No, they do not.
Velocity and distance of an accelerating object would be one example.
false they can be related with quadratic equation as well
Multicollinearity is the condition occurring when two or more of the independent variables in a regression equation are correlated.
Sure. If you can observe that when variable A changed, variable B didn't change, and this happens repeatedly, that is a good indication that there is no relationship between those variables.
No, they do not.
Not necessarily. They must decrease together (the question does not say so). Also, the decreases may not be sufficient for the to be correlated. It is less likely that they are negatively correlated, but with the amount of information in the question that is about all that can be said.
If two variables are highly correlated, the Pearson correlation will be close to -1.0 or +1.0. A correlation of zero shows no relationship.
Velocity and distance of an accelerating object would be one example.
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.
false they can be related with quadratic equation as well
The preposition "with" should follow the word "correlated." For example: "The data suggests that these two variables are strongly correlated with each other."
As the population increases, the living space per capita of the country decreases.
Multicollinearity is the condition occurring when two or more of the independent variables in a regression equation are correlated.
Correlation between two variables implies a linear relationship between them. The existence of correlation implies no causal relationship: the two could be causally related to a third variable. For example, my age is correlated with the number of TV sets in the UK but obviously there is no causal link between them - they are both linked to time.
The number of TV sets in the UK and my age.
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.