A student working on a summer internship in the economic research office of a large corporation, studied the relation between sales of a product (in million dollars) and population in the firm's 50 marketing districts. The computer output appears below: T VALUE PARAMETER STD ERROR PR >T 7.411 0.002 INTERCEPT 1.9802 2.1756 POPULATION 0.7550 0.2192 0.1043 0.018 The fitted regression line is: OY= 0.755 +7.411X OY= 7.411+0.755X O Y = 2.1756 + 0.1043X OY=7.411+ 7.55X
A student working on a summer internship in the economic research office of a large corporation, studied the relation between sales of a product (in million dollars) and population in the firm's 50 marketing districts. The computer output appears below: T VALUE PARAMETER STD ERROR PR >T 7.411 0.002 INTERCEPT 1.9802 2.1756 POPULATION 0.7550 0.2192 0.1043 0.018 The fitted regression line is: OY= 0.755 +7.411X OY= 7.411+0.755X O Y = 2.1756 + 0.1043X OY=7.411+ 7.55X
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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
Transcribed Image Text:A student working on a summer internship in the economic research office of a large corporation, studied the relation between
sales of a product (in million dollars) and population in the firm's 50 marketing districts. The computer output appears below:
T VALUE
PARAMETER
STD ERROR
PR >T
7.411
0.002
INTERCEPT
1.9802
2.1756
POPULATION
0.7550
0.2192
0.1043
0.018
The fitted regression line is:
OY= 0.755 +7.411X
OY= 7.411+0.755X
O Y = 2.1756 + 0.1043X
OY=7.411+ 7.55X
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