29. Below is the result of running linear regression in R. The least-squares regression equation isCall:Lm(formula = variable_C ~ variable_W)Coefficients:(Intercept)-5.5162variable_W0.2666A. variable_W -5.5162 + 0.2666 * variable CB. variable C = -5.5162 + 0.2666 * variable WC. variable W = 0.2666 – 5.5162 * variable CD. variable_C = 0.2666 – 5.5162 * variable_WB. bO.None of the above.MacBook AirDD80888000F3F4F6F7F8F9F5#$&8

29. From the given regression model, The independent variable is Variable_W Dependent variable:…

Below is the result of running linear regression in R. The least-squares regression equation is Call: Im(formula = variable_C variable_W) %3D Coefficients: variable W (Intercept) -5.5162 0.2666 A. variable W -5.5162 + 0.2666 * variable C B. variable C = -5.5162 +0.2666 * variable_W %3D

Below is the result of running linear regression in R. The least-squares regression equation is Call: Im(formula = variable_C variable_W) %3D Coefficients: variable W (Intercept) -5.5162 0.2666 A. variable W -5.5162 + 0.2666 * variable C B. variable C = -5.5162 +0.2666 * variable_W %3D

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

Correlation

Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.

Linear Correlation

A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.

Regression Analysis

Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.

Question

29. Below is the result of running linear regression in R. The least-squares regression equation is
Call:
Lm(formula = variable_C ~ variable_W)
Coefficients:
(Intercept)
-5.5162
variable_W
0.2666
A. variable_W -5.5162 + 0.2666 * variable C
B. variable C = -5.5162 + 0.2666 * variable W
C. variable W = 0.2666 – 5.5162 * variable C
D. variable_C = 0.2666 – 5.5162 * variable_W
B. bO.
None of the above.
MacBook Air
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