a. The correlation between X and Y is spurious b. X is the cause of Y c. Y is the cause of X d. A third variable is the cause of the correlation between X and Y
A good starting point to research and very good at showing relationship between variables but doesn't demonstrate cause and effect
correlation implies the cause and effect relationship,, but casuality doesn't imply correlation.
When interpreting a correlation coefficient, it is important to consider both the strength and direction of the relationship between the two variables, as indicated by the value of the coefficient (ranging from -1 to +1). Additionally, one should examine the context of the data, including sample size and potential confounding variables, which can influence the correlation. Finally, correlation does not imply causation, so it's crucial to avoid jumping to conclusions about cause-and-effect relationships based solely on the correlation coefficient.
The statement "correlation does not imply causation" means that just because two variables are correlated—meaning they change together—it does not necessarily mean that one variable causes the change in the other. Correlation can arise from various factors, including coincidence, confounding variables, or reverse causation. Therefore, establishing a cause-and-effect relationship requires further investigation beyond mere correlation.
that there is a strong correlation between the two variables. This means that as one variable changes, the other variable is likely to change in a consistent way. This correlation can suggest a cause-and-effect relationship between the variables, but further research is needed to establish causation.
a. The correlation between X and Y is spurious b. X is the cause of Y c. Y is the cause of X d. A third variable is the cause of the correlation between X and Y
Strengths:WeaknessesCalculating the strength of a relationship between variables.Cannot assume cause and effect, strong correlation between variables may be misleading.Useful as a pointer for further, more detailedresearch.Lack of correlation may not mean there is no relationship, it could be non-linear.
Researchers term the situation as correlation. Correlation indicates a statistical relationship between two variables, showing how they move together but not necessarily implying causation. The strength and direction of the correlation can provide insights into the relationship between the variables.
Correlation is a relationship between two variables where they change together, but it does not imply causation. Cause and effect, on the other hand, indicates that one variable directly influences the other.
Causation refers to a direct cause-and-effect relationship between two variables, where one variable directly influences the other. Correlation, on the other hand, refers to a relationship between two variables where they tend to change together, but one variable may not necessarily cause the change in the other.
Cause and effect in research studies refer to a direct relationship where one variable causes a change in another variable. Correlation, on the other hand, indicates a relationship between two variables but does not imply causation. In simpler terms, cause and effect shows a clear cause-and-effect relationship, while correlation shows a connection between variables without proving one causes the other.
Causation in statistical analysis refers to a direct cause-and-effect relationship between two variables, where changes in one variable directly cause changes in the other. Correlation, on the other hand, simply indicates a relationship between two variables without implying causation. In other words, correlation shows that two variables tend to change together, but it does not prove that one variable causes the other to change.
Causation refers to a direct cause-and-effect relationship between two variables, where one variable directly influences the other. Correlation, on the other hand, refers to a relationship between two variables where they tend to change together, but one variable may not necessarily cause the change in the other.
Causation refers to a direct cause-and-effect relationship between two variables, where one variable directly influences the other. Correlation, on the other hand, refers to a relationship between two variables where they tend to change together, but one variable may not necessarily cause the change in the other.
The three conditions necessary for causation between variables are covariance (relationship between variables), temporal precedence (the cause must precede the effect in time), and elimination of plausible alternative explanations (other possible causes are ruled out).
cause n affect