Answer :
The statement is False
First understood what is correlation & what is regression ?
Correlation :When measuring the relationship between two continuous variables, such as a dependent and independent variable or between two independent variables, correlation analysis is used.
Regression :Assessing the link between the outcome variable and one or more factors is referred to as regression analysis. Risk factors and co-founders are referred to as predictors or independent variables, whilst the result variable is known as the dependent or response variable. In a regression analysis, the dependent variable is represented by "y" and the independent variables are represented by "x."
Regression and correlation have some distinct differences.
The quantity and strength of the relationship between two variables is displayed through correlation. The data points are not connected by a fixed line. To determine how much one variable changes when the other stays constant, you compute a correlation. When r is zero, there is no relationship. One variable increases as the other does when r is positive. One variable rises while the other falls when r is negative.
While correlation does not match a line, linear regression identifies the optimal line that can predict y from x.
When measuring both variables, correlation is employed, whereas linear regression is typically used when manipulating the variable x.
Why the given statement is false ?
The statement is False because correlation does not imply causation. This means if X and Y are correlated we cannot conclude if X is response or explanatory or Y is response or explanatory...hence no clearly identification of which is explanatory or response is needed
To know more about correlation and regression visit: brainly.com/question/29820584
#SPJ4
