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This will be a math function. Each choice is only going to have one answer in this kind of function.
It assigns exactly one output value for each input value.
A probability density function assigns a probability value for each point in the domain of the random variable. The probability distribution assigns the same probability to subsets of that domain.
An independent variable is the variable that the scientist changes, and the dependent variables are the variables that the scientist doesn't control. So that would mean that the independent variable is typically the variable being manipulated or changed and the dependent variable is the observed result of the independent variable being manipulated. The independent variable in a science experiment is the variable that you change on purpose. The independent variable is the variable that scientists manipulate in an experiment in order to determine its effect on a dependent variable. For example, if you wanted to see what affected frog deformities, you would set up an experiment where you would have frogs placed in the same environments as each other, except for one variable (independent) that is different. Let's say the control group gets exposed to all the same food, temperature, length of daylight, population density, etc., as the experimental group. The experimental group has the amount of UV exposure varied. The UV exposure (independent variable) would be used to determine its effects on frog deformities (dependent variable).