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Confidence interval for the mean daily return if it is normally distributed:
⁻x [tex]-z_{\alpha }[/tex](σ/[tex]\sqrt{n}[/tex]) ≤ μ ≤ ⁻x + [tex]z_{\alpha }[/tex] ( σ/[tex]\sqrt{n}[/tex])
Based on the Central Limit Theorem's result that the sampling distribution of the sample means follows an essentially normal distribution, a confidence interval for a population mean is calculated when the population standard deviation is known.
Take into account the standardising equation for the sampling distribution introduced in the Central Limit Theorem discussion:
[tex]z_{1} =[/tex](⁻x - μ₋ₓ) /( σ ⁻x) = (⁻x - μ) /( σ/[tex]\sqrt{n}[/tex])
Notice that µ is substituted for µx− because we know that the expected value of µx− is µ from the Central Limit theorem and σx− is replaced with σn√/, also from the Central Limit Theorem.
In this formula we know X−, σx− and n, the sample size. (In actuality we do not know the population standard deviation, but we do have a point estimate for it, s, from the sample we took. More on this later.) What we do not know is μ or Z1. We can solve for either one of these in terms of the other. Solving for μ in terms of Z1 gives:
μ=X−±Z1 σ/[tex]\sqrt{n}[/tex]
Remembering that the Central Limit Theorem tells us that the distribution of the X¯¯¯'s, the sampling distribution for means, is normal, and that the normal distribution is symmetrical, we can rearrange terms thus:
⁻x [tex]-z_{\alpha }[/tex](σ/[tex]\sqrt{n}[/tex]) ≤ μ ≤ ⁻x + [tex]z_{\alpha }[/tex] ( σ/[tex]\sqrt{n}[/tex])
This is the formula for a confidence interval for the mean of a population.
Learn more about confidence interval here: https://brainly.com/question/13242669
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