
In a normally distributed set of data, all three measures of central tendency are approximately the same. Using the data sample below, calculate the measures of central tendency.
{1, 3, 8, 3, 7, 11, 8, 3, 9, 10}
Mean (arithmetic)
Since most populations exhibit normality (bellshaped curve) or can be assumed to be normal, the mean is the most common measure for central tendency. It is used to describe normal data.
The formula is the summation of all the values divided by the sample size:
Sum of all values: 63
n: 10 samples
Mean = 6.3
In the example, the mean is of a sample, represented by xbar.
Median
The median is the midpoint, the middle value or observation of the data set. If the set of data has an even count, the median is the average of the middle two values. This is measure for skewed or nonnormal data.
Arrange the numbers in ascending or descending order:
{1, 3, 3, 3, 7, 8, 8, 9, 10, 11}
Since the sample is an even set of data (10 samples) and the middle two values are 7 and 8, then the average of the two middle values is 7.5.
Median = 7.5
Mode
The mode is the most commonly occurring value in the data set. Not commonly used as a measure of central location but can be found in the tallest bar of a vertical histogram chart.
Mode = 3, since it occurs more than any other value.
Measures of dispersion are numerical statistics which describe the spread of data or the width of the distribution.
For the Standard Deviation, Excel uses "n1"
in the denominator to calculate the sample statistics (the rows in
Excel for the data were in rows 316).
Return to DMAIC
Return to BASIC STATISTICS
Templates and Calculators
Subscribe to access all pages within this site
Return to SixSigmaMaterial Home Page
Six Sigma
Six Sigma Certification
Six Sigma Modules
Green Belt Program (1,000+ Slides)
Basic Statistics
SPC
Process Mapping
Capability Studies
MSA
Cause & Effect Matrix
FMEA
Multivariate Analysis
Central Limit Theorem
Confidence Intervals
Hypothesis Testing
T Tests
1Way Anova Test
ChiSquare Test
Correlation and Regression
Control Plan
Kaizen
Error Proofing