Want this question answered?
The correlation remains the same.
correlation implies the cause and effect relationship,, but casuality doesn't imply correlation.
The correlation coefficient, plus graphical methods to verify the validity of a linear relationship (which is what the correlation coefficient measures), and the appropriate tests of the statisitical significance of the correlation coefficient.
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 good starting point to research and very good at showing relationship between variables but doesn't demonstrate cause and effect
Correlation analysis can be misused to explain a cause and effect relationship by misinterpreting data to assume that because something happened when a condition was present, it must have caused it, or vice versa. This isn't necessarily so, and those events and conditions may be unrelated.
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.
The correlation remains the same.
correlation implies the cause and effect relationship,, but casuality doesn't imply correlation.
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.
A very small effect having a greater side effect on a variable or an object may be termed as a strong correlation.
No correlational study is not cause and effect because correlation does not measure cause.
Correlation has no effect on linear transformations.
The time period may not affect the correlation coefficient at all. If looking at the correlation between the mass and volume of steel objects, time is totally irrelevant. The effect of the number of variables depends on whether or not the extra variables are related to ANY of the variables in the equation.
No there is no correlation to T5 and oral BCP. But it does effect your thyroid.
correlation
NO! Mononucleosis is caused by a virus. Antibiotics have no effect on viruses and can increase bacterial antibiotic resistance when misused.