Best Answer

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!

Q: What statistical test to run when comparing the effects of two dichotomous variables on a dependent variable Anova Manova Why would you choose it?

Write your answer...

Submit

Still have questions?

Continue Learning about Math & Arithmetic

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.

Related questions

To choose the appropriate statistical test, the following four question must be answered; What are your dependent and independent variables? What is scale of measurement of the variables? How many groups/samples are there in the study? Have I have met the assumptions of the statistical test?

dichotomous variables

Dichotomous means "having only two possible values." Examples of dichotomous variables are yes/no or male/female.

Yes. You can do it

one dependent and one or more independent variables are related.

levels of variables important in statistical analysis?

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.

In a statistical model, variations in the dependent variable can be attributed to independent variables. However, there is a random element that is not accounted for and this is the stochastic error.

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.

Independent and dependent are types of variables. These variables are used mostly in science and math. When using independent variables you can control them dependent variables you cannot.

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.