Cost Accounting Quiz: Separating Mixed Costs
20 questions · exam conditions
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Separating Mixed CostsQuestion 1 of 20

A manufacturing company provides the following quarterly data on overhead costs and machine hours:

QuarterMachine HoursOverhead Cost
125,000$150,000
235,000$180,000
340,000$215,000
420,000$130,000

Using the high-low method, what is the variable overhead cost per machine hour?

$3.00
$4.25
$4.00
$3.50
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Cost Accounting Quiz

Cost Accounting Quiz: Separating Mixed Costs

Practice Separating Mixed Costs in Cost Accounting with focused quiz questions that help you check what you know, review explanations, and build confidence with test-style prompts.

What this quiz covers

This quiz focuses on Separating Mixed Costs, giving you a quick way to practice the rules, question types, and explanations that matter most for Cost Accounting.

How to use this quiz

Try each quiz question before looking at the correct answer. Use the explanations to review missed ideas, then come back to similar questions until the pattern feels familiar.

All questions

Question 1

A manufacturing company provides the following quarterly data on overhead costs and machine hours:

QuarterMachine HoursOverhead Cost
125,000$150,000
235,000$180,000
340,000$215,000
420,000$130,000

Using the high-low method, what is the variable overhead cost per machine hour?

  1. $3.00
  2. $4.25 (correct answer)
  3. $4.00
  4. $3.50
Explanation: To use the high-low method, you must select the periods with the highest and lowest activity levels, not the highest and lowest costs (though they are sometimes the same).
  1. Identify High and Low Activity Points:
    • High Activity: Quarter 3 (40,000 machine hours, $215,000 cost)
    • Low Activity: Quarter 4 (20,000 machine hours, $130,000 cost)
  2. Calculate the Variable Cost per Machine Hour:
    • Change in Cost = $215,000 - $130,000 = $85,000
    • Change in Hours = 40,000 - 20,000 = 20,000 hours
    • Variable Rate = Change in Cost / Change in Activity
    • Variable Rate = ($85,000 / 20,000) hours = $4.25 per machine hour.

Question 2

Alpha Corp. used the high-low method to analyze its utility costs. The analysis was based on the highest activity of 20,000 machine hours at a total cost of $58,000, and the lowest activity of 12,000 machine hours at a total cost of $46,000. What is the estimated variable portion of the total utility cost in a month with 15,000 machine hours?

  1. $22,500 (correct answer)
  2. $50,500
  3. $28,000
  4. $18,000
Explanation: The question asks for only the variable portion of the total cost at the new activity level, not the total estimated cost.
  1. Calculate Variable Cost per Hour:
    • Change in Cost: $58,000 - $46,000 = $12,000
    • Change in Hours: 20,000 - 12,000 = 8,000 hours
    • Variable Rate = ($12,000 / 8,000) hours = $1.50 per machine hour
  2. Calculate the Variable Portion for 15,000 Hours:
    • Variable Portion = Variable Rate * Activity Level
    • Variable Portion = $1.50 * 15,000 hours = $22,500 The fixed cost portion is $28,000, and the total estimated cost is $50,500, but the question specifically asks for the variable portion.

Question 3

A regression analysis of overhead costs (Y) against machine hours (X) produced the equation Y = $32,000 + $18.00X, with an R-squared value of 0.25. Which of the following is the most appropriate conclusion from this analysis?

  1. The model is a good fit, as fixed costs are $32,000 and variable costs are $18.00 per hour.
  2. Machine hours are a weak predictor of overhead cost because the R-squared value is low. (correct answer)
  3. The relationship is strong because for every additional machine hour, cost is expected to increase by $18.00.
  4. The model explains 75% of the variation in overhead costs, indicating a reliable predictive ability.
Explanation: The R-squared value, or coefficient of determination, measures the proportion of the variance in the dependent variable (cost) that is predictable from the independent variable (activity). An R-squared of 0.25 means that only 25% of the variation in overhead costs is explained by the variation in machine hours. This is a low value, indicating a weak relationship and that machine hours are a poor predictor of overhead costs. Other factors, not included in the model, are responsible for most of the cost variation.

Question 4

A consulting firm analyzed a company's utility costs as a function of machine hours. The regression output was: Cost = –$500 + $8.50 × Machine Hours. The data used for the regression was from 1,000 to 5,000 machine hours per month. What is the most plausible interpretation of the negative $500 intercept?

