Acca F5: Chapter 9 - Quantitative Analysis

1. What is high/low analysis?

A method of analyzing seasonal costs without considering historical data.

A method of analyzing variable costs without separating them from fixed costs.

A method of analyzing only fixed costs within a business.

1 High/low analysis A method of analysing a semi-variable cost into its fixed and variable elements based on an analysis of historical information about costs at different activity levels.

High/low analysis involves breaking down a semi-variable cost into its fixed and variable components based on historical data at different activity levels. This method helps in understanding cost behavior and determining cost drivers.

Explanation

High/low analysis involves breaking down a semi-variable cost into its fixed and variable components based on historical data at different activity levels. This method helps in understanding cost behavior and determining cost drivers.

2. What are the advantages of using the high-low analysis method?

1 High/low analysis - The high-low method has the enormous advantage of simplicity. - It is easy to understand and easy to use.

The high-low method is time-consuming and complex to implement.

The high-low method provides inaccurate results compared to other analysis methods.

The high-low method is not suitable for small businesses or industries.

The high-low method is known for its simplicity and ease of understanding, making it a preferred choice for many analysts.

Explanation

The high-low method is known for its simplicity and ease of understanding, making it a preferred choice for many analysts.

3. What are the disadvantages of high/low analysis?

1 High/low analysis - It does not take into account seasonal fluctuations in costs. - It requires complex mathematical calculations.

1 High/low analysis - It is a time-consuming method. - It often leads to inaccurate cost estimates.

1 High/low analysis - It assumes that activity is the only factor affecting costs. - It assumes that historical costs reliably predict future costs. - It uses only two values, the highest and the lowest, so the results may be distorted due to random variations in these values.

1 High/low analysis - It is only suitable for large organizations. - It does not provide insights into cost behavior over time.

High/low analysis has several disadvantages that may impact the accuracy of cost estimates and the reliability of future predictions.

Explanation

High/low analysis has several disadvantages that may impact the accuracy of cost estimates and the reliability of future predictions.

4. What is regression analysis?

Regression analysis is a method that only uses future data to predict outcomes.

Regression analysis is a method used to analyze the correlation between three or more variables.

2 Regression analysis Regression is another method of forecasting. It involves using historical data to find the line of best fit between two variables (one dependent on the other), and uses this straight line to predict future values.

Regression analysis is a method that involves finding the average of all data points for prediction purposes.

5. In regression analysis, what is the equation for a straight line and what do each symbol represent?

Y = mx + b where y = independent variable, x = dependent variable, m = fixed cost, b = variable cost.

Y = ax + b where y = dependent variable, x = independent variable, a = variable cost, b = fixed cost.

2 Regression analysis y = a + bx y = dependant variablex = independant variablea = fixed costb = var cost per unit

Y = a - bx where y = independent variable, x = dependent variable, a = fixed cost, b = variable cost per unit.

When performing regression analysis, the standard equation for a straight line is of the form y = a + bx. The term 'y' represents the dependent variable being studied, 'x' denotes the independent variable affecting y, 'a' represents the fixed cost or intercept of the line, and 'b' denotes the variable cost per unit or slope of the line.

Explanation

When performing regression analysis, the standard equation for a straight line is of the form y = a + bx. The term 'y' represents the dependent variable being studied, 'x' denotes the independent variable affecting y, 'a' represents the fixed cost or intercept of the line, and 'b' denotes the variable cost per unit or slope of the line.

6. In a regression analysis, what does the coefficient of determination represent?

2 Regression analysis It shows the percentage change in the dependent variable, e.g. sales, that can be explained by a change in the independent variable, e.g. advertising spend r2

It determines the direction of the relationship between dependent and independent variables.

It quantifies the increase in residuals when fitting a regression model.

It measures the strength of the relationship between two variables.

The coefficient of determination, denoted as r^2, explains the proportion of the variance in the dependent variable that is predictable from the independent variable. It ranges from 0 to 1, where 0 indicates no explanatory power, and 1 indicates perfect explanatory power.

Explanation

The coefficient of determination, denoted as r^2, explains the proportion of the variance in the dependent variable that is predictable from the independent variable. It ranges from 0 to 1, where 0 indicates no explanatory power, and 1 indicates perfect explanatory power.

7. What is a time series?

3 Time series analysis A time series is a series of figures relating to the changing value of a variable over time. The data often conforms to a certain pattern over time. This pattern can be extrapolated into the future and hence forecasts are possible. Time periods may be any measure of time including days, weeks, months and quarters.

A time series is a type of watch that has multiple dials showing different time zones.

A time series is a mathematical equation used to calculate the speed of time travel.

A time series is a group of clocks synchronized to display the same time.

A time series, in the context of data analysis, refers to a specific type of dataset where observations are indexed based on time. It helps analyze trends, patterns, and make forecasts based on historical data.

Explanation

A time series, in the context of data analysis, refers to a specific type of dataset where observations are indexed based on time. It helps analyze trends, patterns, and make forecasts based on historical data.

8. In time series analysis, what does the additive model consist of?

Trend x Seasonal Variation = Actual

Trend - Seasonal Variation = Actual

Seasonal Variation = Actual - Trend

3 Time series analysis Actual = Trend + Seasonal Variation (SV) The SV will be expressed in absolute terms.

The additive model in time series analysis involves adding the trend and seasonal variation in absolute terms to arrive at the actual value.

Explanation

The additive model in time series analysis involves adding the trend and seasonal variation in absolute terms to arrive at the actual value.

9. What is the purpose of an average growth model and what is the equation used?

Average growth models are only applicable to certain industries and not universally relevant.

