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A professor in the School of Business in a university polled a dozen colleagues about the number of professional meetings they attended in the past five years (x) and the number of papers they submitted to refereed journals (y) during the same period. The summary data are given as follows: n = 12, * = 4, y = 4 x} = 232, X¡Y = 318, yi = 1025 i=1 Fit a simple linear regression model between x and y by finding out the estimates of the intercept and slope.

A professor in the School of Business in a university polled a dozen colleagues about the number of professional meetings they attended in the past five years (x) and the number of papers they submitted to refereed journals (y) during the same period. The summary data are given as follows: n = 12, * = 4, y = 4 x} = 232, X¡Y = 318, yi = 1025 i=1 Fit a simple linear regression model between x and y by finding out the estimates of the intercept and slope.

MATLAB: An Introduction with Applications
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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A professor in the School of Business in a university polled a dozen colleagues about the number of professional meetings they attended in the past five years (x) and the number of papers they submitted to refereed journals (v) during the same period. The summary data are given as follows: n = 12, X = 4, y = 4 > x} = 232, ) x¡Y{ = 318, 2 xiyı = 318, > yỉ = 1025 %3D i=1 i=1 i=1 Fit a simple linear regression model between x and y by finding out the estimates of the intercept and slope.
Transcribed Image Text:A professor in the School of Business in a university polled a dozen colleagues about the number of professional meetings they attended in the past five years (x) and the number of papers they submitted to refereed journals (v) during the same period. The summary data are given as follows: n = 12, X = 4, y = 4 > x} = 232, ) x¡Y{ = 318, 2 xiyı = 318, > yỉ = 1025 %3D i=1 i=1 i=1 Fit a simple linear regression model between x and y by finding out the estimates of the intercept and slope.
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