y 38 137 56 201 50 152 52 107 37 150 60 173 67 194 54 166 59 154 43 137 30 38 53 193 59 154 40 175 65 247 Table 3: Data for Problem 2 While almost all statistical software and packages provide tools for one to solve the above linear regression problem, we may also consider it as a nonlinear program and solve it with an optimization solver (e.g., MS Excel solver). For the data provided in the following table, solve the linear regression problem. Write down the optimal 3 you find by rounding it to the first digit after the decimal point (e.g., 9.011, 1.229, and 3.245 should be rounded to 9.01, 1.23, and 3.25 to be written down).
y 38 137 56 201 50 152 52 107 37 150 60 173 67 194 54 166 59 154 43 137 30 38 53 193 59 154 40 175 65 247 Table 3: Data for Problem 2 While almost all statistical software and packages provide tools for one to solve the above linear regression problem, we may also consider it as a nonlinear program and solve it with an optimization solver (e.g., MS Excel solver). For the data provided in the following table, solve the linear regression problem. Write down the optimal 3 you find by rounding it to the first digit after the decimal point (e.g., 9.011, 1.229, and 3.245 should be rounded to 9.01, 1.23, and 3.25 to be written down).
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
Transcribed Image Text:y
38
137
56
201
50
152
52
107
37
150
60
173
67
194
54
166
59
154
43
137
30
38
53
193
59
154
40
175
65
247
Table 3: Data for Problem 2
While almost all statistical software and packages provide tools for one to solve the above linear regression
problem, we may also consider it as a nonlinear program and solve it with an optimization solver (e.g., MS Excel
solver). For the data provided in the following table, solve the linear regression problem. Write down the optimal B
you find by rounding it to the first digit after the decimal point (e.g., 9.011, 1.229, and 3.245 should be rounded to
9.01, 1.23, and 3.25 to be written down).
Transcribed Image Text:2. Consider a set of data (x;, Y;), i = 1, .., n, provided in Table 3. If we believe that x; and y; has a linear relationship, we may apply simple linear regression to fit these
data to a linear model. More precisely, we try to find a and B such that the straight line y = a + Bx minimizes the sum of squared errors for all the data points:
min
a,8
E yi – (a + Ba;
i=1
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