budgeting methods Flashcards
learning curve analysis
as the amount produced doubles the average time per unit will reduce.
what is the learning curve
It costs more to produce the first unit of a product than it does to produce the one hundredth unit. due to increase in efficiency until it stays level
Where does learning curve theory apply ?/Limitations of Learning curve
Learning curve effect applies on labor rather than machines.
* When the task is new for the labor.
* When the nature of work is repetitive.
* Where the tasks are complex.
* There are no breaks in production
how to algebraically calculate the time taken for a specific unit number?
Y=a*xb
x = cumulative number of units.
Y = cumulative average time per unit to produce X units.
a = time required to produce the first unit of output.
b = index of learning = log r / log 2, where r = the learning rate expressed as a decimal.
advantages of using spreadsheets in budgeting
- because it is an online spreadsheet it can be accessed from anywhere
- different inputs can be linked with each other by applying formulas thus easy to design the new version of the model simply by changing the inputs figures
- sensitivity analysis can also be performed on the spreadsheet
- it is cheaper and less time consuming
disadvantages of using spreadsheets in budgeting
- if there is one error in the formulae, the whole numbers on the budget will be wrong
- a model can become easily corrupted simply by putting a number in the wrong cell
- no audit trail can be followed in order to check the numbers
- can’t record qualitative information effectively using spreadsheets
what is the linear regression formula
y = a + bx
y= dependent variable (TC)
a= y intercept (FC)
b= gradient of line (VC per unit)
x=independent variable ( UNITS)
how to calculate a
a = തy – b തx
how to calculate b
what are the limitations of using linear regression
- assumes a linear relationship between the variables
- only measures the relationshio between 2 variables when in reality there are many variables that can effect the outcome
- only interpolation is useful from this and extrapolation is a highly untrustworthy figure
- it assumes that historical behavior will continue in the same manner into the future
- interpolated predictions are only reliable if there is a significant correlation between the data
what is the limitation of correlation
- A random sample of data can lead to an apparent correlation even if its not necessarily
present, simply due to coincidence or a sampling error. Therefore, the correlation needs to
be accompanied by a significance test to ensure the reliability. - Correlation does not equal to causation, meaning that even if there exist a relationship
between two variables it does not necessarily mean that the variables are related to one
another.
e.g. Sales of ice cream and Sun glasses during summer
what does the coefficient of determination show
The coefficient of determination, r2, gives the proportion of changes in y that can be explained
by changes in x, assuming a linear relationship between x and y.
For example: If a correlation coefficient r = +0.9, then r2 = 0.81 and we could state that 81% of
the observed changes in y can be explained by the changes in x and that 19% of the changes
must be due to other factors
how to find the coefficient of determination
this squares the correlation in order to express the strength of the relationship between the
variables as a percentage
what is time series analysis
Time series analysis can be used to analyze historic data and establish any underlying trend
and seasonal variations within the data. The trend refers to the general direction the data is
heading in and can be upward or downward. The seasonal variation refers to the regular
variations which exist within the data.
what are the Components of a time series
1.the trend is the long term general movement of the data.
2.Cyclical variations are economic cycles of booms and slumps.
3. Season variations are a regular variation around the trend over a fixed time period, usually
one year.
4.Residual variations are irregular, random fluctuations in the data usually caused by factors
specific to the time series
what are seasonal; variations
seasonal Variation (short term fluctuations)
Seasonal variations can be estimated by comparing an actual time series with the trend line
values calculated from the time series.
- For each ‘season’ the seasonal variation is the difference between the trend line value and
the actual historical value for the same period
what is moving averages method
Moving Averages is a set of calculationused to smooth out the variations in a time series to identify a trend
what are the Steps to calculate Time Series using Moving Averages
- Identify the period, i.e. is quarterly, yearly, monthly (identify the length of the cycle)\
- Calculate the total of the period taken
- Average the total value (this is called the trend), and place it at the midpoint of the cycle.
- Repeat the process for the next cycle, moving on just one period.
- Calculate the Seasonal Variation
what is the additive model
Here the seasonal variation is expressed as an absolute amount to be added on to the trend to find the actual result, e.g. ice cream sales in summer are expected to be $200,000 above the trend.
Forecast/Actual = Trend (T) + Seasonal variation (S)
what is the multiplicative model
Here the seasonal variation is expressed as a ratio / proportion /percentage to be multiplied by the trend to arrive at the actual figure, e.g. ice cream sales
are expected to be 50% more than the trend.
Forecast/Actual = Trend (T) × Seasonal variation (S)
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what are the Limitations of time series analysis
- There is an assumption that what has happened in the past is a reliable guide to the future.
- There is an assumption that a straight-line trend exists.
- There is an assumption that seasonal variations are constant, either in actual values using
the additive model (such as dollars of sales) or as a proportion of the trend line value using
the multiplicative model.
