A. (0.714, 0.826)
Explanation:
Given that:
The sample size n = 150
The number of success(sample proportion) x = 115
The population proportion
=

The population proportion
=

The population proportion
= 0.767
At 90% confidence interval level;

Thus, the confidence interval estimate for the true population proportion can be computed by using the formula:







= (0.767 - 0.0568, 0.767 + 0.0568)
= (0.7102 , 0.8238 )
Due to approximation; the confidence interval estimate for the true population proportion is
(0.714, 0.826)