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A statement that proposes a cause and a effect between two variables?
Wiki User
∙ 12y ago
hypothesis
Wiki User
∙ 12y ago
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How does the time period or number of variables effect correlation coefficient?
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.
What is the ability to neutralize the effect of one variable in order to assess the relationship between two other variables?
makeing the correlation spurious
What is the method used to demonstrate a cause and effect relationship between two variables?
Statistically, you would need to conduct an experiment in which every single other variable was controlled. Not a feasible option so you control the obvious covariates and examine the residual covariance between the two variables of interest. Even so, you may not find something. For example, the covariance between x and y where y= x2 over any symmetric interval is 0.
A test of the effect of a single variable by changing it while keeping all other variables the same?
controlled experiment
What is moderating variable?
Moderation occurs when the relationship between two variable depends on a third variable. The third variable is referred to as the moderate variable or simply the moderator
What is a casual relationship in math?
A cause and effect relationship between the two variables.
How does the time period or number of variables effect correlation coefficient?
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.
Variables in an experiment whose effect you are testing?
'Known' Variables
What are the two variables that effect the force of gravity?
(1) The masses involved, (2) the distance between the masses.
What can be used to show a cause and effect between two variables?
A controlled experiment can be used to show a cause and effect relationship. ex: an experiment studying the effect of a certain medicine on patients.
Why cant you draw a conclusion about the effect of one variable in an investigation when the other key variables are not controlled?
If none of the variables are constant (or controls) you have no idea which variable or combination of variables caused the effect.
What is the difference between correlation and experiment?
An experiment is when the researcher manipulates the independent variable and records its effect on the dependent variable whilst maintaining strict control over any extraneous variables. A correlation is a statistical relationship between two or more variables. The researcher makes a change in one of the variables to see what is affected.
What is the ability to neutralize the effect of one variable in order to assess the relationship between two other variables?
makeing the correlation spurious
Why is it important to establish a cause and effect between the selected known variable and unknown variables?
To show the highest degree of validity of a hypothesis.
What 2 variables effect gravity?
The gravitational force between two objects is directly proportional to the product of their masses and inversely proportional to the square of the distance between their centers.
What is the differences between dependent variables and controlled variables in an experiment?
Controlled variables are things that may effect the outcome of an experiment, like temperature are pressure or the amount of some substance used. Dependent variables are those which change due to the conditions mentioned above. These might be speed of the reaction, or the quantity of some product.
Would the absence of independent variable imply a cause and effect relationship between 2 variables?
No, it would not. It is possible that the statistical model is under-specified and that the variables being studied are all "caused" by another variable.
