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!
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Dichotomous means "having only two possible values." Examples of dichotomous variables are yes/no or male/female.
In cases wherethe dependent variable can take any numerical value for a given set of independent variables multiple regression is used.But in cases when the dependent variable is qualitative(dichotomous,polytomous)then logistic regression is used.In Multiple regression the dependent variable is assumed to follow normal distribution but in case of logistic regression the dependent variablefollows bernoulli distribution(if dichotomous) which means it will be only0 or 1.
A statistical technique used to eliminate variance in dependent variables caused by extraneous sources. In evaluation research, statistical controls are often used to control for possible variation due to selection bias by adjusting data for program and control group on relevant characteristics.
In a statistical model you have two kinds of variable. Response variables are the "outputs" of your model. Explanatory variables, on the other hand, are the "inputs" of your model. Response variables are dependent on the explanatory variables. Explanatory variable are independent of the response variables.Imagine you were trying to formulate a statistical model of your car's fuel economy. The "output" of your model is miles per gallon (or kilometres per litre). That's a dependent variable. "Inputs" into your model might be (for example) engine capacity, number of cylinders, tyre pressure, etc. These are your independent variables. That is, fuel economy may be, or is, (to be determined by the modelling) dependent on engine capacity and/or number of cylinders and/or tyre pressure, etc.
Every time the independent variables change, the dependent variables change.Dependent variables cannot change if the independent variables didn't change.