Hello
what are the differences between Pearson correlation and linear regression?
if we conducted a Pearson correlation before is that necessary to calculate linear regression for two parameters, like LST and albedo?
Dear Amir Tahooni
Pearson correlation is used to show how the tow variables are correlated and the Type of the relationship between tow variables. And Regression model is used to determine the Type of relationship and how the strongly effect of independent variable in to dependnt variable and we can use the estimated equation to predict value of independent variable for given values for independent variables. And also we can conclud the good ness of fit of the estimated model by the value of R-Square.
With best regard
Dear Amir Tahooni
Pearson correlation is used to show how the tow variables are correlated and the Type of the relationship between tow variables. And Regression model is used to determine the Type of relationship and how the strongly effect of independent variable in to dependnt variable and we can use the estimated equation to predict value of independent variable for given values for independent variables. And also we can conclud the good ness of fit of the estimated model by the value of R-Square.
With best regard
http://sites.utexas.edu/sos/guided/inferential/numeric/bivariate/cor/
Correlation is a statistic measure of the strength of association between two related variables and the direction of the relationship. Pearson correlation (r) is one type of correlation measure. It requires both variables to be normally distributed, besides linearity and homoscedasticity. The correlation coefficient varies from +1 (perfect positive relationship) to -1 (perfect negative relationship). Coefficient values towards 0 indicate weaker relationships between the two variables.
Linear regression is a predictive model. It is used to predict the value of an outcome variable depending on one or more input predictor variables.
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