Least Squares Regression 1. In MATLAB write a program to of a function to calculate the slope and intercept for a best-fit line using the least-squares linear regression method. and to calculate the R2 value. Your inputs should be arrays of X- and Y-values. Y our outputs should be the slope and intercept and the R2 value. - Solve the following linear regression problems using your linear regression code. 4 7 10 11 14 17 20 y 1 2 8 7 6. 9. 12

Least Squares Regression 1. In MATLAB write a program to of a function to calculate the slope and intercept for a best-fit line using the least-squares linear regression method. and to calculate the R2 value. Your inputs should be arrays of X- and Y-values. Y our outputs should be the slope and intercept and the R2 value. - Solve the following linear regression problems using your linear regression code. 4 7 10 11 14 17 20 y 1 2 8 7 6. 9. 12

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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Computational Systems

The computation systems can be defined as the systems that are capable of solving a problem that includes calculations either mathematical or logical, and are able to produce the result as an output. For example, a simple calculator that is given a set of numbers and operators as input and produces the result as an output can be defined as a computational system. The above example is a simple illustration only, the computational systems range from simple devices to complex devices such as the systems that control the SpaceX companies, Starlink satellites which are being quite recently seen in the skies at many places globally.

Question

Least Squares Regression
1.
In MATLAB write a program to of a function to calculate the slope and intercept for a best-fit
line using the least-squares linear regression method. and to calculate the R2 value. Your inputs
should be arrays of X- and Y-values. Y our outputs should be the slope and intercept and the R2
value.
- Solve the following linear regression problems using your linear regression code.
X
2
4
6.
7
10
11
14
17
20
y
1
2
2
8
7
6
9
12
LO

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1. Suppose that a set of samples x1, x2, ..., xn, all real numbers, are drawn i.i.d. from the same distribution. Also assume that this distribution is a Gaussian distribution, which can be represented as N(u, o²). Write a function that accepts a set of samples and returns the MLE estimator for u. NOTE: The code below will be evaluated by a Python 2.7 interpreter. def mle(samples): pass Run Reset Once your function is correct, your will receive a submission code that you should input into the answer field. Enter answer here 2. In the previous question, you were asked to write a function for an estimator of a parameter of a distribution. Is the result of this function, an estimator, a random variable? Yes No
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How do I code this in MATLAB?
Use matlab to solve the questions
Write the code in matlab.
Solve using MATLAB and give the picture of code and the output
Solve this question by developing a computer program (MATLAB). Thank you
The following is a Matlab code. I am getting too big of an error. The max error should be less than 1*10^-3. I think its because of the backslash operator I used here, X = A \ b. I can't do X = A * inv(b) because b is not a square matrix. How should I reduce the error? % Problem 1% Step 1: Compute the right-hand side vector bn = 20;Xo = ones(n, 1); % Column vector with ones in each componentA = zeros(n); % Initialize the matrix A for i = 1:n for j = 1:n A(i, j) = 1 / (i + j - 1); endend b = A * Xo; % Step 2: Solve the system AX = b numericallyX = A \ b; % Backslash operator for numerical solution % Step 3: Compute the error vector E and find the maximum componentE = X - Xo;E_inf = max(abs(E));
The solution needed to be printed, not handwritten
#2