# Measures of Central Tendency | Engineering Data Analysis

Measures of Central Tendency

Measures of central tendency are statistical tools used to describe the center or typical value of a dataset. They provide a single value that attempts to describe the middle or center of a distribution of values. The three main measures of central tendency are:

• Mean
• Median
• Mode

Each of these measures has its own strengths and weaknesses, and their usefulness depends on the nature of the data and the purpose of the analysis.

The Arithmetic Mean

The arithmetic mean, commonly referred to as the average, is the sum of all values in a dataset divided by the number of values.

Formula

For a dataset with n values, the arithmetic mean (x̄) is calculated as:

x̄ = (x₁ + x₂ + … + xₙ) / n

Where x₁, x₂, …, xₙ are individual values in the dataset.

Properties

• The mean takes into account every value in the dataset.
• It’s sensitive to extreme values (outliers).
• It can be used with interval and ratio data.
• The sum of deviations from the mean is always zero.

• It uses all the data in its calculation.
• It’s suitable for further statistical calculations.
• It’s widely understood and commonly used.

• It can be skewed by outliers.
• It may not represent the typical value in skewed distributions.

Example

Dataset: 2, 4, 4, 5, 5, 7, 9 Mean = (2 + 4 + 4 + 5 + 5 + 7 + 9) / 7 = 36 / 7 = 5.14

The Median

The median is the middle value when a dataset is ordered from least to greatest.

Calculation

• For odd-numbered datasets: The median is the middle number.
• For even-numbered datasets: The median is the average of the two middle numbers.

Properties

• The median is not affected by extreme values (outliers).
• It can be used with ordinal, interval, and ratio data.
• 50% of the data falls below the median, and 50% falls above it.

• It’s not affected by extreme values.
• It’s useful for skewed distributions.
• It can be used when a distribution has open-ended classes.

• It doesn’t take into account every value in the dataset.
• It’s not suitable for many mathematical operations.

Example

Dataset: 2, 4, 4, 5, 5, 7, 9 Ordered: 2, 4, 4, 5, 5, 7, 9 Median = 5 (middle value)

The Mode

The mode is the value that appears most frequently in a dataset.

Properties

• A dataset can have no mode, one mode (unimodal), two modes (bimodal), or more than two modes (multimodal).
• It’s the only measure of central tendency that can be used with nominal data.
• It’s not affected by extreme values.

• It can be used with all types of data, including nominal data.
• It’s easy to determine for small datasets.
• It’s useful for describing categorical data.

• It may not be unique.
• It may not exist for some datasets.
• It’s not suitable for many mathematical operations.

Example

Dataset: 2, 4, 4, 5, 5, 7, 9 Mode = 4 and 5 (bimodal)

Weighted Mean

A weighted mean is an average that takes into account the varying degrees of importance of the numbers in a dataset.

Formula

Weighted Mean = (w₁x₁ + w₂x₂ + … + wₙxₙ) / (w₁ + w₂ + … + wₙ)

Where w₁, w₂, …, wₙ are the weights, and x₁, x₂, …, xₙ are the values.

Applications

• Calculating GPA
• Computing price indices
• Analyzing survey data with different response importances

Geometric Mean

The geometric mean is the nth root of the product of n numbers.

Formula

Geometric Mean = (x₁ * x₂ * … * xₙ)^(1/n)

Applications

• Calculating average growth rates
• Analyzing returns in finance
• Comparing different products with multiple criteria

Harmonic Mean

The harmonic mean is the reciprocal of the arithmetic mean of reciprocals.

Formula

Harmonic Mean = n / (1/x₁ + 1/x₂ + … + 1/xₙ)

Applications

• Calculating average speed over a fixed distance
• Computing average rates
• Electrical circuit analysis

Relationship Between Mean, Median, and Mode

Symmetrical Distributions

In perfectly symmetrical distributions:

Mean = Median = Mode

Skewed Distributions

• Right-skewed (positively skewed): Mode < Median < Mean
• Left-skewed (negatively skewed): Mean < Median < Mode

Choosing the Appropriate Measure of Central Tendency

Consider the Type of Data

• Nominal: Use mode
• Ordinal: Use median or mode
• Interval/Ratio: Can use mean, median, or mode

Consider the Distribution

• Symmetrical: Mean, median, or mode
• Skewed: Median often preferred
• Presence of outliers: Median or mode

Consider the Purpose of Analysis

• Need for further statistical analysis: Often mean
• Describing typical value: Median or mode might be more representative

Measures of Central Tendency in Different Fields

Economics

• Mean income vs. median income
• Consumer Price Index (weighted mean)

Education

• GPA calculation (weighted mean)
• Standardized test scores (often reported as mean and median)

Meteorology

• Average temperature (mean)
• Median rainfall

Medicine

• Survival rates (often median)
• Drug effectiveness (various measures depending on data type)