Bonus Identify 3 locations for which the acquisition of additional ground truthing points would be especially valuable based on Kriging variance