In a normal distribution, how are the mean, median, and mode related?
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Symmetry and unimodality place all three measures at the center.
This equality is a characteristic of the ideal normal model.
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Symmetry and unimodality place all three measures at the center.
This equality is a characteristic of the ideal normal model.
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The normal density is symmetric around its mean and has one central peak.
Its tails decrease smoothly in both directions.
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A normal distribution is specified by a location parameter and a scale parameter.
These correspond to its mean and standard deviation.
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Repeated simulated trials approximate the event's long-run frequency.
Accuracy generally improves as the number of independent trials increases.
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A quantile function inverts the CDF.
For example, qnorm(0.975) gives the normal value with 97.5 percent below it.
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The p-prefix evaluates the cumulative distribution function.
For example, pnorm(x) gives the normal probability at or below x.
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The d-prefix evaluates a density for continuous distributions or mass for discrete ones.
Prefixes p, q, and r correspond to CDF, quantile, and random generation.
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Functions beginning with r generate random variates in R's distribution naming convention.
runif() produces draws from a uniform distribution.
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rnorm() generates normal random variates, and the first argument is the number requested.
Default mean and standard deviation are 0 and 1.
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After each draw, the item is returned to the sampling pool.
Draws therefore remain possible for all items at every step.
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A fixed seed lets collaborators recreate the same simulated sample.
This supports verification and diagnosis of computational workflows.
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Computer generators use algorithms and an internal state to produce random-like sequences.
Because they are deterministic, a seed can reproduce them.