Respuesta :
Answer:
[tex] y = \beta_1 x + \beta_o[/tex]
With [tex]\beta_1 [/tex] representing the slope we have that:
[tex] \hat \beta_1 = -0.0437[/tex]
[tex]\hat \beta_o = 7.5[/tex]
And we are interest on this case the interpretation about the slope and we can conclude that:
For every unit increase in literacy rate (percent of the population that is literate) the age difference (husband minus wife age) falls by 0.0437 units, on average.
Step-by-step explanation:
For this case we have that the regression model adjusted between age difference (husband minus wife age) representing the y variable and literacy rate (percent of the population that is literate) representing the variable x is given by:
[tex] y= -0.0437 x + 7.5[/tex] where [tex] 17 \leq x\leq 100[/tex]
And we know that the method used in order to adjust the regression line was least squares.
For this case our dependent variable is y = age difference (husband minus wife age) and the independent variable is x=literacy rate (percent of the population that is literate)
If we compare the regression model adjusted with the linear regression model:
[tex] y = \beta_1 x + \beta_o[/tex]
With [tex]\beta_1 [/tex] representing the slope we have that:
[tex] \hat \beta_1 = -0.0437[/tex]
[tex]\hat \beta_o = 7.5[/tex]
And we are interest on this case the interpretation about the slope and we can conclude that:
For every unit increase in literacy rate (percent of the population that is literate) the age difference (husband minus wife age) falls by 0.0437 units, on average.