Which of the following is true of the t-statistic?
It is the value of the coefficient estimate divided by its standard error.
It is the coefficientâs standard error normalized to lie between 0 and 1.
It tells us how many standard errors the coefficient estimate is equal to zero.
It measures the overall statistical validity of the regression equation.
It is the sum of the value of the coefficient estimate and its standard error.
The standard error of the regression:
is equal to the slope of the regression equation.
measures the explanatory power of the regression equation and lies between 0 and 1.
is equal to the sum of squared errors minus the total sum of squares.
measures the explained variation in the dependent variable.
measures the unexplained variation in the dependent variable.
A regression analysis is said to suffer from multicollinearity when:
the dependent and independent variables move in the same direction.
the degrees of freedom in the regression is equal to zero.
the explanatory variables vary independently of one another.
the correlation coefficient between the predicted and the explanatory variables is equal to zero.
two or more explanatory variables tend to move together.
Which of the following is true of the t-statistic?
It is the value of the coefficient estimate divided by its standard error.
It is the coefficientâs standard error normalized to lie between 0 and 1.
It tells us how many standard errors the coefficient estimate is equal to zero.
It measures the overall statistical validity of the regression equation.
It is the sum of the value of the coefficient estimate and its standard error.
The standard error of the regression:
is equal to the slope of the regression equation.
measures the explanatory power of the regression equation and lies between 0 and 1.
is equal to the sum of squared errors minus the total sum of squares.
measures the explained variation in the dependent variable.
measures the unexplained variation in the dependent variable.
A regression analysis is said to suffer from multicollinearity when:
the dependent and independent variables move in the same direction.
the degrees of freedom in the regression is equal to zero.
the explanatory variables vary independently of one another.
the correlation coefficient between the predicted and the explanatory variables is equal to zero.
two or more explanatory variables tend to move together.