It does not have to. It is simply a study where two variables have a joint probability density function. There is no requirement for both variables to be dependent - one may be dependent on the other (which is independent).
The F-variate, named after the statistician Ronald Fisher, crops up in statistics in the analysis of variance (amongst other things). Suppose you have a bivariate normal distribution. You calculate the sums of squares of the dependent variable that can be explained by regression and a residual sum of squares. Under the null hypothesis that there is no linear regression between the two variables (of the bivariate distribution), the ratio of the regression sum of squares divided by the residual sum of squares is distributed as an F-variate. There is a lot more to it, but not something that is easy to explain in this manner - particularly when I do not know your knowledge level.
If there are only two variables, then the dependent variable has only one variable it can depend on so there is absolutely no point in calculating multiple regression. There are no other variables!
Dependent variable is the variable that can be measured. However, the independent variable is the variable that changes in the two groups.
The two variables graphed on a coordinate graph are typically referred to as the independent variable and the dependent variable. The independent variable is plotted on the x-axis, while the dependent variable is plotted on the y-axis. This arrangement allows you to observe how changes in the independent variable affect the dependent variable.
An alternating function is a function in which the interchange of two independent variables changes the sign of the dependent variable.
Dependent and Independent variables
The two types of variables in an experiment are independent variables, which are controlled by the experimenter and can be manipulated, and dependent variables, which are the outcome or response that is measured in the experiment and may change in response to the independent variable.
In a hypothesis, variables are typically classified into two main types: independent and dependent variables. The independent variable is the one that is manipulated or controlled to observe its effect on the dependent variable, which is the outcome being measured. Additional variables, such as controlled variables, may also be included to minimize the impact of extraneous factors. Together, these variables help structure an experiment or study to test the validity of the hypothesis.
You can have many dependent variables. If you measure the length, width and height of a solid block of metal and have temperature as the changing variable; the length, width, and height can be the dependent variables.
The F-variate, named after the statistician Ronald Fisher, crops up in statistics in the analysis of variance (amongst other things). Suppose you have a bivariate normal distribution. You calculate the sums of squares of the dependent variable that can be explained by regression and a residual sum of squares. Under the null hypothesis that there is no linear regression between the two variables (of the bivariate distribution), the ratio of the regression sum of squares divided by the residual sum of squares is distributed as an F-variate. There is a lot more to it, but not something that is easy to explain in this manner - particularly when I do not know your knowledge level.
If there are only two variables, then the dependent variable has only one variable it can depend on so there is absolutely no point in calculating multiple regression. There are no other variables!
Dependent variable is the variable that can be measured. However, the independent variable is the variable that changes in the two groups.
The main advantage is that it allows you to see how different dependent variables change according to changes in the same "independent" variable. It is relatively simple to use two vertical axes for the dependent variables, but the degree to which the two axes relate to one another is arbitrary. Furthermore, if the ranges of the dependent variables are very different the chart becomes unreadable.
It can have as many as it needs. You can even change different variables at the same time and study their individual influence with proper statistical tools in many type of experiments.
dependent
A functional relation can have two or more independent variables. In order to analyse the behaviour of the dependent variable, it is necessary to calculate how the dependent varies according to either (or both) of the two independent variables. This variation is obtained by partial differentiation.
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.