Final answer:
The mean and 90 could refer to calculating a 90th percentile or a 90 percent confidence interval. The 90th percentile is the value below which 90 percent of data lies, determined using the invNorm function with mean and standard deviation. Ninety percent confidence intervals imply that 90 percent of these intervals from repeated sampling would contain the true population mean.
Step-by-step explanation:
To calculate the mean and 90, assuming 90 refers to a percentile or a confidence interval, we need to follow different processes. Let's address the calculation of a value that is two standard deviations above the expected value, or mean. The notion of 'expected value' often implies the mean of a distribution. In normal distributions, two standard deviations above the mean roughly corresponds to the 97.5th percentile (if the distribution is symmetrical). However, we don't have sufficient context or numerical data to calculate an actual value here.
With regards to confidence intervals and percentiles, a 90th percentile indicates the value below which 90 percent of the data falls. Assuming we are given a mean (μ), standard deviation (σ), and sample size (n), the 90th percentile can be calculated using the formula invNorm(0.90, μ, σ). For example, with a mean age sum of 2008.5 and a standard deviation of 72.56 for a sample of 65 people, the 90th percentile would be calculated as invNorm(0.90,2008.5,72.56), which equals 2101.5 years.
When discussing 90 percent confidence intervals, we anticipate that 90 percent of such intervals constructed from repeated sampling would include the true population mean. If we hypothetically calculate a multitude of these intervals, we would expect 90 out of 100 to contain the true mean.