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24 Cards in this Set
- Front
- Back
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Descriptive statistics
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statistics that summarize or describe important characteristics of a set of data.
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Inferential statistics
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uses sample data to make inferences about a population.
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CVDOT
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center, variance, distribution, outliers, time.
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Measure of center
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the value at the center or middle of a data set.
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Arithmetic mean
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the measure of center found by adding the values and dividing the total by the number of values. AKA mean.
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Mean formula
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Σx
---- n |
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Median
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the measure of center that is the middle value when the original values are arranged in order of increasing (or decreasing) magnitude.
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Mode
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the value that occurs most frequently.
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Midrange
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the measure of center that is the value midway between the maximum and minimum values in the original data set.
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Midrange formula
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max + min
---------------- 2 |
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Round-off rule
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Carry one more decimal place than is present in the original set of values.
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Mean from frequency distribution formula
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Σ(f * x)
---------- Σf f = frequency x = class midpoint |
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Weighted mean
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computed with different values assigned different weights.
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Weighted mean formula
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Σ( w * x)
------------ Σw w = weight x = value |
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Skewed
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when a distribution is not symmetric and extends more to one side or the other.
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Symmetric
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when the left half of a histogram is a mirror image of the right half.
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Trimmed mean
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the deletion of a certain percentage of values from the top and bottom.
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Range
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the difference between the maximum value and the minimum value.
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Standard deviation
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a measure of variation of values about the mean.
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Variance
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a measure of variation equal to the square of the standard deviation.
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Empirical rule
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for data sets having a distribution that is approximately bell-shaped, the following properties apply:
* 68% fall within 1 std devs * 95% fall within 2 std devs * 99.7% fall within 3 std devs |
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Chebyshev's Theorem
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The proportion (or fraction) of any set of data lying within K std devs is always at least 1 - 1/K^2 where K > 1
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Coefficient of variation
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(CV) expressed as a percentage, describes the std dev relative to the mean.
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z-score
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(AKA standardized value) the number of std devs that a given value x is above or below the mean.
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