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Microlesson · 5-min read

Methods of Segregating Semi-Variable Costs

## Segregating Semi-Variable Costs into Fixed and Variable Components

Semi-variable costs have both fixed and variable elements. Five methods are used to separate them:

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### 1. Graphical Method

Steps:

1. Collect observations: total cost at various output levels.

2. Plot output (X-axis) vs total cost (Y-axis).

3. Draw a line of best-fit by visual judgment.

4. Where the line intersects the Y-axis = Fixed Cost.

5. Draw a horizontal line at that intercept = fixed cost line.

6. For any output level: Variable Cost = Total Cost − Fixed Cost.

Advantages: Simple; accessible to non-specialists; quick rough estimates.

Limitations: Subjective (line drawn by judgment); less precise; unreliable outside data range.

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### 2. High-Low Method

Formula:

$$\text{Variable Cost Rate} = \frac{\text{Cost at Highest Level} - \text{Cost at Lowest Level}}{\text{Output at Highest Level} - \text{Output at Lowest Level}}$$

Then: Fixed Cost = Total Cost at either level − (Variable Rate × Output at that level)

Advantage: Quick and simple.

Limitation: Uses only two extreme observations; ignores all other data points; distorted if extremes are outliers.

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### 3. Analytical Method

  • Experienced cost accountant empirically estimates the variable proportion of each semi-variable item.
  • e.g., Supervision cost: 20% variable, 80% fixed.
  • Advantage: Easy to apply.
  • Limitation: Highly subjective; relies on individual judgment.

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### 4. Comparison by Period / Level of Activity Method

  • Compare two output levels and their corresponding costs.
  • Since fixed element is constant, the difference in cost = difference in variable cost.

$$\text{Variable Cost per Unit} = \frac{\text{Change in Expense}}{\text{Change in Output}}$$

Then: Fixed Cost = Total Cost − (Variable Rate × Output)

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### 5. Least Squares Method (Best Method)

  • Statistical method; finds the line of best fit using all data points (not just two extremes).
  • Uses the linear equation:

$$y = mx + c$$

Where:

  • $y$ = Total cost
  • $x$ = Volume of output
  • $m$ = Variable cost per unit
  • $c$ = Total fixed cost
  • Normal equations solved simultaneously to find $m$ and $c$.
  • Most accurate because it uses all observations and minimises squared deviations.

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### Method Comparison

MethodData UsedSubjectivityAccuracy
GraphicalAll points (visual)HighLow-Medium
High-LowOnly 2 pointsLowLow (outlier risk)
AnalyticalJudgmentVery HighDepends on expertise
Comparison by Period2 selected levelsLowModerate
Least SquaresAll points (statistical)NoneHighest

Worked example

### Example 1

High-Low Method:

Output (units): Highest = 1,000; Lowest = 400.

Total Cost: At 1,000 units = ₹18,000; At 400 units = ₹12,000.

Variable rate = (18,000 − 12,000) ÷ (1,000 − 400) = 6,000 ÷ 600 = ₹10 per unit.

Fixed Cost = 18,000 − (10 × 1,000) = ₹8,000.

Verification: 12,000 − (10 × 400) = 12,000 − 4,000 = ₹8,000 ✓

### Example 2

Comparison by Period Method:

Level 1: Output = 500 units, Cost = ₹9,500.

Level 2: Output = 800 units, Cost = ₹12,500.

Variable rate = (12,500 − 9,500) ÷ (800 − 500) = 3,000 ÷ 300 = ₹10 per unit.

Fixed Cost = 9,500 − (10 × 500) = ₹4,500.

### Example 3

Least Squares Method (Small Dataset):

Data: (100 units, ₹5,500), (200, ₹7,000), (300, ₹8,500).

Σx = 600, Σy = 21,000, Σx² = 140,000, Σxy = 4,600,000, n = 3.

Solve normal equations:

  • Σy = nc + mΣx → 21,000 = 3c + 600m
  • Σxy = cΣx + mΣx² → 4,600,000 = 600c + 140,000m

Solving: m = ₹15/unit, c = ₹4,000 fixed cost.

⚠️ Common exam mistakes

  • Using High-Low method when extreme data points are outliers (e.g., due to a strike or flood) — this distorts both variable rate and fixed cost estimates.
  • In the graphical method, extending the line beyond the observed data range to read fixed cost — extrapolation beyond the relevant range is unreliable.
  • In Least Squares, forgetting to use ALL observations — using only two defeats the purpose of statistical fitting.
  • Forgetting to verify fixed cost using both the high AND low data points after applying the High-Low method — a mismatch signals a calculation error.
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