  1. The company receives a $500 credit if it operates at zero machine hours.
  2. The variable costs are decreasing, which makes the fixed cost appear negative.
  3. The intercept is a statistically meaningless value because zero machine hours is outside the relevant range. (correct answer)
  4. The analysis indicates a data entry error, as fixed costs cannot be negative.
Explanation: In cost accounting, the intercept of the regression equation is an estimate of fixed costs. However, this interpretation is only valid within the relevant range of the data. Since the data ranged from 1,000 to 5,000 machine hours, the point of zero machine hours is a significant extrapolation. The negative intercept is a result of the mathematical projection of the regression line back to the y-axis and does not have a real-world economic meaning in this context. While a data error is possible, the extrapolation outside the relevant range is a more fundamental statistical explanation for a non-intuitive intercept.

Question 5

A company is analyzing its electricity cost. A scattergraph of monthly costs versus machine hours reveals a pattern of points that are widely dispersed and show no discernible upward or downward trend. What is the most likely conclusion about the company's electricity cost?

  1. Electricity cost is a purely variable cost with a strong relationship to machine hours.
  2. Electricity cost is a mixed cost, but machine hours are not the primary cost driver. (correct answer)
  3. Electricity cost is a fixed cost, as it does not change with machine hours.
  4. Electricity cost exhibits a step-variable pattern that is only visible at high volumes.
Explanation: A scattergraph is used to visualize the relationship between a cost and a potential activity driver. If the points are widely scattered with no clear trend, it indicates a very weak or nonexistent correlation between the two variables. Since electricity cost is known to have both fixed (e.g., basic connection fee) and variable (e.g., usage charge) components, it is a mixed cost. The lack of a pattern suggests that machine hours are not the primary factor driving the changes in electricity cost; some other activity driver is likely more influential, or the cost is influenced by multiple factors.

Question 6

A company's maintenance costs are a mixed cost. The company has compiled the following data for the first four months of the year:

MonthMachine HoursMaintenance Cost
January7,000$23,500
February9,500$28,000
March8,000$29,000
April6,000$21,000

Using the high-low method, what would be the estimated total maintenance cost for a month with 8,500 machine hours?

  1. $26,500 (correct answer)
  2. $27,000
  3. $26,000
  4. $29,000
Explanation: The high-low method uses the periods with the highest and lowest activity levels to determine the cost formula.
  1. Identify High and Low Activity Points:
    • High Activity: February (9,500 hours, $28,000 cost)
    • Low Activity: April (6,000 hours, $21,000 cost)
  2. Calculate Variable Cost per Hour:
    • Change in Cost: $28,000 - $21,000 = $7,000
    • Change in Hours: 9,500 - 6,000 = 3,500 hours
    • Variable Rate = ($7,000 / 3,500) hours = $2.00 per machine hour
  3. Calculate Total Fixed Cost:
    • Using the high point: $28,000 - (9,500 hours * $2.00/hour) = $28,000 - $19,000 = $9,000
    • Using the low point: $21,000 - (6,000 hours * $2.00/hour) = $21,000 - $12,000 = $9,000
  4. Estimate Cost for 8,500 Hours:
    • Total Cost = Fixed Cost + (Variable Rate * Activity)
    • Total Cost = 9,000+(9,000 + (2.00 * 8,500) = $9,000 + $17,000 = $26,500

Question 7

A regression analysis reveals a strong correlation (R-squared = 0.95) between a company's sales commissions and the number of lawnmowers sold by a competitor in the same city. Assuming no business relationship exists between the two companies, what is the most logical conclusion?

  1. The competitor's sales are the primary driver of the company's sales commissions.
  2. A high R-squared value proves a cause-and-effect relationship between the two variables.
  3. The statistical relationship is likely spurious and driven by a common underlying factor, such as seasonality. (correct answer)
  4. The regression model is invalid because the variables are from two different companies.
Explanation: This question tests the critical concept of correlation versus causation. A high R-squared indicates a strong statistical correlation, meaning the two variables move together. However, it does not prove that one causes the other. In this case, it is implausible that a competitor's sales would directly cause the company's commission costs. A more logical explanation is a lurking or confounding variable, such as season (e.g., more lawnmowers are sold by everyone in the spring and summer), which is driving both variables in the same direction. This creates a spurious correlation.

Question 8

A company's cost accountant developed a regression equation for monthly utility costs: Y = $1,500 + $0.75X, where X is the number of machine hours. The average monthly activity is 10,000 machine hours. Next month, activity is expected to increase by 20%. What is the predicted total utility cost for next month?

