Extrapolation
Uses trends from historical data to forecast the future.
Moving averages
A moving average takes a data series and “smoothes” the fluctuations in data to show an average. The aim is to take out the extremes of data from period to period.
Extrapolation is not just drawing a straight line and assuming that conditions will all remain stable or equivalent to historial data. It requires attention to be paid to the various different internal and exterenal factors.
Factors include:
- product life cycle
- pace of technological innovation
- market saturation
- etc
Pros and Cons of Extrapolation
Pros
- A simple method
- Not much data required
- Quick and cheap
Cons
- Unrealiable
- Assumes the continuation of past trends
- Ignores many qualitative factors (changes in fashion or taste for instance)
Correlation
Correlation is the strength of the relationship between two variables.
Independent Variable
The factor that causes the dependent variable to change.
X axis
Dependent Variable
The variable that is influenced by the indepedent variable. Y axis
Types of correlation
Positive correlation
A positive relatioshiop, when the independent increases so does the dependent.
Negative correlation
A negative relationship, when the independent decreases so does the dependent.
No correlation
There is no obvious relationship between the two variables.
Strong correlations are very definite, whereas a weak correlation means the data is quite spread.