Number one regression line 5. The correlation coefficient is r-0.73Your Turn: Data Set 2Data Set: {(-3, 4), (-2,3), (–1,3), (0, 7), (1,5), (2, 6), (3, 1)}1. The regression line is: y =2. Based on the regression line, we would expect the value of response variable to bewhen the explanatory variable is 0.3. For each increase of 1 in of the explanatory variable, we can expect a(n) decrease ofin the response variable.4. If a = -3.5, the y =This is an example of extrapolation5. The correlation coefficient is r =-0.04(Round to the nearest hundredth.)Check

Given: X: {-3,-2,-1,0,1,2,3} Y: {4,3,3,7,5,6,1} Cov(X,Y)=∑ixi-xyi-yn-1Var(X)=∑ixi-x2n-1

Answered: ata Set: {(-3, 4), (-2,3), (-1,3),…

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ata Set: {(-3, 4), (-2,3), (-1,3), (0,7), (1,5), (2, 6), (3, 1)} 1. The regression line is: y %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

Number one regression line

5. The correlation coefficient is r
-0.73
Your Turn: Data Set 2
Data Set: {(-3, 4), (-2,3), (–1,3), (0, 7), (1,5), (2, 6), (3, 1)}
1. The regression line is: y =
2. Based on the regression line, we would expect the value of response variable to be
when the explanatory variable is 0.
3. For each increase of 1 in of the explanatory variable, we can expect a(n) decrease of
in the response variable.
4. If a = -
3.5, the y =
This is an example of extrapolation
5. The correlation coefficient is r =
-0.04
(Round to the nearest hundredth.)
Check

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