  1. $9,000
  2. $10,500 (correct answer)
  3. $10,800
  4. $12,000
Explanation: This is a two-step problem: first determine the new activity level, then use the cost formula to predict the cost.
  1. Calculate the New Activity Level:
    • Current average activity = 10,000 hours
    • Increase = 20% of 10,000 = 0.20 * 10,000 = 2,000 hours
    • New activity level = 10,000 + 2,000 = 12,000 machine hours
  2. Predict the Total Utility Cost using the Regression Equation:
    • Y = $1,500 + $0.75 * X
    • Y = $1,500 + $0.75 * (12,000)
    • Y = $1,500 + $9,000
    • Y = $10,500

Question 9

A service firm wants to determine the cost behavior of its client support costs. The firm has the following information for the last two quarters:

QuarterSupport TicketsTotal Cost
Q13,000$140,000
Q25,000$190,000

Using the high-low method, what is the cost formula for client support costs per quarter?

  1. Y = $65,000 + $25.00X (correct answer)
  2. Y = $50,000 + $30.00X
  3. Y = $25,000 + $38.33X
  4. Y = $80,000 + $20.00X
Explanation: The high-low method is used to separate the fixed and variable components of the mixed cost.
  1. Calculate Variable Cost per Ticket (the 'b' coefficient):
    • Change in Cost = $190,000 - $140,000 = $50,000
    • Change in Tickets = 5,000 - 3,000 = 2,000 tickets
    • Variable Rate (b) = ($50,000 / 2,000) tickets = $25.00 per ticket
  2. Calculate Total Fixed Cost (the 'a' coefficient):
    • Using the high point (Q2): Fixed Cost = Total Cost - (Variable Rate * Activity)
    • Fixed Cost = 190,000−(190,000 - (25.00 * 5,000) = $190,000 - $125,000 = $65,000
    • Using the low point (Q1): Fixed Cost = 140,000−(140,000 - (25.00 * 3,000) = $140,000 - $75,000 = $65,000
  3. State the Cost Formula: Y = $65,000 + $25.00X, where X is the number of support tickets.

Question 10

A regression analysis of manufacturing overhead costs resulted in the following equation: Total Cost = $75,000 + $12.50 * Direct_Labor_Hours. The analysis was based on monthly data ranging from 4,000 to 7,000 direct labor hours. The company is evaluating a special project that would require 9,000 direct labor hours. Which statement best assesses the use of the regression equation to estimate the overhead cost for this project?

  1. The equation will accurately predict total overhead costs as the relationship is linear.
  2. The estimate may be unreliable because the activity level is outside the relevant range of the data. (correct answer)
  3. The fixed cost component of $75,000 is the most reliable part of the estimate for this project.
  4. The estimated overhead cost is $187,500, calculated as $12.50 multiplied by 9,000 hours plus $75,000.
Explanation: Cost behavior patterns identified via regression or the high-low method are reliable only within the relevant range—the range of activity over which the cost relationships are expected to hold true. The analysis was based on data between 4,000 and 7,000 hours. The project's 9,000 hours falls outside this range. Extrapolating beyond the relevant range is risky because the underlying cost behavior might change (e.g., fixed costs might increase in a step, or variable costs might change due to capacity constraints or bulk discounts). Therefore, the estimate may be unreliable.

Question 11

A company is analyzing its maintenance costs. An accountant states that within the relevant range of 10,000 to 20,000 machine hours, fixed costs are $40,000 per month. However, if activity exceeds 20,000 hours, fixed costs increase by $15,000. Last month, activity was 18,000 hours and total cost was $94,000. This month, activity is budgeted at 22,000 hours. What is the budgeted maintenance cost for this month?

  1. $106,000
  2. $121,000 (correct answer)
  3. $109,000
  4. $136,000
Explanation: This problem involves a step-fixed cost, which changes at a certain activity level. The solution requires multiple steps.
  1. Determine the Variable Cost Rate: Use the data from last month, as it falls within the initial relevant range.
    • Total Cost = $94,000 at 18,000 hours.
    • Fixed Cost for this activity level = $40,000.
    • Total Variable Cost = Total Cost - Fixed Cost = $94,000 - $40,000 = $54,000.
    • Variable Rate = Total Variable Cost / Activity = ($54,000 / 18,000) hours = $3.00 per hour.
  2. Determine the Fixed Cost for the Budgeted Month: The budgeted activity is 22,000 hours, which is above the 20,000-hour threshold.
    • New Fixed Cost = Base Fixed Cost + Step Increase = $40,000 + $15,000 = $55,000.
  3. Calculate the Total Budgeted Cost:
    • Total Budgeted Cost = New Fixed Cost + (Variable Rate × Budgeted Activity)
    • Total Budgeted Cost = 55,000+(55,000 + (3.00 × 22,000 hours) = $55,000 + $66,000 = $121,000.

