Given data:
Scott
[tex]\begin{gathered} Sample\text{ mean (}\mu_1)=\text{ \$93,430} \\ S.d\text{ (}\sigma_1)=\text{ \$5602} \\ Sample-size(n_1\text{) = 35} \end{gathered}[/tex]
Ligonier
[tex]\begin{gathered} Sample\text{ mean (}\mu_2)=\text{ \$98,}043 \\ S.d\text{ (}\sigma_2)=\text{ \$4731} \\ Sample-size(n_2\text{) = 4}0 \end{gathered}[/tex]
step 1: State the null hypothesis (H₀) and alternative hypothesis (H1)
[tex]\begin{gathered} H_0=\mu_1-\mu_2=0 \\ H_1=\mu_1-\mu_2\ne0 \end{gathered}[/tex]
step 2: Find the critical value with a significance σ= 0.01
The critical value- two-tailed is: -2.576 and 2.576
Note that,
[tex]If,z>2.576\text{ or }z<-2.576\text{ },\text{ reject the null hypothesis}[/tex]
step 3:
Find the z-score
[tex]\begin{gathered} z=\frac{(X_1-X_2)-(\mu_1-\mu_2)}{\sqrt{\frac{\sigma^2_1}{n_1}+\frac{\sigma^2_1}{n_1}}} \\ z=\frac{(93,430-98,043)-0}{\sqrt[]{\frac{5602}{35}^2+\frac{4731}{40}^2_{}}} \\ z=\frac{-4613}{1206.73} \\ z=-3.823 \end{gathered}[/tex]
step 4: Since, z= -3.823 < -2.576 (critical value)
Therefore, we reject the null hypothesis - we can conclude that there is enough evidence to reject the claim that the average cost of a home in both locations is the same.
There is a significant difference in house prices.