Module 2: Textbook Flashcards
frequency table
In terms of levels
a table showing levels of a variable together with the frequency associated with each level.
Bimodal
A bimodal distribution is one with two equal or approximately equally high value frequencies. Note that a distribution with two distinct peaks is not considered bimodal unless the peaks are very nearly equal.
Multimodal
A multimodal distribution is one with multiple, approximately equal peaks. A bimodal distribution could be considered as a special case of a multimodal distribution.
Skewness in data is a result of
Having outliers in the data, i.e., data values that have the effect of extending the tail of the distribution in one direction or the other.
Negative skew happens when the mass of the distribution is concentrated on the —- of the figure. It has relatively few — values. The distribution is said to be —skewed or negatively skewed.
- right
- low
- left
Positive skew happens when the mass of the distribution is concentrated on the — of the figure. It has relatively few —- values. The distribution is —skewed or positively skewed.
- left
- high
- right
Skewness can also be a result of either a——
floor effect or a ceiling effect
A floor effect (3)
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occurs when values pile up against some lower limit, resulting in a positive skew, for example, as in the case of an exam that is too difficult for the target population.
A ceiling effect
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occurs when values pile up against some upper limit, resulting in a negative skew, for example, as in the case of an exam that is too easy so that most testers score very high.
In the example above, notice how the —- and — are “pulled” to one side of the peak in the direction of the skew. Both are affected by extreme values in the distribution; the — is the most sensitive to outliers, or extreme values, in a distribution.
- median
- mean
- mean
Explain how to determine the Mean of a Grouped Data Set
The two most-used measures of dispersion, or variability, are the ——
variance and the standard deviation.
If the entire population of values is given, deviation is represented as
(x – μ), the distance between a raw value x and the population mean, mu
If the distribution is a subset of values, as in a sample, deviation is represented as
(x – x̅), the distance between a raw value x and the sample mean, x-bar
The sum of deviations from the mean in a distribution always equals —-. Because —-
- zero
- since the mean is the average of the values in the distribution, the differences between the mean and values above the mean will be offset by the differences below the mean.