Question 12

A company's production data for two semi-annual periods are as follows:

  • First 6 months: 60,000 units produced, $420,000 total overhead.
  • Second 6 months: 75,000 units produced, $495,000 total overhead.

Using the high-low method, what is the estimated total overhead for a future month in which 11,000 units are expected to be produced?

  1. $75,000 (correct answer)
  2. $55,000
  3. $72,000
  4. $175,000
Explanation: This is a multi-step problem that requires adjusting the time frame for the fixed cost component.
  1. Calculate Variable Cost per Unit using the semi-annual data:
    • Change in Cost = $495,000 - $420,000 = $75,000
    • Change in Units = 75,000 - 60,000 = 15,000 units
    • Variable Rate = ($75,000 / 15,000) units = $5.00 per unit
  2. Calculate the Semi-Annual Fixed Cost:
    • Using the high point: $495,000 - (75,000 units * $5.00/unit) = $495,000 - $375,000 = $120,000 (for a 6-month period)
  3. Convert to Monthly Fixed Cost:
    • Monthly Fixed Cost = ($120,000 / 6) months = $20,000 per month
  4. Estimate Total Overhead for the Future Month:
    • Total Cost = Monthly Fixed Cost + (Variable Rate * Units for the month)
    • Total Cost = 20,000+(20,000 + (5.00 * 11,000) = $20,000 + $55,000 = $75,000

Question 13

At its high point of activity, a company incurred total costs of $180,000 for 20,000 units. At its low point, it incurred total costs of $120,000 for 10,000 units. The average cost per unit was $9.00 at the high point and $12.00 at the low point. Using this information, what is the company's estimated total fixed cost within this relevant range?

  1. $60,000 (correct answer)
  2. $30,000
  3. $90,000
  4. $100,000
Explanation: This question requires using the total cost and unit data to find the cost formula. The average cost per unit is extra information but can also be used to find total cost if not given directly.
  1. Calculate Variable Cost per Unit using High-Low Method:
    • Change in Cost: $180,000 - $120,000 = $60,000
    • Change in Units: 20,000 - 10,000 = 10,000 units
    • Variable Rate = ($60,000 / 10,000) units = $6.00 per unit
  2. Calculate Total Fixed Cost:
    • Using the high point: Total Cost = Fixed Cost + (Variable Rate * Units)
    • 180,000=FixedCost+(180,000 = Fixed Cost + (6.00 * 20,000)
    • $180,000 = Fixed Cost + $120,000
    • Fixed Cost = $60,000

Question 14

A visual-fit line on a scattergraph of total cost versus activity indicates a y-intercept of approximately $10,000 and passes through a data point representing 5,000 units and a total cost of $45,000. Based on this line, what is the estimated variable cost per unit?

  1. $9.00
  2. $7.00 (correct answer)
  3. $11.00
  4. $8.00
Explanation: The y-intercept of the cost line represents the total fixed cost. The variable cost per unit is the slope of the line.
  1. Identify Fixed Cost: The y-intercept is given as $10,000.
  2. Use the data point to find the variable cost component:
    • Total Cost at 5,000 units = $45,000
    • Total Cost = Total Fixed Cost + Total Variable Cost
    • $45,000 = $10,000 + Total Variable Cost
    • Total Variable Cost = $35,000
  3. Calculate Variable Cost per Unit:
    • Variable Cost per Unit = Total Variable Cost / Activity Level
    • Variable Cost per Unit = ($35,000 / 5,000) units = $7.00 per unit.

Question 15

A company reports the following data for its shipping department. Management has identified a potential data entry error in one month.

MonthPackages ShippedTotal Cost
Q1500$4,000
Q2900$5,200
Q3600$7,500
Q4800$4,800

Using the high-low method, what is the variable cost per package shipped?

