Consider the Following Multiple Regression Models (A)to (D)below $\widehat { \operatorname { Earn } _ { i } } = \widehat { \beta _ { 0 } } + \widehat { \beta } _ { 1 }$

Consider the following multiple regression models (a)to (d)below.DFemme = 1 if the individual is a female, and is zero otherwise; DMale is a binary variable which takes on
The value one if the individual is male, and is zero otherwise; DMarried is a binary
Variable which is unity for married individuals and is zero otherwise, and DSingle is (1-
DMarried).Regressing weekly earnings (Earn)on a set of explanatory variables, you will
Experience perfect multicollinearity in the following cases unless: a. $\widehat { \operatorname { Earn } _ { i } } = \widehat { \beta _ { 0 } } + \widehat { \beta } _ { 1 }$ DFemme $+ \widehat { \beta _ { 2 } }$ Dmale $+ \widehat { \beta _ { 3 } } X _ { 3 i }$ .
b. $\widehat { \text { Earn } _ { i } } = \widehat { \beta } _ { 0 } + \widehat { \beta } _ { 1 }$ DMarried $+ \widehat { \beta } _ { 2 }$ DSingle $+ \widehat { \beta } _ { 3 } X _ { 3 i }$ .
c. $\widehat { \operatorname { Earn } _ { i } } = \widehat { \beta _ { 0 } } + \widehat { \beta } _ { 1 }$ DFemme $+ \widehat { \beta } _ { 3 } X _ { 3 i }$ .