Statistics: Data Description
- Characteristic or measure obtained from a sample
- Characteristic or measure obtained from a population
- Sum of all the values divided by the number of values. This can either be a population
mean (denoted by mu) or a sample mean (denoted by x bar)
- The midpoint of the data after being ranked (sorted in ascending order). There are as
many numbers below the median as above the median.
- The most frequent number
- Skewed Distribution
- The majority of the values lie together on one side with a very few values (the tail) to
the other side. In a positively skewed distribution, the tail is to the right and the mean is
larger than the median. In a negatively skewed distribution, the tail is to the left and the
mean is smaller than the median.
- Symmetric Distribution
- The data values are evenly distributed on both sides of the mean. In a symmetric
distribution, the mean is the median.
- Weighted Mean
- The mean when each value is multiplied by its weight and summed. This sum is divided
by the total of the weights.
- The mean of the highest and lowest values. (Max + Min) / 2
- The difference between the highest and lowest values. Max - Min
- Population Variance
- The average of the squares of the distances from the population mean. It is the sum of
the squares of the deviations from the mean divided by the population size. The units
on the variance are the units of the population squared.
- Sample Variance
- Unbiased estimator of a population variance. Instead of dividing by the population size,
the sum of the squares of the deviations from the sample mean is divided by one less
than the sample size. The units on the variance are the units of the population squared.
- Standard Deviation
- The square root of the variance. The population standard deviation is the square root of
the population variance and the sample standard deviation is the square root of the
sample variance. The sample standard deviation is not the unbiased estimator for the
population standard deviation. The units on the standard deviation is the same as the
units of the population/sample.
- Coefficient of Variation
- Standard deviation divided by the mean, expressed as a percentage. We won't work
with the Coefficient of Variation in this course.
- Chebyshev's Theorem
- The proportion of the values that fall within k standard deviations of the mean is at least
where k > 1. Chebyshev's theorem can be applied to any distribution
regardless of its shape.
- Empirical or Normal Rule
- Only valid when a distribution in bell-shaped (normal). Approximately 68% lies within
1 standard deviation of the mean; 95% within 2 standard deviations; and 99.7% within 3
standard deviations of the mean.
- Standard Score or Z-Score
- The value obtained by subtracting the mean and dividing by the standard deviation.
When all values are transformed to their standard scores, the new mean (for Z) will be
zero and the standard deviation will be one.
- The percent of the population which lies below that value. The data must be ranked to
- Either the 25th, 50th, or 75th percentiles. The 50th percentile is also called the median.
- Either the 10th, 20th, 30th, 40th, 50th, 60th, 70th, 80th, or 90th percentiles.
- Lower Hinge
- The median of the lower half of the numbers (up to and including the median). The
lower hinge is the first Quartile unless the remainder when dividing the sample size by
four is 3.
- Upper Hinge
- The median of the upper half of the numbers (including the median). The upper hinge is
the 3rd Quartile unless the remainder when dividing the sample size by four is 3.
- Box and Whiskers Plot (Box Plot)
- A graphical representation of the minimum value, lower hinge, median, upper hinge, and
maximum. Some textbooks, and the TI-82 calculator, define the five values as the
minimum, first Quartile, median, third Quartile, and maximum.
- Five Number Summary
- Minimum value, lower hinge, median, upper hinge, and maximum.
- InterQuartile Range (IQR)
- The difference between the 3rd and 1st Quartiles.
- An extremely high or low value when compared to the rest of the values.
- Mild Outliers
- Values which lie between 1.5 and 3.0 times the InterQuartile Range below the 1st
Quartile or above the 3rd Quartile. Note, some texts use hinges instead of Quartiles.
- Extreme Outliers
- Values which lie more than 3.0 times the InterQuartile Range below the 1st Quartile or
above the 3rd Quartile. Note, some texts use hinges instead of Quartiles.
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