In the regression model $Y _ { i } = \beta _ { 0 } + \beta _ { 1 } X _ { i } + \beta _ { 2 } D _ { i } + \beta _ { 3 } \left( X _ { i } \times D _ { i } \right) + u _ { i }$ where X is a continuous variable and D is a binary variable, $\beta _ { 3 }$
A) indicates the slope of the regression when D=1 .
B) has a standard error that is not normally distributed even in large samples since D is not a normally distributed variable.
C) indicates the difference in the slopes of the two regressions.
D) has no meaning since $\left( X _ { i } \times D _ { i } \right) = 0 \text { when } D _ { i } = 0 \text {. }$

Question

In the regression model $Y _ { i } = \beta _ { 0 } + \beta _ { 1 } X _ { i } + \beta _ { 2 } D _ { i } + \beta _ { 3 } \left( X _ { i } \times D _ { i } \right) + u _ { i }$ where  X  is a continuous variable and  D  is a binary variable, $\beta _ { 3 }$
A) indicates the slope of the regression when  D=1 .
B) has a standard error that is not normally distributed even in large samples since  D  is not a normally distributed variable.
C) indicates the difference in the slopes of the two regressions.
D) has no meaning since $\left( X _ { i } \times D _ { i } \right) = 0 \text { when } D _ { i } = 0 \text {. }$

Quizplus · Accepted Answer

The Answer of In the regression model $Y _ { i } = \beta _ { 0 } + \beta _ { 1 } X _ { i } + \beta _ { 2 } D _ { i } + \beta _ { 3 } \left( X _ { i } \times D _ { i } \right) + u _ { i }$ where  X  is a continuous variable and  D  is a binary variable, $\beta _ { 3 }$
A) indicates the slope of the regression when  D=1 .
B) has a standard error that is not normally distributed even in large samples since  D  is not a normally distributed variable.
C) indicates the difference in the slopes of the two regressions.
D) has no meaning since $\left( X _ { i } \times D _ { i } \right) = 0 \text { when } D _ { i } = 0 \text {. }$

In the Regression Model Yi=β0+β1Xi+β2Di+β3(Xi×Di)+uiY _ { i } = \beta _ { 0 } + \beta _ { 1 } X _ { i } + \beta _ { 2 } D _ { i } + \beta _ { 3 } \left( X _ { i } \times D _ { i } \right) + u _ { i }Yi​=β0​+β1​Xi​+β2​Di​+β3​(Xi​×Di​)+ui​

In the Regression Model $Y _ { i } = \beta _ { 0 } + \beta _ { 1 } X _ { i } + \beta _ { 2 } D _ { i } + \beta _ { 3 } \left( X _ { i } \times D _ { i } \right) + u _ { i }$