1. Nicole Seaman

    P1.T2.20.12. BLUE estimators, Law of large numbers (LLN), and central limit theorem (CLT)

    Learning objectives: Explain what is meant by the statement that the mean estimator is BLUE. Describe the consistency of an estimator and explain the usefulness of this concept. Explain how the Law of Large Numbers (LLN) and Central Limit Theorem (CLT) apply to the sample mean. Estimate and...
  2. Nicole Seaman

    YouTube T2-12 The p value is the exact significance level

    The p value is the area in the rejection region(s). In this example, we observe a sample mean of +15 bps and our null hypothesis is that the "true" population mean is zero. The corresponding p value of 2.36% is the exact (i.e., lowest) significance level at which we can reject the null. Put...
  3. Nicole Seaman

    YouTube T2-10 Test of sample mean (Confidence interval, test statistic and p-value)

    The explores the answer to Miller's EOC Question #2: "You are given the following sample of annual returns for a portfolio manager. If you believe that the distribution of returns has been stable over time and will continue to be stable over time, how confident should you be that the portfolio...
  4. FlorenceCC

    Miller - variance of the sample mean vs. sample variance

    Hello, I was reading about the Central Limit Theorem today, in the study notes for Miller chapter 4 (p79 specifically), and I realized that I am unclear about the following: (I) we indicate that the variance of each random variable is σ^2/n. As we have shown in the preceding ochapter, this is...
  5. Nicole Seaman

    P1.T2.718. Confidence in the mean and variance (Miller Ch.7)

    Learning objectives: Calculate and interpret the sample mean and sample variance. Construct and interpret a confidence interval. Construct an appropriate null and alternative hypothesis, and calculate an appropriate test statistic. Questions: For the following questions, please feel free to...