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.
In the context of regression, it is the y-intercept: the value of the dependent variable when the independent is zero.
regression analysis
Multiple regression analysis in statistical modeling is used to examine the relationship between multiple independent variables and a single dependent variable. It helps to understand how these independent variables collectively influence the dependent variable and allows for the prediction of outcomes based on the values of the independent variables.
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 dependent variable is dependent on the independent variable, so when the independent variable changes, so does the dependent 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.
The Independent variable is the one you control. The dependent variable is controlled by the Independent Variable.