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Q: If there is a strong correlation between the variables a and b b must cause a?

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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.

Let's say we look at the consumption of junk food and heart attacks. What we would see is a correlation. The more junk food you eat the less risk of a heart attack. There is a correlation but is there a cause and effect relationship? Probably not. Young people eat a lot more junk food than older people. And older people are much more likely to suffer from a heart attack. Mathematically this is due to correlation between your x variables. In statistical analysis you usually assume independent variables. In reality thins are much more complicated. If you want to establish true relationships you need to use design of experiments (DoE).

A cause and effect relationship between the two variables.

Related questions

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.

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

A good starting point to research and very good at showing relationship between variables but doesn't demonstrate cause and effect

The experiment shows that there is a correlation between the two variables, meaning that as one variable changes, the other variable changes in a consistent way. However, it does not necessarily establish a cause-and-effect relationship between the variables. Further analysis is needed to determine causation.

correlation implies the cause and effect relationship,, but casuality doesn't imply correlation.

Correlation by itself is understood not to be sufficient to conclude causation. That two variables change together in a highly correlated way could mean that changes in both variables are being controlled or caused by something altogether different that has not yet come to light or that has not been considered as the cause.

Correlation analysis is a type of statistical analysis used to measure the strength of the relationship between two variables. It is used to determine whether there is a cause-and-effect relationship between two variables or if one of the variables is simply related to the other. It is usually expressed as a correlation coefficient a number between -1 and 1. A positive correlation coefficient means that the variables move in the same direction while a negative correlation coefficient means they move in opposite directions.Regression analysis is a type of statistical analysis used to predict the value of one variable based on the value of another. This type of analysis is used to determine the relationship between two or more variables and to determine the direction strength and form of the relationship. Regression analysis is useful for predicting future values of the dependent variable given a set of independent variables.Correlation Analysis is used to measure the strength of the relationship between two variables.Regression Analysis is used to predict the value of one variable based on the value of another.

Let me rephrase: Case 1: You have x and y variables, but the values for x is a constant (vertical line) Case 1: You have x and y variables, but the values for y is a constant (horizontal line) Result is that you have zero covariance, so a correlation coefficient can not be calculated because that would cause a division by zero. If one of your x value (Case 1) or y value (case 2) is not exactly the same as the others, then a correlation coefficient can be calculated, but does it mean anything? The correlation coefficient indicates a linear relationship between two random variables, not between a constant and a random variable.