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# mean

1. ### P1.T2.20.13. Coskewness and cokurtosis

Learning objectives: Use sample data to estimate quantiles, including the median. Estimate the mean of two variables and apply the CLT. Estimate the covariance and correlation between two random variables. Explain how coskewness and cokurtosis are related to skewness and kurtosis. Questions...
2. ### P1.T2.20.11. Sample moments and bias

Learning objectives: Estimate the mean, variance, and standard deviation using sample data. Explain the difference between a population moment and a sample moment. Distinguish between an estimator and an estimate. Describe the bias of an estimator and explain what the bias measures. Questions...
3. ### P1.T2.712. Skew, kurtosis, coskew and cokurtosis (Miller, Chapter 3)

Learning objectives: Describe the four central moments of a statistical variable or distribution: mean, variance, skewness, and kurtosis. Interpret the skewness and kurtosis of a statistical distribution, and interpret the concepts of coskewness and cokurtosis. Describe and interpret the best...
4. ### P1.T2.711. Covariance and correlation (Miller, Ch.3)

Learning objectives: Calculate and interpret the covariance and correlation between two random variables. Calculate the mean and variance of sums of variables. Questions: 711.1. The following probability matrix displays joint probabilities for an inflation outcome, I = {2, 3, or 4}, and an...
5. ### P1.T2.710. Mean and standard deviation (Miller, Ch.3)

Learning objectives: Interpret and apply the mean, standard deviation, and variance of a random variable. Calculate the mean, standard deviation, and variance of a discrete random variable. Interpret and calculate the expected value of a discrete random variable. Questions: 710.1. The...