It depends on the relationship, if any, between the independent and dependent variables.
Least Squares method
In statistics, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables.
i am not sure. it seems that casual relationship compares between to things where there is no relationship and no sense. just is. on the other hand, an actual relationship does make sense. both these phrases mean the the same thing: comparing 2 different independent and dependent variables. it's just that casual relationship is inconsistent and makes no sense.
It can tell you how the dependent variable (usually represented on the y-axis) changes in relation (and hence the rate of change) to the independent varaible (usually represented on the x-axis).
Depends on the relationship between the independent and dependent variables.
It depends on the relationship, if any, between the independent and dependent variables.
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.
Every time the independent variables change, the dependent variables change.Dependent variables cannot change if the independent variables didn't change.
One is dependent and one is independent
Independent variables are those that you change in an experiment. Dependent variables are the ones that you measure in an experiment. Dependent variables are influenced by the independent variables that you change, so they are dependent upon the independent variable. Generally, experiments should have only one independent variable.
The dependent variable is influenced by changes in the independent variable. The dependent variable's values depend on the values of the independent variable. This relationship is often explored through statistical analysis in research studies.
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.
To illustrate the relationship between one or more dependent variables and a variable (often an independent variable).
"Player" is the independent variable, and "Points" is the dependent variable.
Time Series.