No.
This a common misunderstanding and it is sometime the case but not necessarily.
A person who drives a lot gets in more accidents but may have caused none of them, they may have been hit by a drunk driver, etc.
Gamble more and you lose more.
Those are correlated and one caused the other.
One shortcoming is the danger of assuming that because 2 variables are highly correlated then one must have caused the other. Correlations alone can never support this assumption.
Two variables are said to be positively correlated if an increase in one is accompanied by an increase in the other. There need not be any causal link between these changes.
they are related, but one might not be causing the other
B. They are correlated because the hot-air gallon and the traffic are related to travel.
A positive correlation between two variables, say X and Y, means that if one increases, the other will too. No correlation means that they are not related. A negative correlation means that as one increases, the other decreases. Normally you will see this in studies as "Recent studies demonstrated a positive correlation between eating too much and obesity." Or, "recent studies demonstrate a negative correlation between a healthy, balanced diet and obesity".
One shortcoming is the danger of assuming that because 2 variables are highly correlated then one must have caused the other. Correlations alone can never support this assumption.
Correlation is defined as the degree of relationship between two or more variables. It is also called the simple correlation. The degree of relationship between two or more variables is called multi correlation. when two or more variables are said to be higjly correlated it means that they have a strong relationship such that a given rise or fall in one variable will lead to a direct change in the other variable or variables. good examples of highly correlated variables are price and quantity, wage rate and out put, tax and income.
If two graphs have exactly the same shape, it indicates that the variables are proportional to each other. This means that as one variable increases or decreases, the other variable changes in a consistent and fixed ratio.
co-related to or co- related with
No, correlation and causation are not the same thing. Correlation means that two variables are related in some way, while causation means that one variable directly causes a change in another variable. Just because two variables are correlated does not mean that one causes the other.
When things are correlated it means one thing predicts the other, but it doesn't mean it causes the other. I'll give an example. Golden anniversaries and hair loss are correlated. Now if you didnt know this phrase you would think long marriages causes hair loss, but its just that if your reach your golden anniversary it means youre probably very old, which accompanies hair loss. Correlated, not caused!
The two variables involved are highly but not perfectly correlated. When the value of one of them rises the other falls, and vice versa.
Correlation is a statistical relationship between two variables, while causation implies that one variable directly influences the other. Just because two variables are correlated does not mean that one causes the other.
Two variables are negatively correlated when the slope of the best-fit line that is drawn on the scatter plot with the independent variable on the x-axis and the dependent variable on the y-axis is negative.
Skirt lengths and intelligence are randomly correlated having a correlation coefficient of zero to plus 0.15 ie knowing the measure of one does not predict the value of the other--they are independent variables. To say such and such are not correlated is to say you have not compared the variables. They may have identity with a value of plus one, or they may be inversely related having a value of negative one, or they may be randomly correlated with a value of zero--but to compare is to correlate.
Two variables are said to be positively correlated if an increase in one is accompanied by an increase in the other. There need not be any causal link between these changes.
They are related but one might not be causing the other