Constructing Confidence Intervals In Exercises 13–24, assume the sample is from a normally distributed population and construct the indicated confidence intervals for (a) the population variance σ^2. Interpret the results.
Drug Concentration The times (in minutes) for the drug concentration to peak when the drug epinephrine is injected into 15 randomly selected patients are listed. Use a 90% level of confidence.

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Step 1: Calculate the sample variance (s^2). First, compute the sample mean (x̄) by summing all the data points and dividing by the number of observations (n = 15). Then, use the formula for variance: s^2 = (Σ(xi - x̄)^2) / (n - 1), where xi represents each data point.

Step 2: Identify the chi-square critical values for the given confidence level (90%) and degrees of freedom (df = n - 1 = 14). Use a chi-square distribution table or calculator to find the values for χ^2_lower and χ^2_upper.

Step 3: Construct the confidence interval for the population variance (σ^2) using the formula: [(n - 1) * s^2 / χ^2_upper, (n - 1) * s^2 / χ^2_lower]. This formula uses the sample variance and the chi-square critical values.

Step 4: To interpret the results, note that the confidence interval provides a range of plausible values for the population variance. A 90% confidence level means that if we were to repeat this process many times, 90% of the intervals would contain the true population variance.

Step 5: If needed, you can also calculate the confidence interval for the population standard deviation (σ) by taking the square root of the lower and upper bounds of the variance confidence interval.

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Confidence Interval

A confidence interval is a range of values, derived from sample statistics, that is likely to contain the true population parameter with a specified level of confidence. For example, a 90% confidence interval suggests that if we were to take many samples and construct intervals in the same way, approximately 90% of those intervals would contain the true population parameter.

Population Variance (σ²)

Population variance is a measure of the dispersion of a set of values in a population. It quantifies how much the values in the population differ from the population mean. In the context of confidence intervals, estimating the population variance is crucial for determining the width of the interval, as it affects the standard error of the mean.

Normal Distribution

Normal distribution is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. Many statistical methods, including the construction of confidence intervals, assume that the underlying population is normally distributed, especially when sample sizes are small.