  1. $3.00
  2. $4.00
  3. $8.75
  4. $2.00 (correct answer)
Explanation: A common mistake with the high-low method is to use the highest and lowest costs instead of the highest and lowest activity levels. The method requires using the data points corresponding to the activity extremes.
  1. Identify High and Low Activity Points:
    • High Activity: Q2 (900 packages, $5,200 cost)
    • Low Activity: Q1 (500 packages, $4,000 cost)
    • Note: Q3 has the highest cost ($7,500), but not the highest activity. This is likely the outlier and should be ignored for the selection of high/low points, which are based on activity.
  2. Calculate Variable Cost per Package:
    • Change in Cost: $5,200 - $4,000 = $1,200
    • Change in Packages: 900 - 500 = 400 packages
    • Variable Rate = ($1,200 / 400) packages = $2.00 per package

Question 16

Coastal Electronics tracks its shipping costs and has noticed they vary with both the number of shipments and total weight shipped. The company is considering three approaches to separate these mixed costs: (1) Use number of shipments as the sole cost driver, (2) Use total weight as the sole cost driver, or (3) Use multiple regression with both variables.

Last month's data showed: Week 1 (50 shipments, 1,200 lbs, $2,800), Week 2 (40 shipments, 1,000 lbs, $2,400), Week 3 (60 shipments, 1,400 lbs, $3,200), Week 4 (45 shipments, 1,100 lbs, $2,600). Using shipments as the cost driver with the high-low method, what would be the predicted cost for a week with 55 shipments weighing 1,350 lbs?

  1. $3,000 (correct answer)
  2. $2,950
  3. $3,100
  4. $3,050
Explanation: Using shipments as the cost driver: High point is Week 3 (60 shipments, $3,200) and low point is Week 2 (40 shipments, 2,400).Variablecostpershipment=(2,400). Variable cost per shipment = (3,200 - $2,400) ÷ (60 - 40) = $800 ÷ 20 = $40. Fixed cost = 2,400−(2,400 - (40 × 40) = $800. For 55 shipments: 800+(800 + (40 × 55) = $800 + $2,200 = $3,000. Note that weight is irrelevant when using shipments as the sole cost driver. Choice B uses an incorrect variable rate. Choice C incorrectly includes weight in the calculation. Choice D uses the wrong high/low points.

Question 17

Midwest Services performed regression analysis to separate mixed costs for three different expense categories. The results were: Office supplies (R² = 0.65, Fixed = $1,200, Variable = $3.50 per unit), Maintenance (R² = 0.89, Fixed = $2,500, Variable = $8.00 per unit), Communications (R² = 0.42, Fixed = $800, Variable = $2.25 per unit). If the company needs the most reliable cost estimates for budgeting purposes, which cost relationship should receive the highest confidence level?

  1. Office supplies because it has moderate R² and the lowest variable cost rate, providing stable predictions
  2. Communications because it has the lowest fixed cost component, making it easier to predict accurately
  3. Maintenance because it has the highest R² value, indicating the strongest statistical relationship (correct answer)
  4. Office supplies because the combination of fixed and variable costs creates the most balanced cost structure
Explanation: When analyzing mixed cost relationships from regression analysis, you need to evaluate the statistical reliability of each cost equation. The key indicator of reliability is the R² value (coefficient of determination), which measures how well the regression line fits the actual data points. The R² value ranges from 0 to 1, where values closer to 1 indicate stronger relationships between the independent variable (activity level) and dependent variable (total cost). A higher R² means the cost equation will provide more accurate predictions for budgeting purposes. Looking at the three relationships: Maintenance has R² = 0.89 (very strong), Office supplies has R² = 0.65 (moderate), and Communications has R² = 0.42 (weak). Maintenance's R² of 0.89 indicates that 89% of the variation in maintenance costs can be explained by changes in activity level, making it the most reliable for budgeting. Choice A incorrectly focuses on the variable cost rate and assumes lower rates mean more stability, but statistical reliability isn't determined by the magnitude of cost components. Choice B misunderstands cost prediction accuracy—the size of the fixed cost component doesn't indicate reliability; only the statistical relationship (R²) does. Choice D confuses cost structure balance with statistical reliability, but having "balanced" fixed and variable components has no bearing on prediction accuracy. Study tip: For regression analysis questions, always look for the highest R² value first—it's your best indicator of which cost relationship will give you the most reliable estimates for decision-making purposes.

Question 18

Precision Tools Inc. wants to separate its maintenance costs into fixed and variable components. The company has narrowed its analysis to two potential cost drivers: machine hours and number of setups. Using least squares regression, machine hours yielded: Fixed cost = $8,500, Variable cost = $12.50 per hour, R² = 0.78. Number of setups yielded: Fixed cost = $6,200, Variable cost = $425 per setup, R² = 0.91. Which approach should the company choose and why?

