answersLogoWhite

0


Best Answer

The strength of the linear relationship between the two variables in the regression equation is the correlation coefficient, r, and is always a value between -1 and 1, inclusive.

The regression coefficient is the slope of the line of the regression equation.

User Avatar

Wiki User

13y ago
This answer is:
User Avatar

Add your answer:

Earn +20 pts
Q: What is regression coefficient and correlation coefficient?
Write your answer...
Submit
Still have questions?
magnify glass
imp
Continue Learning about Math & Arithmetic

What is the relationship between correlation coefficient and linear regreassion?

A correlation coefficient is a value between -1 and 1 that shows how close of a good fit the regression line is. For example a regular line has a correlation coefficient of 1. A regression is a best fit and therefore has a correlation coefficient close to one. the closer to one the more accurate the line is to a non regression line.


What are the properties of correlation?

The correlation coefficient is symmetrical with respect to X and Y i.e.The correlation coefficient is the geometric mean of the two regression coefficients. or .The correlation coefficient lies between -1 and 1. i.e. .


Distinguish between correlation and regression?

Correlation is a measure of the degree of agreement in the changes (variances) in two or more variables. In the case of two variables, if one of them increases by the same amount for a unit increase in the other, then the correlation coefficient is +1. If one of them decreases by the same amount for a unit increase in the other, then the correlation coefficient is -1. Lesser agreement results in an intermediate value. Regression involves estimating or quantifying this relationship. It is very important to remember that correlation and regression measure only the linear relationship between variables. A symmetrical relationshup, for example, y = x2 between values of x with equal magnitudes (-a < x < a), has a correlation coefficient of 0, and the regression line will be a horizontal line. Also, a relationship found using correlation or regression need not be causal.


Is -0.0001 a correlation coefficient?

Yes it can be a correlation coefficient.


Is 1.5 a correlation coefficient?

No, it cannot be a correlation coefficient.

Related questions

What is the relationship between correlation coefficient and linear regreassion?

A correlation coefficient is a value between -1 and 1 that shows how close of a good fit the regression line is. For example a regular line has a correlation coefficient of 1. A regression is a best fit and therefore has a correlation coefficient close to one. the closer to one the more accurate the line is to a non regression line.


What are the properties of correlation coefficient?

The correlation coefficient is symmetrical with respect to X and Y i.e.The correlation coefficient is the geometric mean of the two regression coefficients. or .The correlation coefficient lies between -1 and 1. i.e. .


What are the properties of correlation?

The correlation coefficient is symmetrical with respect to X and Y i.e.The correlation coefficient is the geometric mean of the two regression coefficients. or .The correlation coefficient lies between -1 and 1. i.e. .


What Are The Properties Of Regression Coefficient?

(a) Correlation coefficient is the geometric mean between the regression coefficients. (b) If one of the regression coefficients is greater than unity, the other must be less than unity. (c) Arithmetic mean of the regression coefficients is greater than the correlation coefficient r, provided r > 0. (d) Regression coefficients are independent of the changes of origin but not of scale.


Properties of regression coefficient-statistics?

8.7.4 Properties of Regression Coefficients:(a) Correlation coefficient is the geometric mean between the regression coefficients. (b) If one of the regression coefficients is greater than unity, the other must be less than unity.(c) Arithmetic mean of the regression coefficients is greater than the correlation coefficient r, providedr > 0.(d) Regression coefficients are independent of the changes of origin but not of scale.


When all of the points fall on the regression line what is the value of the correlation coefficient?

1 or -1


What is the sign of slope of the regression line if the correlation coefficient is -0.15?

The sign is negative.


When doing linear regression if the correlation coefficient is positive the slope of the line is negative?

False.


Can A regression equation have a negative coefficient of correlation and a negative coefficient of determination?

It's not quite possible for the coefficient of determination to be negative at all, because of its definition as r2 (coefficient of correlation squared). The coefficient of determination is useful since tells us how accurate the regression line's predictions will be but it cannot tell us which direction the line is going since it will always be a positive quantity even if the correlation is negative. On the other hand, r (the coefficient of correlation) gives the strength and direction of the correlation but says nothing about the regression line equation. Both r and r2 are found similarly but they are typically used to tell us different things.


Distinguish between correlation and regression?

Correlation is a measure of the degree of agreement in the changes (variances) in two or more variables. In the case of two variables, if one of them increases by the same amount for a unit increase in the other, then the correlation coefficient is +1. If one of them decreases by the same amount for a unit increase in the other, then the correlation coefficient is -1. Lesser agreement results in an intermediate value. Regression involves estimating or quantifying this relationship. It is very important to remember that correlation and regression measure only the linear relationship between variables. A symmetrical relationshup, for example, y = x2 between values of x with equal magnitudes (-a < x < a), has a correlation coefficient of 0, and the regression line will be a horizontal line. Also, a relationship found using correlation or regression need not be causal.


Difference between regression coefficient and correlation coefficient?

difference between correlation and regression?(1) The correlation answers the STRENGTH of linear association between paired variables, say X and Y. On the other hand, the regression tells us the FORM of linear association that best predicts Y from the values of X.(2a) Correlation is calculated whenever:* both X and Y is measured in each subject and quantify how much they are linearly associated.* in particular the Pearson's product moment correlation coefficient is used when the assumption of both X and Y are sampled from normally-distributed populations are satisfied* or the Spearman's moment order correlation coefficient is used if the assumption of normality is not satisfied.* correlation is not used when the variables are manipulated, for example, in experiments.(2b) Linear regression is used whenever:* at least one of the independent variables (Xi's) is to predict the dependent variable Y. Note: Some of the Xi's are dummy variables, i.e. Xi = 0 or 1, which are used to code some nominal variables.* if one manipulates the X variable, e.g. in an experiment.(3) Linear regression are not symmetric in terms of X and Y. That is interchanging X and Y will give a different regression model (i.e. X in terms of Y) against the original Y in terms of X.On the other hand, if you interchange variables X and Y in the calculation of correlation coefficient you will get the same value of this correlation coefficient.(4) The "best" linear regression model is obtained by selecting the variables (X's) with at least strong correlation to Y, i.e. >= 0.80 or


How do you determine coefficient of determination in excel?

= CORREL(x values,y values) ***clarification**** CORREL gives you the correlation coefficient (r), which is different than the coefficient of determination (R2) outside of simple linear regression situations.