Mathematical Foundation

The mean, or arithmetic average, is a fundamental measure of central tendency that represents the typical value in a dataset. It provides a single representative value that summarizes the entire collection of data points by distributing the total sum equally across all observations. The mean is sensitive to extreme values and serves as the foundation for many statistical calculations and data analysis techniques.

Arithmetic Mean Formula:
  • For individual data values: $\overline{x} = \frac{1}{n} \sum_{i=1}^{n} x_i = \frac{x_1 + x_2 + \cdots + x_n}{n}$
  • For grouped data: $\overline{x} = \frac{\sum (f_i \cdot x_i)}{\sum f_i}$ where $f_i$ is frequency
  • For data with weights: $\overline{x} = \frac{\sum (w_i \cdot x_i)}{\sum w_i}$ where $w_i$ is weight
Where:
  • $\overline{x}$ = sample mean
  • $n$ = number of data points
  • $x_i$ = individual data values
  • $\sum$ = summation symbol

Interactive Mean Calculator

Data Visualization

Current Data: [5, 7, 3, 8, 12, 6]
Sum: 41
Count: 6
Mean: 6.83
6.83
Mean
41
Total Sum
6
Data Points
4.07
Variance

Worked Example 1: Calculating Mean of Test Scores

A student scored 85, 92, 78, 96, and 88 on five math tests. Find the mean score.

Solution:

Sum = 85 + 92 + 78 + 96 + 88 = 439

Mean = 439 ÷ 5 = 87.8

The student's average test score is 87.8

Worked Example 2: Mean with Frequencies (Grouped Data)

The table shows the number of hours students spent studying:

HoursFrequency
25
48
66
83

Solution:

Mean = (2×5 + 4×8 + 6×6 + 8×3) ÷ (5+8+6+3) = (10 + 32 + 36 + 24) ÷ 22 = 102 ÷ 22 = 4.64

The average study time is 4.64 hours

Worked Example 3: Weighted Mean

A student's grades are: Quiz (20% weight): 85, Midterm (30%): 78, Final (50%): 92. Find the weighted mean.

Solution:

Weighted Mean = (85×0.2 + 78×0.3 + 92×0.5) ÷ (0.2 + 0.3 + 0.5) = (17 + 23.4 + 46) ÷ 1 = 86.4

The weighted average grade is 86.4

Properties of the Mean
  • Balance Point: The mean acts as a balance point for the data - the sum of deviations from the mean equals zero.
  • Least Squares Property: The mean minimizes the sum of squared deviations from any point.
  • Effect of Extreme Values: Outliers can significantly affect the mean, making it less representative of the data.
  • Additive Property: The mean of combined datasets can be calculated from individual means and sample sizes.
Comparison with Other Measures

Mean (Average)

  • Uses all data points
  • Affected by outliers
  • Mathematically useful
  • Balance point of data

Median

  • Middle value when ordered
  • Resistant to outliers
  • Position-based measure
  • Not affected by extreme values

Mode

  • Most frequent value
  • Can have multiple modes
  • Resistant to outliers
  • May not exist

Practice Problems

Problem 1: Find the mean of: 12, 15, 18, 22, 25, 30
Solution:
Sum = 12 + 15 + 18 + 22 + 25 + 30 = 122
Mean = 122 ÷ 6 = 20.33
Problem 2: The mean of 8 numbers is 25. If one number is removed, the mean becomes 22. Find the removed number.
Solution:
Original sum = 8 × 25 = 200
New sum = 7 × 22 = 154
Removed number = 200 - 154 = 46
Problem 3: Calculate the mean of the following frequency distribution:
ScoreFrequency
603
705
804
902

Solution:
Sum = (60×3) + (70×5) + (80×4) + (90×2) = 180 + 350 + 320 + 180 = 1030
Total frequency = 3 + 5 + 4 + 2 = 14
Mean = 1030 ÷ 14 = 73.57
Problem 4: The mean of three numbers is 15. When a fourth number is added, the mean becomes 17. Find the fourth number.
Solution:
Original sum = 3 × 15 = 45
New sum = 4 × 17 = 68
Fourth number = 68 - 45 = 23
Problem 5: Find the missing value if the mean of 5, 8, 12, x, 15 is 10.
Solution:
Sum = 5 + 8 + 12 + x + 15 = 40 + x
Mean = (40 + x) ÷ 5 = 10
40 + x = 50
x = 10

Key Takeaways

  • Definition: The mean is the arithmetic average, calculated by summing all values and dividing by the number of values.
  • Formula: $\overline{x} = \frac{\sum x_i}{n}$ for individual data, or $\overline{x} = \frac{\sum (f_i \cdot x_i)}{\sum f_i}$ for grouped data.
  • Properties: The mean serves as the balance point of the data; sum of deviations from mean equals zero.
  • Sensitivity: The mean is affected by extreme values (outliers), unlike the median.
  • Applications: Used in quality control, performance evaluation, financial analysis, and scientific research.
  • Weighted Mean: When data points have different importance, use weights: $\overline{x} = \frac{\sum (w_i \cdot x_i)}{\sum w_i}$.
  • Comparison: Mean uses all data points but is sensitive to outliers; median is robust but ignores magnitude of values.