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used to predict the dependent variable
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.
True.
regression analysis
In the context of regression, it is the y-intercept: the value of the dependent variable when the independent is zero.
I want to develop a regression model for predicting YardsAllowed as a function of Takeaways, and I need to explain the statistical signifance of the model.
The independent variable is the variable that is altered by the scientist, and the dependent variable's value is dependent on the value of the independent variable.
The multiple regression statistical method examines the relationship of one dependent variable (usually represented by 'Y') and one independent variable (represented by 'X').
Simple regression is used when there is one independent variable. With more independent variables, multiple regression is required.
and independent variable is bigger than a dependent variable
The dependent variable is dependent on the independent variable, so when the independent variable changes, so does the dependent variable.
ControlThe answer will depend on the nature of the effect. IFseveral requirements are met (the effect is linear, the "errors" are independent and have the same variance across the set of values that the independent variable can take (homoscedasticity) then, and only then, a linear regression is a standard. All to often people use regression when the data do not warrant its use.