What is the support of a standard exponential waiting-time model?
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Waiting times cannot be negative, so the exponential distribution begins at zero.
Its density extends over positive real values.
Practice Data Visualization questions with answers and explanations.
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Waiting times cannot be negative, so the exponential distribution begins at zero.
Its density extends over positive real values.
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Subtracting the mean centers the observation, and dividing by the standard deviation scales it.
The result is unitless and supports cross-scale comparison.
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Very wide bins can hide modes and gaps, while very narrow bins can emphasize noise.
Analysts should examine reasonable alternative widths.
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Heat maps map matrix cells to colors representing magnitude.
They are useful for patterns across two categorical or ordered dimensions.
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Variance scales with the square of the multiplicative constant.
Multiplying values by 3 multiplies squared deviations by 3² = 9.
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A PMF describes a discrete random variable by listing outcome probabilities.
The probabilities are nonnegative and sum to one.
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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.
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Conditional on survival to the present, the future waiting distribution is unchanged.
This special property follows from a constant hazard rate.
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The box extends from the first to the third quartile.
The internal line marks the second quartile, which is the median.
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Bar charts encode category counts or values with separated bars.
Separation emphasizes that the categories are distinct rather than continuous intervals.
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The rows and columns can represent the two categorical dimensions.
Cell color then communicates the associated count, rate, or summary.
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Adding a constant shifts the entire distribution without changing relative distances.
Measures of spread therefore remain the same.