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X and Y are not correlated, but X is significant predictor of Y in multiple regression. What does it mean?
Is there a significant relationship between X and Y?
Conclusion: There is sufficient evidence to conclude that there is a significant linear relationship between x and y because the correlation coefficient is significantly different from zero. What the conclusion means: There is a significant linear relationship between x and y.
When do you say the correlation coefficient is significant?
If the test concludes that the correlation coefficient is significantly different from zero, we say that the correlation coefficient is “significant.” Conclusion: There is sufficient evidence to conclude that there is a significant linear relationship between x and y because the correlation coefficient is significantly different from zero.
Is there an independent variable in correlational research?
Neither test score is thought to cause the other, so there is no independent variable to manipulate. In fact, the terms independent variable and dependent variable do not apply to this kind of research. Another strength of correlational research is that it is often higher in external validity than experimental research.
Is there a causal relationship between X and Y?
A → B → C is a causal path that infers that A indirectly causes C through B. For simplicity, assume all causal relationships are linear. First, consider a simple example of confounder bias: Here, a simple bivariable regression will suggest a dependence between X and Y. However, there is no direct causal relationship between X and Y.
Is it true that X predicts unique variance in Y?
X predicts unique variance in Y, but since these are not correlated (Pearson), it is somehow difficult to interpret. I know of opposite cases (i.e., two variables are correlated but regression is not significant) and those are relatively simpler to understand from a theoretical and statistical perspective.
Are there any correlations between y = BP and x 3?
As the matrix plot and the following correlation matrix suggest: there appears to be not only a strong relationship between y = BP and x 2 = Weight ( r = 0.950) and a strong relationship between y = BP and the predictor x 3 = BSA ( r = 0.866), but also a strong relationship between the two predictors x 2 = Weight and x 3 = BSA ( r = 0.875).