Velocity and distance of an accelerating object would be one example.
No. It may appear to cause the change because the changes are correlated. However, it is quite possible that changes in both variables are caused by some third, possibly unknown, variable.No. It may appear to cause the change because the changes are correlated. However, it is quite possible that changes in both variables are caused by some third, possibly unknown, variable.No. It may appear to cause the change because the changes are correlated. However, it is quite possible that changes in both variables are caused by some third, possibly unknown, variable.No. It may appear to cause the change because the changes are correlated. However, it is quite possible that changes in both variables are caused by some third, possibly unknown, variable.
variables are all related because they can equal to any number
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!
Because there are two unknown variables.
Because they allow you to generalise results. Then, for specific value of the variables you get specific answers.
No. It may appear to cause the change because the changes are correlated. However, it is quite possible that changes in both variables are caused by some third, possibly unknown, variable.No. It may appear to cause the change because the changes are correlated. However, it is quite possible that changes in both variables are caused by some third, possibly unknown, variable.No. It may appear to cause the change because the changes are correlated. However, it is quite possible that changes in both variables are caused by some third, possibly unknown, variable.No. It may appear to cause the change because the changes are correlated. However, it is quite possible that changes in both variables are caused by some third, possibly unknown, variable.
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.
They are because they can.
In science, independent variables are variables that you control the change of, to see how somethings changes as a result of changing these variables. Dependent variables are variables that change because the independent variables are changed, but you don't change directly. A good example of this would be an experiment where you're measing how cold a glass of water gets after putting in different amounts of ice in it and wating 5 minutes. The independant variable would be the amount of ice you put into each glass, because that's what you're directly changing. The dependent variable is how cold each glass gets, because that's the result you're trying to see by changing the independent variable - it changes because something else changes. Additionally, when graphing, independent variables are put on the x-axis (horizontal line), and dependent variables are put on the y-axis (vertical line).
They are because they can.
In statistics. a confounding variable is one that is not under examination but which is correlated with the independent and dependent variable. Any association (correlation) between these two variables is hidden (confounded) by their correlation with the extraneous variable. A simple example: The proportion of black-and-white TV sets in the UK and the greyness of my hair are negatively correlated. But that is not because the TV sets are becoming colour sets and so my hair is loosing colour, nor the other way around. It is simply that both are correlated with the passage of time. Time is the confounding variable in this example.
Controls are the things you leave the same when you do an experiment. Variables are the things you affect in an experiment to see if it makes a difference. It depends on the experiment how you would "control" the variable.
variables are all related because they can equal to any number
because music is insperation and the type of music can change your mood
You isolate variables in math because the point of an equation is to solve for the variables. By isolating the variables you have learned what that variable stands for and thus solved the equation.
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!
Weather is variable because of a country's place or location on the earth. Weather is quite variable in Great Britain and it is very unique here.