  1. Machine hours because the variable cost per unit is lower, making it more cost-effective for budgeting purposes
  2. Machine hours because it results in higher fixed costs, which provides more conservative cost estimates
  3. Number of setups because it has a higher R² value, indicating better correlation and more reliable predictions (correct answer)
  4. Number of setups because the variable cost per unit is higher, providing better cost coverage margins
Explanation: When analyzing cost behavior using regression analysis, the key metric for choosing between cost drivers is the R² value (coefficient of determination). This statistic measures how well your chosen cost driver explains the variation in costs, with values closer to 1.0 indicating stronger predictive power. In this case, number of setups produces an R² of 0.91, meaning 91% of the variation in maintenance costs can be explained by the number of setups. Machine hours only explains 78% of the cost variation. The higher R² for setups indicates a much stronger correlation and more reliable cost predictions, making option C correct. Option A incorrectly focuses on the variable cost per unit ($12.50 vs. $425). You cannot directly compare these amounts because they measure different activities - the cost per setup versus cost per machine hour serve different purposes and volumes. Option B misunderstands the role of fixed costs in regression analysis. Higher fixed costs don't inherently provide "conservative" estimates - accuracy matters more than the absolute level of fixed costs. The $8,500 versus $6,200 difference is irrelevant for choosing the better model. Option D incorrectly suggests that higher variable costs provide "better cost coverage margins." This confuses cost prediction accuracy with pricing strategy. The goal of cost driver analysis is predictive reliability, not maximizing variable cost rates. Remember: When comparing regression models for cost accounting, always prioritize the R² value. It's your best indicator of which cost driver will give you the most accurate cost predictions for budgeting and planning purposes.

Question 19

Alpine Manufacturing uses the scattergraph method to analyze utility costs. After plotting 12 months of data (machine hours vs. utility costs), the visual inspection suggests a linear relationship with the line of best fit appearing to intersect the y-axis at approximately $3,200. The line passes through the point (2,000 hours, $8,200). However, when management reviewed the data, they noticed that the March data point (1,800 hours, $12,500) appears to be an outlier due to emergency repairs. What should be the estimated variable cost per machine hour after removing this outlier?

  1. $2.50 per hour (correct answer)
  2. $3.15 per hour
  3. $2.75 per hour
  4. $3.00 per hour
Explanation: Using the scattergraph method with visual inspection, the line intersects the y-axis at $3,200 (fixed cost) and passes through (2,000 hours, 8,200).Variablecostperhour=(8,200). Variable cost per hour = (8,200 - $3,200) ÷ 2,000 = $5,000 ÷ 2,000 = $2.50. The outlier removal doesn't affect this calculation since we're using the visually fitted line that already excludes outliers. Choice B incorrectly includes the outlier in the calculation. Choice C uses an incorrect fixed cost estimate. Choice D uses the wrong reference point.

Question 20

TechFlow Industries collected quarterly data for equipment maintenance costs over two years: Q1-Y1 (850 hours, $18,500), Q2-Y1 (920 hours, $19,800), Q3-Y1 (780 hours, $17,200), Q4-Y1 (1,050 hours, $22,100), Q1-Y2 (890 hours, $19,200), Q2-Y2 (950 hours, $20,300), Q3-Y2 (810 hours, $17,800), Q4-Y2 (1,080 hours, $22,600). The company wants to use the high-low method but is concerned about seasonal variations. Which quarters should be used as high and low points to minimize seasonal bias?

  1. Q4-Y2 (high) and Q3-Y1 (low) because they represent the absolute highest and lowest activity levels
  2. Q4-Y1 (high) and Q3-Y2 (low) because they are from different years, reducing seasonal impact (correct answer)
  3. Q2-Y2 (high) and Q1-Y1 (low) because they represent moderate activity levels with less extreme seasonal variation
  4. Average the Q4 data from both years and Q3 data from both years to eliminate seasonal fluctuations completely
Explanation: The high-low method requires using the highest and lowest activity levels, which are Q4-Y2 (1,080 hours) and Q3-Y1 (780 hours). However, choice B correctly identifies that using points from different years (Q4-Y1 at 1,050 hours and Q3-Y2 at 810 hours) would reduce seasonal bias while still representing high and low activity levels within a reasonable range. Choice A uses the absolute extremes but ignores seasonal considerations. Choice C abandons the high-low method principle of using extreme points. Choice D incorrectly suggests averaging, which violates the high-low methodology.