The data below obtained from the experiment: 1.5 1.8 2.4 2.7 3.2 3.6 f (x) 0.7555 0.6206 0.5052 0.4812 0.4498 0.4337 This data can be fitted to the curve y = where C and D are C + Dx constants. 1 C a. Show that the curve above can be written as D+ - b. Hence, by using the least squares linearization process, determine the value of C and D. Start your calculation with n S = (Y: - Y(X;))² i=0 where S is the total squares error. c. Estimate the value of f(2.5) based on the results obtained in part (b).

MATLAB: An Introduction with Applications
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The data below obtained from the experiment:
1.5
1.8
2.4
2.7
3.2
3.6
f (x) 0.75550.6206 0.5052 0.4812 0.4498 0.4337
This data can be fitted to the curve y =
where C and D are
C + Dx
constants.
1
C
a. Show that the curve above can be written as
D+
-
b. Hence, by using the least squares linearization process, determine
the value of C and D. Start your calculation with
n
S = E(Y: - Y(X;))²
i=0
where S is the total squares error.
c. Estimate the value of f(2.5) based on the results obtained in part
(b).
Transcribed Image Text:The data below obtained from the experiment: 1.5 1.8 2.4 2.7 3.2 3.6 f (x) 0.75550.6206 0.5052 0.4812 0.4498 0.4337 This data can be fitted to the curve y = where C and D are C + Dx constants. 1 C a. Show that the curve above can be written as D+ - b. Hence, by using the least squares linearization process, determine the value of C and D. Start your calculation with n S = E(Y: - Y(X;))² i=0 where S is the total squares error. c. Estimate the value of f(2.5) based on the results obtained in part (b).
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