Chapter Summary

A normal random variable has a bell-shaped probability distribution. The two parameters of a normal random variable are its mean μ and its standard deviation σ. A standardized normal random variable, often denoted by Z, has mean 0 and standard deviation 1. The Central Limit Theorem offers one reason why this distribution appears so often in practice. The Empirical Rule relies on probabilities from the normal distribution. A normal model for data matches the parameters of a normal random variable to the mean and standard deviation of the data. The normal quantile plot allows us to check how well a normal model describes the variation in data. The skewness and kurtosis are summary statistics that measure the symmetry and presence ...

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