Respuesta :
Answer:
[tex]H_0: \beta_2 = 3\beta_1[/tex]
Explanation:
The null hypothesis is the hypothesis that there is no significant difference between specified populations and that any observed difference is due to sampling/experimental error.
We would like to test that one year of attending a university with regression parameter [tex]\beta_2[/tex] is worth 3 years of attending a junior college with regression parameter [tex]\beta_1[/tex] in terms of the wages earned.
In the model:[tex]log(wage)=\beta_0+\beta_1jc+\beta_2univ+\beta_3exper + u[/tex]
The null hypothesis will therefore be:
[tex]H_0: \beta_2 = 3\beta_1[/tex]
Answer:
[tex]H_{0} : \beta_{2} = 3 \beta_{1}[/tex]
Explanation:
A null hypothesis ([tex]H_{0})[/tex] is a hypothesis that says there is no statistical significance between the two variables. It is usually the hypothesis a researcher or experimenter will try to disprove or discredit.
An alternative hypothesis [tex](H_{a} )[/tex] is a hypothesis that states there is a statistically significant relationship between two variables.
In this example, the hypothesis to test is that one year of attending a university is worth 3 years of attending a two-year junior college
If [tex]\beta_{2} =[/tex] the number of years of attending a university, and
[tex]\beta_{1} =[/tex] the number of years of attending a junior college
Since 3 years of attending the junior college is equivalent to 1 year of attending the university, the null hypothesis is given by:
[tex]H_{0} : \beta_{2} = 3 \beta_{1}[/tex]
