1. The hypothesis test for the coefficient(s) in a linear regression model is that: a. HO: The sample coefficient is equal to zero. / HA: the sample coefficient is not equal to zero. b. HO: The population coefficient is equal to one. / HA: the population coefficient is not equal to one. c. HO: The entire model is non-significant. / HA: the entire model is statistically significant. d. HO: The population coefficient is equal to zero. / HA: the population coefficient is not equal to zero.Below is a scatter plot and the linear regression model statistics for a model using husband height and wife height as the two variables. Use this output to answer the following questions. 70 3; O) .l: E :E 65 0 . g :3 . Ina" ' I Estimate Std. Error 1 value Fr(>|t|} '5 ' - (Intercept) 43.5755 4.6842 0.30 0.0000 g 60 " ' heightJ'iusband 0.2353 0.0686 4.17 0.0000 s 55 I i i so 65 70 7'5 Husband's height {in inches) 1. The explanatory or independent variable in this model is: a. Husband's height b. Wife's height 2. The "height_husband" coefficient can be explained as: a. A one inch increase in husbands height indicates an average 0.2863 increase in wife's height b. A 0.2863 inch increase in husband's height indicates an average 1 inch increase in wife's height 0. A 0.2863 inch increase in wife's height indicates an average 1 inch increase in husband' height d. A one inch increase in wife's height indicates an average 0.2863 increase in husband's height 3. If we reversed the independent and dependent variables, the coefficient value would be the same: a. True b. False 4. If we reversed the independent and dependent variables, the coefficient would no longer be statistically significant: a. True b. False