Problem 1. Implement KNN Regression algorithm from scratch. Dataset: {(x@), y(@))}|00, where:i=0;x(0)= -50 and æ(i) = x(i-1) + 0.5 for i = 1, ..., 100;y(i) = (x(@))2 for all i = 0,..., 100.

In this task, we're tasked with implementing the K-Nearest Neighbors (KNN) Regression algorithm from…

Answered: Problem 1. Implement KNN Regression…

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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Suppose you estimate a regression equation Income = α + β1Experience + β2Business + β3Gender + β4Private + β5Experience*Gender + ε Income - annual income is in thousands of dollars (1 unit of income = $1000);Experience is in years;Business is a dummy variable set to 1 if person has a business degree, 0 otherwise;Gender is a dummy variable set to 1 for females, 0 for males;Private is a dummy variable set to 1 if a person attended private college, 0 otherwise; The data are on individuals who graduated from college within last 10 years. 1.) A. Use information from Question 1 . What is the average income of males with non-business degree, who went to public college, and who have 3 years of experience? (to 3 decimal points) B. Use information from Question 1 What is the average income of females with non-business degree, who went to public college, and who have 6 years of experience? (to 3 decimal points) C. Use information from Question 1. Which of the following statements is a correct…
(A) Is it likely that the error term is homoskedastic in here? Briefly explain. (B) Suppose that an you modified this regression with two binary variables of female and male on the same equation. Can you run this regression? what problem would you run into?
The annual expenditure for cell phones varies by the age of an individual. The average annual expenditure E(a) (in $) for individuals of age a (in years) is given below:a: 20, 30, 40, 50, 60, 70E(a): 502, 658, 649, 627, 476, 2131. Use quadratic regression to find the model that best represents the data.2. At what age is the yearly expenditure for cell phones the greatest? Round the answer to the nearest year. YOU MUST SHOW THE WORK FOR THIS PART!
5
Q2 (a)State in algebraic notation and explain the assumption about the classical linear regression models disturbances that are referred to by the term ‘homoscedasticity’. (b)What would the consequence be for a regression model if theerrors were not homoscedastic? (c) How might you proceed if you found that (b) were actually the case? (d) What do you understand by the term ‘autocorrelation’?
27-16. Prove that: (a) If R123 = 0, X1 is uncorrelated with any of other variables i.e., r12=r13 = 0. (b) If R1-23 = 1, then linear regression of X, on X, and X3 is perfect.
suppose that the regression line is ỹ=2(x-4), then the predicted value of y when x = 3 is O -2 none O -4 O -6 O 2 O -1
3. Find the the equation of the least squares regression line for the data as the linear model f(x) = ao + a₁x in the manner discussed in the textbook using the formula A = (XTX)-¹XTY. What are your values for ao and a₁?
6. Suppose we estimate a linear regression equation Y; = Bo + B1X; + u; by OLS. (a) Show thatE ûi 0, where the û;'s are the regression residuals. i3D1 (b) Suppose we regress X; on û; by OLS, including a constant term in the regression. Show that the estimated coefficient on û; is equal to zero. (c) Suppose we regress Y; on the predicted value Y; by OLS, including a constant term in the regression. Show that the estimated coefficient on Y; is equal to one. (Hint: the regression residual is defined by û; = Y; – Y; where the predicted value Y; = Bo + B,X;. Here, Bo and B1 are the OLS estimators.)
It’s not 49 ()()))()(!&&):$:&:&
1. The following is an image of a Cartesian diagram. y (3, 4) (5, 4) (3, 3) (6, 3) (3, 2) (1, 1) From the picture above, determine the estimation result of the Linear Regression Equation.

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Problem 1. Implement KNN Regression algorithm from scratch. Dataset: {(x(i), y(i))}100, where: i=0; (0) = -50 and x(i) = x(i-1) +0.5 for i=1,..., 100; • y(i)= (x(i))2 for all i = 0,..., 100.