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# iNZight Overview

 20 An Overview of what to do with iNZight Remember we are showing evidence of each component of the statistical cycle Focus on the discussion and interpretation in context The excellent (Auckland University videos are worth re watching: Introducing Time Series Data Victoria Accommodation takings csv data (although last 6 months data removed for iNZight to work An iNZight overview (Class notes) thanks to Robyn Headifen at Rangitoto College

 (1) Input some data (data sets are already loaded into 'Nayland College iNZight' (2) Make a graph the time series data with trend line. (find the gradient) Discussion: Of the Long Term Trend in very general terms but in context (3) Decompose the data into trend, seasonal & residual. (work out the % contribution of each component) Discuss relative effect of seasonal effect vs long term trend Residuals - Any unusual observations - do they warrant further investigation (4) Recompose the data. Discuss recomposed data and individual data points (above and below average) (5) Graph the Individual & Estimated Seasonal effects. Discuss the estimated seasonal effects ie what one seasonal cycle is like and possible reason why? Discussion in context Discuss the individual seasonal effects and how they may have changed over time. (6) Make Predictions of the next two cycles of data (with confidence intervals) Discuss the predictions in context Discuss the accuracy or margin or error of the predictions Discuss how might the forecasts be used (and who might use them) (7) 'Compare Series' between different variables that are available. Discuss what you notice about the comparative data series - similarities, differences, possible relationships, reasons, causes, links etc Discuss and compare the Trend Lines. Discuss and compare the Average Seasonal Effects (red line) and the Seasonal Effects for each cycle (gray lines) (8) Combine variables to make a new variable into the series and analyse. Discuss why the new variable has been added. Discuss the further insight and information provided by the new variable (9) Test model Robustness by removing the last three data values then estimating them again Discuss whether the actual data values are within the 95% confidence intervals for the new predictions - or not. Are the estimates above or below expected? (10) Compare the 'additive and the 'multiplicative' models. Discuss & compare & contrast the two models. Which may be more appropriate and why? Compare forecasts,