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What is an example of two variables that are likely to be correlated because they are both changing over time?
Wiki User
∙ 14y ago
[This is a brief statement on the fundamental nature of the statistic "correlation", and a caution against misinterpretations.]
AnswerFor pre-pubescent children, these three will probably correlate: age, weight and height. This would certainly make sense. A simple correlation will not say anything about how "linear" the relationship is.
Also, your age will correlate with the population of the US.
Your age will correlate with the cumulative total of books published in the US.
Over time, the cumulative number of catalogued objects in the sky will correlate with the total number of US presidents.
It's easy to come up with fun or funny correlations like these. The thing that takes skill is to find the fundamenal problem in a correlation that seems reasonable but in fact is misleading or meaningless.
Wiki User
∙ 14y ago
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What are examples of two variables that are likely to be correlated because they are both changing over time -?
Velocity and distance of an accelerating object would be one example.
The causes of muticolinearity in multiple regression?
There is multicollinearity in regression when the variables are highly correlated to each other. For example, if you have seven variables and three of them have high correlation, then you can just use one them in your dependent variable rather than using all three of them at the same time. Including multicollinear variables will give you a misleading result since it will inflate your mean square error making your F-value significant, even though it may not be significant.
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!
What is an Example of two independent variables?
Variables are symbols that replace unknown numbers. Variables are often letters. For example: 5*x=10 7*6=y Here "x" and "y" are the variables.
How is saying that two variables are correlated different from saying saying that one caused the other?
Correlation and causality are not necessarily related. My age is pretty well correlated with the number of TV sets in the world. But neither of them is caused by the other. In this particular example, they both happen to be correlated to time, but there need not be such a factor. Conversely, let y = x2. Compile a set of pairs of x values, x = -m and x = +m and the corresponding y values, m2. Now, y is totally defined by x, but the correlation of y with x (not x2) for the above set of values will be zero. In this example x causes y but the relationship is not linear - the model is wrongly specified.
What are examples of two variables that are likely to be correlated because they are both changing over time -?
Velocity and distance of an accelerating object would be one example.
What does a relationship between two correlated variables have?
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.
What do dependent and independent variables represent in science?
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).
What is the meaning of confounding in statistics?
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.
Examples of independent and dependent variables in science?
Independent and dependent variables are the variables that change during the course of an experiment. An example might be an experiment on how temperature affects plant growth. Changing the temperature is the independent variable, while the level of plant growth that results is the dependent variable.
What is the body of facts that has been systematically classified and correlated?
One example of this is an encyclopedia.
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!
The causes of muticolinearity in multiple regression?
There is multicollinearity in regression when the variables are highly correlated to each other. For example, if you have seven variables and three of them have high correlation, then you can just use one them in your dependent variable rather than using all three of them at the same time. Including multicollinear variables will give you a misleading result since it will inflate your mean square error making your F-value significant, even though it may not be significant.
What is an Example of two independent variables?
Variables are symbols that replace unknown numbers. Variables are often letters. For example: 5*x=10 7*6=y Here "x" and "y" are the variables.
How is saying that two variables are correlated different from saying saying that one caused the other?
Correlation and causality are not necessarily related. My age is pretty well correlated with the number of TV sets in the world. But neither of them is caused by the other. In this particular example, they both happen to be correlated to time, but there need not be such a factor. Conversely, let y = x2. Compile a set of pairs of x values, x = -m and x = +m and the corresponding y values, m2. Now, y is totally defined by x, but the correlation of y with x (not x2) for the above set of values will be zero. In this example x causes y but the relationship is not linear - the model is wrongly specified.
What are the example of an independent variable?
Examples of independent variables are:AgeRaceeducationWhy age is independent? because you can assign many variables that are dependent to age. Example: maturity, character, experience, and similar others.Race is also independent since many variables can be due to race. Example: color of the skin, language, belief, height, and similar others.But a race may also become a dependent variable if you relate it to- example the european continent. European continent now becomes the independent variable and races, beliefs, religions, and languages are dependent variables.
Why is it so important to control variables in an experiment?
You need to control variables in an experiment so as to make sure that only the variable you are testing and changing is the one affecting the results of your experiment. For example, in an experiment to find the effect of light intensity on the rate of photosynthesis of plant, you'll change light by putting a plant in sun and another in dark but you must not change carbon dioxide level for both plants so by that you have controlled other variables in the experiment(variables which must be the same always in the experiment).
