I am getting a positive correlation between two variables. But in Multi-linear regression, i find the regression coefficient to be negative between those two variables. Is this normal?
The variables studied are Land surface temperature and Particulate matter concentration
Hi, yes. MLR is based on the association of multiple variables, whereas correlation is a one to one comparison. I would suggest looking up literature on multicollinearity. Generally, a high correlation (rule of thumb would be 0.7 at p
Well, the results can be said to be normal in view of the fact that they are similar at one point in time but distinct in the other circumstances.
Specifically, correlation matrix is a diagnostic test or analysis that shows the degree of relationships between the independent variables and the outcome variable(s) both individually and cumulatively. On the other spectrum, the multiple linear regression model is the estimation test or technique of data analysis that examine the impact or effect of the explanatory variables on the outcome variable of the study. The key similarity here is that, they both examine the relationships between the explanatory variables and the explained variable(s). Their major differences is that, while correlation matrix coefficients are used to determine the presence or absence of high correlations among variables, the coefficients in the Multiple Linear Regression (MLR)model are used to ascertain the explanatory power of the independent parameters on the outcome variable of the study.
It should be noted therefore, that a correlation matrix coefficient can serve as a signal for knowing the likely significance level of the coefficients in the MLR model. However, variations normally exist between the two especially regarding their significance level (P-values) and their corresponding signs which could be negative or positive. Therefore, the differences in their correlation coefficients sign (Whether + or -) sign is a normal scenario in a panel data analysis. However, such variations must be backed by strong justifications from both practical, theoretical, and methodological perspectives.
I hope this could be of great help in your data analysis.