part E

PARTA X Y 10 22 15 30 20 36 25 38 30 43 35 42 40 44 45 47 50 49 55 51 Using the 8 non-shaded values above, find as 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. PARTB Using the 8 non-shaded values from part A, find a, a, and az 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 PARTC 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, PART D Fill in the following test table: Y values Absolute Error: Actual-Predicted Results X lincar linear parabolic linear linear parabolic actual best best interpolation fit fit interpolation fit fit value method 25 38 45 47 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 answer. PARTE In Part B you used Microsoft Excel to solve for the coefficients for parabolic least squares regression (polynomial of degree 2). Using Visual Basic to automate Microsoft Excel, create this Excel Spreadsheet. You will learn about this during the second week in problem-solving. Tum in a printout (screen shot) of this program