**Problem Statement:** Find a best-fit line for the following 5 data points. Please state the equation of the line, and give the error vector.**Data Points Table:**| t | y ||---|---|| 1 | 2 || 2 | 3 || 3 | 3 || 4 | 5 || 5 | 6 |To solve this problem, you will need to use methods such as least squares regression to determine the line of best fit for the provided data points. Then, calculate the error vector by finding the difference between the actual y-values and the predicted y-values from your best-fit line.1. **Equation of the Line:** The line of best fit can be expressed in the form *y = mx + b*, where *m* is the slope and *b* is the y-intercept.2. **The Error Vector:** After finding the equation of the line, substitute the x-values from the table into the equation to get predicted y-values. The error vector is the difference between these predicted y-values and the actual y-values given in the table.

Given data : The following data are passed: t Y 1 2 2 3 3 3 4 5 5 6 The independent…

Answered: Find a best-fit line for the following…

Find a best-fit line for the following 5 data points. Please state the equation of the line, and give the error vector. 33 4|5 56

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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Concept explainers

Estimation

A new framework of data analysis where the new statistical methods are used for interpreting the results and analyzing the data is known as estimation in statistics.

Question

**Problem Statement:**
Find a best-fit line for the following 5 data points. Please state the equation of the line, and give the error vector.

**Data Points Table:**

| t | y |
|---|---|
| 1 | 2 |
| 2 | 3 |
| 3 | 3 |
| 4 | 5 |
| 5 | 6 |

To solve this problem, you will need to use methods such as least squares regression to determine the line of best fit for the provided data points. Then, calculate the error vector by finding the difference between the actual y-values and the predicted y-values from your best-fit line.

1. **Equation of the Line:**
The line of best fit can be expressed in the form *y = mx + b*, where *m* is the slope and *b* is the y-intercept.

2. **The Error Vector:**
After finding the equation of the line, substitute the x-values from the table into the equation to get predicted y-values. The error vector is the difference between these predicted y-values and the actual y-values given in the table.

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