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1. Solve for y, using the method of successive approximation. dr =x+ y; y(1) = 1 2. Find f(2.6) by interpolating the following table of values. Using Lagrange interpolation. yi 2.7183 xi 1 2 7.3891 3 3 20.0855 3. Using multiple linear regression, estimate the values of a, b and c in the given regression model. MODEL: y = ax ecx 3 4 3.6 5.2 6.8 8.8

1. Solve for y, using the method of successive approximation. dr =x+ y; y(1) = 1 2. Find f(2.6) by interpolating the following table of values. Using Lagrange interpolation. yi 2.7183 xi 1 2 7.3891 3 3 20.0855 3. Using multiple linear regression, estimate the values of a, b and c in the given regression model. MODEL: y = ax ecx 3 4 3.6 5.2 6.8 8.8

Advanced Engineering Mathematics
Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
Section: Chapter Questions
Problem 1RQ
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Instructions: Submit your solution in pdf format. Strictly one problem per page. 1. Solve for y3 using the method of successive approximation. dy dx = x + y; y(1) = 1 2. Find f(2.6) by interpolating the following table of values. Using Lagrange interpolation. yi 2.7183 i xi 1 1 2 2 7.3891 3 3 20.0855 3. Using multiple linear regression, estimate the values of a, b and c in the given regression model. MODEL: y = ax ecx %3! 1 3.6 5.2 6.8 8.8
Transcribed Image Text:Instructions: Submit your solution in pdf format. Strictly one problem per page. 1. Solve for y3 using the method of successive approximation. dy dx = x + y; y(1) = 1 2. Find f(2.6) by interpolating the following table of values. Using Lagrange interpolation. yi 2.7183 i xi 1 1 2 2 7.3891 3 3 20.0855 3. Using multiple linear regression, estimate the values of a, b and c in the given regression model. MODEL: y = ax ecx %3! 1 3.6 5.2 6.8 8.8
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