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When two variables are correlated there are four possible explanations of the correlation What are they?
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 and weaknesses of correlation?
A good starting point to research and very good at showing relationship between variables but doesn't demonstrate cause and effect
What is the relationship between correlation and causation?
correlation implies the cause and effect relationship,, but casuality doesn't imply correlation.
How can there be a correlation between two things even though one does not cause the other?
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).
What third variables might there be that would cause a spurious correlation in the survey results?
The third variable could be one which is correlated to both variables. These are called confounding variable. For example, in the UK you could find a correlation between coastal air pollution and ice cream sales. This is not because eating ice cream causes air pollution nor because air pollution causes people to eat ice cream. The confounding variable is the temperature. Warm weather gets people to drive to the sea!
When a n experiment shows that two variables are closely related the experiment shows?
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.
When two variables are correlated there are four possible explanations of the correlation What are they?
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
What are the strengths and weaknesses of correlation analysis?
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.
When two variables are related but one does not cause the other researchers term the situation?
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.
What is the difference between correlation and cause and effect?
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.
What is the difference between causation and correlation?
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.
What is the difference between cause and effect vs correlation in research studies?
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.
What is the difference between causation and correlation in statistical analysis?
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.
Can you explain the difference between causation and correlation?
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.
What's the difference between causation and correlation?
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.
What are the three conditions necessary for causation between 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).
Two variables are said to display correlation if?
cause n affect
