Use the sample data and confidence level to construct the confidence interval estimate of the population proportion p.

n=500, x=350, 90% confidence

?<p<? (Round to three decimal places as needed.)

To construct the confidence interval estimate of a population proportion, you can use the following formula:

Confidence Interval = p̂ ± Z * √(p̂(1-p̂)/n)

Where:
- p̂ is the sample proportion (x/n)
- Z is the Z-score corresponding to the desired level of confidence
- n is the sample size

In this case, the sample size (n) is 500, the sample proportion (p̂) is 350/500 = 0.7, and the confidence level is 90%.

To find the Z-score corresponding to a 90% confidence level, you can use a standard normal distribution table or a statistical calculator. The Z-score for a 90% confidence level is approximately 1.645.

Now, plug the values into the formula:

Confidence Interval = 0.7 ± 1.645 * √(0.7(1-0.7)/500)

Calculating the square root and simplifying the formula:

Confidence Interval = 0.7 ± 1.645 * √(0.21/500)

Confidence Interval = 0.7 ± 1.645 * 0.020727

Confidence Interval = 0.7 ± 0.03407

Therefore, the confidence interval estimate of the population proportion (p) is 0.666 < p < 0.734 (rounded to three decimal places).

Proportional confidence interval formula:

CI90 = p ± (1.645)[√(pq/n)]
...where p = x/n, q = 1 - p, and n = sample size.
Note: ± 1.645 represents 90% confidence interval.

For p in your problem: 350/500
For q: 1 - p = q
n = 500

I let you take it from here to calculate the interval. (Note: convert all fractions to decimals.)