Look at the following graph? What trends can you see in the data?

Time Series Analysis aims: understand what is meant by correlation be able to map figures and identify trends be able to use trends to forecast data

Correlation The degree to which there is a relationship between two or more random variables The closer the relationship the higher the degree of correlation

Trends Looking for patterns in data collections Frequency and reliability of trends Impact of external factors, e.g. seasonal variation, random events, cyclical trends

Averages Averages are a measure of central tendency – the most likely or common item in a data series Quarterly moving averages

Time-Series Analysis Used to analyse movements of a variable over a time period – usually years, quarters, months, etc. Importance of assessing the: Trend Seasonality Key moments Magnitude

Quantitative Makes use of all the statistical data collected by the firm and by other firms/organisations to help inform decision making Surveys Sales data Impact on sales Primary data – collected by the firm themselves Data collected by others and used by the firm, e.g. Office of National Statistics (ONS), Gallup, Mori, Mintel

Qualitative Focus groups - a group of individuals selected and assembled by researchers to discuss and comment on, from personal experience, a topic, issue or product User groups – similar to focus groups but consisting of those who have experience in the use of a product, system, service, etc. Panel surveys – repeated measurements from the same sample of people over a period of time Delphi method – calls on the expertise and insights of a panel of experts to help with forecasting – seen as being more reliable than data analysis only Could be drawn together from around the world as there is no need to have people together at the same time In-house judgements – Use the expertise and judgements of those involved in the business in aiding and making judgements

Forecasting Advantages and disadvantages: Data from several years can give accurate guides to future performance Statistical techniques can make the data informative and useful All depends on the quality of the data and the accuracy of the techniques used to analyse the data