Electronics Engineering  ·  Level 5
Engineering Technician Mathematics I
Chapter 7: Apply Statistics and probability
📚 10 Topics

Compiled by: Beatrice Chepkorir

7.1 Measures of central tendency mean, mode and median

Engineers rarely work with a single measurement. A technician testing a batch of resistors, a coil of cable or a set of battery cells collects many readings, and a measure of central tendency is one number that represents the typical value of the whole set. The three common measures are the mean, the median and the mode. Each answers a slightly different question, and choosing the right one is part of reporting data honestly.

The arithmetic mean

The mean is the sum of all the values divided by the number of values. For n values x₁, x₂, …, xₙ,

x̄ = (x₁ + x₂ + … + xₙ) ÷ n

where x̄ (read "x bar") is the mean. The mean uses every value, so it is the natural choice for symmetrical data, but one very large or very small value pulls it noticeably.

Example 1: Ten readings of a resistor marked 220 Ω were taken with a multimeter: 218, 221, 220, 219, 222, 220, 223, 217, 220 and 221 Ω. Find the mean.

Sum = 218 + 221 + 220 + 219 + 222 + 220 + 223 + 217 + 220 + 221 = 2201

x̄ = 2201 ÷ 10 = 220.1 Ω

Answer: The mean resistance is 220.1 Ω.

The median

The median is the middle value when the data are arranged in order of size. If n is odd, the median is the value in position (n + 1) ÷ 2. If n is even, it is the mean of the two middle values, in positions n ÷ 2 and (n ÷ 2) + 1. The median is not disturbed by extreme values, so it is the better choice when the data contain a faulty reading or are very uneven.

Example 2: Find the median of the readings in Example 1.

Arranged in order: 217, 218, 219, 220, 220, 220, 221, 221, 222, 223

There are 10 values, so the median is the mean of the 5th and 6th values: (220 + 220) ÷ 2 = 220.

Answer: The median is 220 Ω.

Example 3: A workshop records the number of soldered joints a trainee completes in six timed sessions: 12, 15, 9, 14, 10 and 16. Find the mean and the median.

Mean = (12 + 15 + 9 + 14 + 10 + 16) ÷ 6 = 76 ÷ 6 = 12.67

In order: 9, 10, 12, 14, 15, 16. With six values the median is the mean of the 3rd and 4th: (12 + 14) ÷ 2 = 13.

Answer: Mean = 12.67 joints, median = 13 joints.

The mode

The mode is the value that occurs most often. A set of data may have one mode, more than one mode (bimodal or multimodal) or none at all if every value occurs once. The mode is the only measure that can be used for data that are not numbers, such as the most common fault type found in repaired power supplies.

In Example 1 the value 220 appears three times and every other value appears once or twice, so the mode is 220 Ω. In the data 4, 4, 5, 7, 7, 9 the values 4 and 7 each occur twice, so the data are bimodal with modes 4 and 7.

Choosing between the measures

Consider five readings of a bench power supply set to 3.3 V: 3.2, 3.4, 3.3, 3.9 and 3.3 V. The mean is 17.1 ÷ 5 = 3.42 V, the median is 3.3 V and the mode is 3.3 V. The single high reading of 3.9 V has moved the mean away from where most readings lie, while the median and mode stay at 3.3 V. If the 3.9 V reading came from a loose probe, the median describes the supply better than the mean.

Grouped data

When there are many readings they are collected into classes with a frequency for each class. The exact values are then unknown, so the measures are estimated. For the mean, each class is represented by its midpoint m, and

x̄ = Σ(f × m) ÷ Σf

where f is the class frequency and Σf = N is the total frequency.

For the median of grouped data, find the class that contains the N ÷ 2 th value (the median class) and interpolate:

Median = L + [(N ÷ 2 − F) ÷ f] × w

where L is the lower boundary of the median class, F is the cumulative frequency before it, f is its frequency and w is its class width.

For the mode, find the modal class (the highest frequency) and use

Mode = L + [d₁ ÷ (d₁ + d₂)] × w

where d₁ is the modal frequency minus the frequency of the class before, d₂ is the modal frequency minus the frequency of the class after, and L and w refer to the modal class.

Example 4: The lifetimes of 70 LED lamps tested in a workshop are recorded as follows.

Lifetime (hours) Frequency
1000 to under 1200 6
1200 to under 1400 14
1400 to under 1600 25
1600 to under 1800 18
1800 to under 2000 7

Estimate the mean, the median and the mode.

Mean: the midpoints are 1100, 1300, 1500, 1700 and 1900, so

Σ(f × m) = 6 × 1100 + 14 × 1300 + 25 × 1500 + 18 × 1700 + 7 × 1900 = 6600 + 18200 + 37500 + 30600 + 13300 = 106200

x̄ = 106200 ÷ 70 = 1517.1 hours

Median: N ÷ 2 = 35. The cumulative frequency up to 1400 is 6 + 14 = 20, and up to 1600 is 45, so the median class is 1400 to under 1600.

Median = 1400 + [(35 − 20) ÷ 25] × 200 = 1400 + 120 = 1520 hours

Mode: the modal class is 1400 to under 1600, with d₁ = 25 − 14 = 11 and d₂ = 25 − 18 = 7.

Mode = 1400 + [11 ÷ (11 + 7)] × 200 = 1400 + 122.2 = 1522.2 hours

Answer: Mean ≈ 1517.1 hours, median = 1520 hours, mode ≈ 1522.2 hours.

The three values are close to each other, which tells us the distribution of lifetimes is nearly symmetrical about 1520 hours.

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🔒7.2 Measures of dispersion

Two sets of data can have exactly the same mean and still behave very differently. A batch of resistors that all lie within 2 Ω of the marked value and a batch that is spread over 30 Ω may both average 220 Ω, yet only the first is useful in a precision circuit…

🔒7.3 Variance and standard deviation

The variance and the standard deviation are the standard measures of how spread out data are. Like the mean deviation they use every value, but they avoid absolute value signs by squaring the differences from the mean. This makes them easy to combine and to us…

🔒7.4 Definition of probability

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🔒7.5 Laws of probability

Most real questions involve more than one event: a board that has a solder fault or a component fault, a system that works only if every part works, a lamp that fails given that the supply was surged. The laws of probability tell us how to combine the probabil…

🔒7.6 Expectation variance and SD

A random variable is a quantity whose value depends on chance, such as the number of repairs a technician completes in a day or the lifetime of a lamp. The expectation (expected value, mean) of a random variable is its long-run average, and the variance and st…

🔒7.7 Calculations involving discrete and continuous random variables

Random variables come in two kinds. A discrete random variable takes separate values, usually counts, such as the number of faulty units in a box. A continuous random variable can take any value in an interval, such as a voltage, a length or a time. The calcul…

🔒7.8 Types of distributions

A probability distribution states how the probability of a random variable is spread over its possible values. Choosing the right distribution for a situation lets us calculate probabilities from a few facts, such as the number of trials and the chance of succ…

🔒7.9 Mean, variance and SD of probability distributions

Every probability distribution has a mean that gives its centre, a variance that measures its spread, and a standard deviation, the square root of the variance. For the standard distributions these have simple formulas in terms of the parameters, so they can b…

🔒7.10 Application of probability distributions

The binomial, Poisson, normal and exponential distributions are working tools. They are used to decide how many spare parts to keep, whether to accept a delivery of components, whether a production process meets its specification, and how reliable a system is.…