Independent variables can take values within a given boundary. The dependent variable will take values based on the independent variable and a given relationship at which the former can take its values.
Independent variables can take values within a given boundary. The dependent variable will take values based on the independent variable and a given relationship at which the former can take its values.
a DEPENDENT variable is one of the two variables in a relationship.its value depends on the other variable witch is called the independent variable.the INDEPENDENT variable is one of the two variables in a relationship . its value determines the value of the other variable called the independent variable.
an independent variable is a variable that changes the dependent variable.___________________________________________________Independentvariableis:a factor or phenomenon thatcausesorinfluencesanotherassociatedfactor or phenomenon called adependent variable. For example,incomeis an independentvariablebecause it causes and influences another variableconsumption. In a mathematicalequationormodel, the independent variable is the variable whosevalueis given. In anexperiment, it is the controlledcondition(that is allowed tochangein asystematicmanner) whose effect on thebehaviorof a dependent variable is studied. Also calledcontrolled variable,explanatory variable, orpredictor variable.
Depends on the experiment - there may be no relationship. Typically proportional, inversly proportional, proportional to the log and similar are given in set experiments at schools. So a staight line going up and straingt line going down or a curve of some sort when drawn as a line graph.
Independent variables can take values within a given boundary. The dependent variable will take values based on the independent variable and a given relationship at which the former can take its values.
The independent variable is that which the investigator changes, which results in the dependant variable which you then measure.
It is generally not recommended to have two dependent variables in a single analysis, as it can complicate the interpretation of results. It is usually clearer to analyze each dependent variable separately in order to understand the relationship with the independent variable. If the dependent variables are closely related, consider creating a composite score or index to represent the construct.
The steps are to find the y-axis (dependent variable) and the x-axis (independent variable), then make a scale for your variables on the graph.
There is the mathematical use and statistical use of the word "dependent" as explained on the related link. The mathematical use is a dependent variable is the outcome of a function with one or more independent variables. The statistical definition in the setting of an experiment, is similar, it is a variable which is expected to be affected by changes in independent variables. Example: If I don't eat (quantity of food- independent variable), I will be hungry (dependent variable). I will not have as much energy (second dependent variable). But nothing is perfect in experimentation. The circumstances of the experiment with numerous unknown relationships may cause unexpected results. In observational studes, with typically less control, variables can be hypothesized (or conjectured) to be related, i.e. global warming causes artic ice to melt, and artic ice melting leads to more global warming. This would also be called a feedback effect. I can produce a dependent series of numbers from an independent series. If I take a series of random numbers of size N which come from a given distribution, and rank them from low to high , with n = 1 for the lowest and n = N for the highest, the ranks become dependent variables. A change in one value could affect the rank of another variable. I have included some more information on dependent variables. See related links.
No, a manipulated variable (also known as independent variable) is deliberately changed in an experiment to see its effect on the dependent variable. The dependent variable is what is being measured or observed in response to changes in the manipulated variable. They are not the same but are related in an experiment.
A situation-relevant confounding variable is a third variable that is related to both the independent and dependent variables being studied, which can lead to a spurious relationship between them. It is crucial to identify and control for situation-relevant confounding variables in research to ensure that the true relationship between the variables of interest is accurately captured.
If there are two variables X and Y such that changes in the value of X cause changes in the value of Y but changes in Y do not cause changes in X, then X is the independent variable and Y is the dependent variable.However, if changes in the value of X cause changes in the value of Y and changes in Y cause changes in X, then both X and Y are dependent variables.