Im having issues with Part B and Part D. This is usually group work, but my partners choose not to help with any of the questions. Ultimately, this leads me to finishing it all which i prefer so I can prepare for the Exam. Any help finding these solutions would be appreciated. Thank you.

COMPUTER PROBLEM-SOLVING IN ENGINEERING AND COMPUTER SCIENCE EGR 1400 WINTER 2018 PROBLEM-SOLVING EXERCISE #3 ESTIMATING AND PREDICTING UNKNOWNS In this exercise you will use least-squares curve fitting to develop two equations to model the data given. Using these equations, we will predict the function values for two inputs and evaluate the prediction made by each of the curves and linear interpolation. 12 25 38 31 39 36 42 41 46 51 48 X 9 Y 18 30 35 Using the 8 non-shaded values above, find ao and a for the least squares linear regression. We will save the shaded values for our test data, that is, data points that are known but we will not include in the information used to make a representative curve. We will use these points to see how close our curve fit is to predicting actual values that were not used to derive the curve Compute the overall squared-error. Write the completed polynomial ART B Using the 8 non-shaded values from part A, find ao, a, and a2 for a parabolic least squares regression (polynomial of degree 2). Use MS Excel to solve for these coefficients. Compute the overall squared-error. Write the completed polynomial. Include a printout of your Excel spreadsheet On two separate graphs, plot the non-shaded data points and show the resulting curves from Part A and Part B; a separate graph for each curve. Use graph paper ART D Fill in the following test table Xlinear Y values Absolute Erro JActual-Predicted Results linear parabolic inear linear parabolic actual best best interpolation fit fit interpolation fit fit value method 35 Which method(s) performed the best? Would you have expected the outcomes? How do these perform for these data points vs. the linear and parabolic curve's squared errors? Discuss your