Respuesta :
Answer:
At a significance level of 0.05, there is enough evidence to claim that there is a significant relationship between x and y.
P-value = 0.003.
Step-by-step explanation:
If we perform a regression analysis relating x and y, we get the best fitting line with equation:
[tex]y=15.82x+92.9[/tex]
and a correlation coefficient r:
[tex]r=0.693[/tex]
We have to test the hypothesis, where the alternative hypothesis claims that there is a relationship between these two variables, and the null hypothesis claiming there is no relationship (meaning that the correlation is not significantly different from 0).
This can be written as:
[tex]H_0: \rho=0\\\\H_a:\rho\neq0[/tex]
where ρ is the population correlation coefficient for x and y.
The significance level is assumed to be 0.05.
The sample size is n=16.
The degrees of freedom are df=14.
[tex]df=n-2=16-2=14[/tex]
The test statistic can be calculated as:
[tex]t=\dfrac{r\sqrt{n-2}}{\sqrt{1-r^2}}=\dfrac{0.693\sqrt{14}}{\sqrt{1-(0.693)^2}}=\dfrac{2.593}{0.721}=3.597[/tex]
For a test statistic t=2.05 and 14 degrees of freedom, the P-value is calculated as:
[tex]\text{P-value}=2\cdot P(t>3.597)=0.003[/tex]
The P-value (0.003) is smaller than the significance level (0.05), so the effect is significant enough.
The null hypothesis is rejected.
At a significance level of 0.05, there is enough evidence to claim that there is a significant relationship between x and y.
