Problem 2. [SW 14.9] You have a sample of size n = 1 with data y₁ = 2 and x₁ = 1. You are interested in the value of 3 in the regression Y = XB+ u. (Note there is no intercept.) (a) Plot the sum of squared residuals (y₁ - br₁)² as function of b. You can choose your own range for b, one reasonable choice is b € [-2,5]. (Use any software you prefer. Excel is one option.) (b) Show that the least squares estimate of 3 is BOLS = 2. (c) Using ARidge = 1, plot the Ridge penalty term ARidgeb² as a function of b. (d) Using Ridge = 1, plot the Ridge penalized sum of squared residuals (y₁ - bx₁)² + XRidgeb². Please put all three lines in one plot.
Problem 2. [SW 14.9] You have a sample of size n = 1 with data y₁ = 2 and x₁ = 1. You are interested in the value of 3 in the regression Y = XB+ u. (Note there is no intercept.) (a) Plot the sum of squared residuals (y₁ - br₁)² as function of b. You can choose your own range for b, one reasonable choice is b € [-2,5]. (Use any software you prefer. Excel is one option.) (b) Show that the least squares estimate of 3 is BOLS = 2. (c) Using ARidge = 1, plot the Ridge penalty term ARidgeb² as a function of b. (d) Using Ridge = 1, plot the Ridge penalized sum of squared residuals (y₁ - bx₁)² + XRidgeb². Please put all three lines in one plot.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Parts A, C, and D. Can you put them all on one excel graph please. It should be one figure with three lines.
![**Problem 2. [SW 14.9]**
You have a sample of size \( n = 1 \) with data \( y_1 = 2 \) and \( x_1 = 1 \). You are interested in the value of \( \beta \) in the regression \( Y = X\beta + u \). (Note there is no intercept.)
(a) Plot the sum of squared residuals \((y_1 - bx_1)^2\) as a function of \( b \). You can choose your own range for \( b \); one reasonable choice is \( b \in [-2, 5] \). *(Use any software you prefer. Excel is one option.)*
(b) Show that the least squares estimate of \( \beta \) is \( \hat{\beta}^{OLS} = 2 \).
(c) Using \( \lambda_{Ridge} = 1 \), plot the Ridge penalty term \(\lambda_{Ridge}b^2\) as a function of \( b \).
(d) Using \( \lambda_{Ridge} = 1 \), plot the Ridge penalized sum of squared residuals \((y_1 - bx_1)^2 + \lambda_{Ridge}b^2\). Please put all three lines in one plot.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F61ff295b-afd4-43c3-8ed2-2cc17b9c2249%2Fe82395eb-3bb3-4535-bffa-7de6f921901d%2Fx9g33g8_processed.png&w=3840&q=75)
Transcribed Image Text:**Problem 2. [SW 14.9]**
You have a sample of size \( n = 1 \) with data \( y_1 = 2 \) and \( x_1 = 1 \). You are interested in the value of \( \beta \) in the regression \( Y = X\beta + u \). (Note there is no intercept.)
(a) Plot the sum of squared residuals \((y_1 - bx_1)^2\) as a function of \( b \). You can choose your own range for \( b \); one reasonable choice is \( b \in [-2, 5] \). *(Use any software you prefer. Excel is one option.)*
(b) Show that the least squares estimate of \( \beta \) is \( \hat{\beta}^{OLS} = 2 \).
(c) Using \( \lambda_{Ridge} = 1 \), plot the Ridge penalty term \(\lambda_{Ridge}b^2\) as a function of \( b \).
(d) Using \( \lambda_{Ridge} = 1 \), plot the Ridge penalized sum of squared residuals \((y_1 - bx_1)^2 + \lambda_{Ridge}b^2\). Please put all three lines in one plot.
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