4 Average growth models Strategic plans may incorporate an objective of a target average growth of profit or sales over a number of years. There may also be requirements for a target average growth rate of productivity over a number of years. g = average growth rate, as a decimaln = number of periods of growth

Average growth models are used to predict short-term fluctuations in sales and profit.

The equation for average growth model is g = final value - initial value / number of years.

Average growth models are designed to target a specific growth rate over a period of time, not to predict short-term fluctuations. The correct equation for average growth rate involves multiplying the growth rate by the number of growth periods, not subtracting final and initial values. Additionally, average growth models can be used in a variety of industries to set growth targets.

Explanation

Average growth models are designed to target a specific growth rate over a period of time, not to predict short-term fluctuations. The correct equation for average growth rate involves multiplying the growth rate by the number of growth periods, not subtracting final and initial values. Additionally, average growth models can be used in a variety of industries to set growth targets.

10. Why are judgement and experience important in forecasting?

Quantitative models are always accurate in predicting future results

Forecasting can be done accurately without considering past results

5 The use of judgement and experience in forecasting The quantitative models available are mainly based on past information and extrapolate these results into the future. Managers will have access to many sources of information to help them judge whether past results are likely to be good predictors of future results. ie a competitor has recently launched a new product in the market or a new advertising medium is to be used

The use of judgement and experience in forecasting is not important

Judgement and experience are crucial in forecasting as they help in assessing the validity of past results and making informed decisions based on various factors. Quantitative models alone may not capture all the nuances and external factors that could impact future outcomes.

Explanation

Judgement and experience are crucial in forecasting as they help in assessing the validity of past results and making informed decisions based on various factors. Quantitative models alone may not capture all the nuances and external factors that could impact future outcomes.

11. What is Wright's Law in relation to learning curves?

Wright's Law refers to the idea that learning curves do not apply to the manufacturing industry.

6 Learning curves As cumulative output doubles, the cumulative average time per unit falls to a fixed percentage (referred to as the learning rate) of the previous average time.

Wright's Law states that learning curves plateau after a certain period of time.

Wright's Law proposes that the more units produced, the longer it takes for the average time per unit to decrease.

Wright's Law describes the relationship between cumulative output and average time per unit in relation to learning curves. It states that as cumulative output doubles, the cumulative average time per unit falls to a fixed percentage of the previous average time, known as the learning rate.

Explanation

Wright's Law describes the relationship between cumulative output and average time per unit in relation to learning curves. It states that as cumulative output doubles, the cumulative average time per unit falls to a fixed percentage of the previous average time, known as the learning rate.

12. What are the two methods for calculating the learning curve effect?

1) Method 1: use a random number generator to determine the learning rate each time the output doubles 2) Method 2: estimate the learning curve effect without any calculations

1) Method 1: divide the total time by the total output to get the average time per unit 2) Method 2: calculate the average time reduction based on guesswork

6 Learning curves 1) Method 1: set up a table and reduce the average time by the learning rate each time the output doubles 2) Method 2: using the formula y = axb y = cumulative average time (or average cost) per unit or per batch a = time (or cost) for first unit or batch x = log r/log 2 (r = rate of learning, expressed as a decimal) b = cumulative output in units or in batches

1) Method 1: ignore the output and focus solely on the initial time per unit 2) Method 2: estimate the learning curve effect based on the phase of the moon

Learning curve effects can be accurately calculated using the methods provided in the correct answer, while the incorrect answers involve unreliable or nonsensical approaches to determining the learning curve effect.

Explanation

Learning curve effects can be accurately calculated using the methods provided in the correct answer, while the incorrect answers involve unreliable or nonsensical approaches to determining the learning curve effect.

13. What are some applications of the learning effect?

6 Learning curves 1) prices will be set too high if based on the costs of making the first few units. 2) less labour per unit will be required as more units are made. This may have management implications, e.g. workers may be laid off. 3) the viability of a product may change if a learning effect exists 4) if a product enjoys a learning effect but this effect is ignored, then the standard cost will be too high. The presence of a learning effect can also make standard setting difficult. 5) the presence of a learning effect should be taken into account when setting budgets. For example, the labour budget may be reduced by a learning effect but working capital may be required sooner than expected

3) Product viability remains constant regardless of a learning effect.

1) Learning curves have no impact on pricing decisions.

2) Work scheduling is not affected by learning curves.

Learning curves have significant implications on various aspects of business operations, including pricing decisions, work scheduling, product viability, standard setting, and budgeting.

Explanation

Learning curves have significant implications on various aspects of business operations, including pricing decisions, work scheduling, product viability, standard setting, and budgeting.

14. What is the steady state in the context of 6 Learning curves?

A state where the costs start increasing instead of decreasing.

6 Learning curves 1) Direct labour hours will not reduce any further and this will become the basis on which the budget is produced.

A state where the production completely halts due to inefficiency.

A state where learning stops and no further improvement is possible.

The steady state refers to the point where the reduction in direct labour hours will not continue any further, and this becomes the benchmark for budgeting purposes in the context of 6 Learning curves.

Explanation

The steady state refers to the point where the reduction in direct labour hours will not continue any further, and this becomes the benchmark for budgeting purposes in the context of 6 Learning curves.

15. What are some limitations of the learning curve model?

6 Learning curves 1) the process is labour intensive: modern manufacturing can be very machine intensive. The learning effect will not apply if machines limit the speed of labour 2) there are no breaks in production: a break in production may result in the learning effect being lost 3) the product is new: the introduction of a new product makes it more probable that there will be a learning effect 4) the product is complex: the more complex the product, the more probable that the learning effect will be significant and the longer it will take for the learning effect to reach the steady state 5) the process is repetitive: if the process is not repetitive, a learning effect will not be enjoyed.

1) Learning curves only apply to simple products

2) Learning curves do not account for changes in market demand

3) The learning curve model is not useful for predicting future costs