• When is multiple regression used? • describe the relationship between the coef
ID: 3363251 • Letter: #
Question
• When is multiple regression used?• describe the relationship between the coefficient of determination and the coefficient of alienation. What do these coefficients tell us?
• Regression is a special type of ______________ .
• When adding predictors to a regression equation, the two predictors should be __________ to each other.
• When adding predictors to a regression equation, the two predictors should be ______________ to the criterion available. • When is multiple regression used?
• describe the relationship between the coefficient of determination and the coefficient of alienation. What do these coefficients tell us?
• Regression is a special type of ______________ .
• When adding predictors to a regression equation, the two predictors should be __________ to each other.
• When adding predictors to a regression equation, the two predictors should be ______________ to the criterion available.
• describe the relationship between the coefficient of determination and the coefficient of alienation. What do these coefficients tell us?
• Regression is a special type of ______________ .
• describe the relationship between the coefficient of determination and the coefficient of alienation. What do these coefficients tell us?
• Regression is a special type of ______________ .
• When adding predictors to a regression equation, the two predictors should be __________ to each other.
• When adding predictors to a regression equation, the two predictors should be ______________ to the criterion available.
Explanation / Answer
a) When is multiple regression used?
Multiple regression is an extension of simple Linear regression. It is used when we want to predict the value of a variable based on the value of two or more other variables. The variable we want to predict is called the dependent variable (or sometimes, the outcome, target or criterion variable).
b) describe the relationship between the coefficient of determination and the coefficient of alienation. What do these coefficients tell us?
The squared coefficient of correlation gives the proportion of common variance between two variables. It is also called the coefficient of determination. For example, the coefficient of determination is equal to r 2 W.Y = .25.
The proportion of variance not shared between the variables is called the coefficient of alienation, for example, it is equal to 1r 2 W.Y = .75 .
A coefficient of correlation equals to +1 or 1 indicates that a plot of the observations will show that they are positioned on a line. The coefficient of alienation (a.k.a., coefficient of non-determination) represents the proportion of variance in the dependent variable that is not accounted for by the independent variable(s). It is the coefficient of determination's counterpart. It is estimated by 1 - r2. Effectively, it is a measure of the non-association between two variables.
c) Regression is a special type of ______________ .
Regression is a set of statistical processes for estimating the relationships among variables.
d) When adding predictors to a regression equation, the two predictors should be __________ to each other.
the two predictorsshould be Independent to each other.
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