1.12 Consider a single-input single-output data set of the form {(xp, yp), p = 1, 2, ..., 16}: X = [X₁ X₂ X₁6][-2.1 -2 -1.1 -1 -0.9 -0.2 -0.1 0 0.1 0.9 1 1.1 1.9 2 2.1 3] Y₁6] = [1 2 1 2 3 1 2 3 4 2 3 4 3 455] [V₁ Y₂ ... ... 42 (a) Apply least-squares linear regression to the data to generate a prediction model yw'x+b*. (b) In a single figure, plot the sixteen data samples and the optimized model y=wx+b* as a straight line in the x-y space. (c) Use the model obtain to predict the output for input x = 1.5.
1.12 Consider a single-input single-output data set of the form {(xp, yp), p = 1, 2, ..., 16}: X = [X₁ X₂ X₁6][-2.1 -2 -1.1 -1 -0.9 -0.2 -0.1 0 0.1 0.9 1 1.1 1.9 2 2.1 3] Y₁6] = [1 2 1 2 3 1 2 3 4 2 3 4 3 455] [V₁ Y₂ ... ... 42 (a) Apply least-squares linear regression to the data to generate a prediction model yw'x+b*. (b) In a single figure, plot the sixteen data samples and the optimized model y=wx+b* as a straight line in the x-y space. (c) Use the model obtain to predict the output for input x = 1.5.
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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![1.12 Consider a single-input single-output data set of the form {(xp, yp), p = 1, 2, ..., 16}:
X = [X₁ X₂
%₁6]=[-2.1 -2 -1.1 -1 -0.9 -0.2 -0.1 0 0.1 0.9 1 1.1 1.9 2 2.13]
₁6] [12 1 2 3 1 2 3 4 2 3 4 3 455]
[V₁ V₂
16
...
...
42
(a) Apply least-squares linear regression to the data to generate a prediction model y=w*x+b*.
(b) In a single figure, plot the sixteen data samples and the optimized model y=w*x+b* as a
straight line in the x-y space.
(c) Use the model obtain to predict the output for input x = 1.5.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F2bb4ea8a-8d98-4b3d-8cae-1c255fbd5aa7%2F22f2b069-c40a-4bad-a69a-45e6cb148d96%2F6d9fcq7_processed.jpeg&w=3840&q=75)
Transcribed Image Text:1.12 Consider a single-input single-output data set of the form {(xp, yp), p = 1, 2, ..., 16}:
X = [X₁ X₂
%₁6]=[-2.1 -2 -1.1 -1 -0.9 -0.2 -0.1 0 0.1 0.9 1 1.1 1.9 2 2.13]
₁6] [12 1 2 3 1 2 3 4 2 3 4 3 455]
[V₁ V₂
16
...
...
42
(a) Apply least-squares linear regression to the data to generate a prediction model y=w*x+b*.
(b) In a single figure, plot the sixteen data samples and the optimized model y=w*x+b* as a
straight line in the x-y space.
(c) Use the model obtain to predict the output for input x = 1.5.
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