dependent = y values, independent = x values
Intervening variables cannot be directly measured because they are theoretical constructs that explain the relationship between the independent and dependent variables in a study. Their impact is inferred based on the relationship between the variables of interest.
Yes, a correlation can exist between two variables, regardless of their nature as dependent or independent. The correlation coefficient quantifies the degree of relationship between variables, indicating how changes in one variable are associated with changes in the other. However, correlation does not imply causation.
The dependent variable changes in response to changes in the independent variable. This change in the dependent variable is measured to determine the impact or relationship between the two variables.
In a hypothesis, the independent variable is the factor that is manipulated or changed, while the dependent variable is the factor that is measured or observed to see how it is affected by the independent variable. The relationship between them is that changes in the independent variable are believed to cause changes in the dependent variable, allowing researchers to test their hypothesis and draw conclusions.
The experimenter deliberately changes the independent variable, which is the factor that is manipulated or controlled in an experiment to observe its effect on the dependent variable. This allows the experimenter to determine the causal relationship between the independent and dependent variables.
Depends on the relationship between the independent and dependent variables.
It depends on the relationship, if any, between the independent and dependent variables.
control
A regression graph is most useful for predicting dependent variables, as it shows the relationship between the independent and dependent variables, allowing for the prediction of future values.
dependent variable is current and independent variable is resisitance
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.
Intervening variables cannot be directly measured because they are theoretical constructs that explain the relationship between the independent and dependent variables in a study. Their impact is inferred based on the relationship between the variables of interest.
The term that describes the relationship in which both the dependent and independent variables in a graph increase is called a "positive correlation." In a positively correlated relationship, as the independent variable increases, the dependent variable also tends to increase, indicating a direct relationship between the two. This is often represented by an upward-sloping line on a graph.
"Player" is the independent variable, and "Points" is the dependent variable.
Time Series.
To illustrate the relationship between one or more dependent variables and a variable (often an independent variable).
Every time the independent variables change, the dependent variables change.Dependent variables cannot change if the independent variables didn't change.