Nayland College

Nayland College - Mathematics

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NZAMT NZQA NZ Grapher NZ Maths Census at School Study It

3.8 Time Series HOME | Achievement Objectives | Overview | The Statistics Cycle
NZ Grapher: The best choice | iNZight: Getting Started , Importing Data
Using EXCEL: Smoothing, Trend, Seasonal Effects, Graphing, Forecasts, Seasonal Adjustment, Non-Linear models, Comparing Excel & iNZight
Report Writing: Summary | Introduction | Trend | Decompose | Recompose | Seasonal Effect | Forecasts | Robustness | Additive vs Multiplicative model | Comparing | Combining | Conclusion | Practice Assessment

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
Seasonal Decomposition and Forecasting, Part I
Seasonal Decomposition and Forecasting, Part II
Comparing Series

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,

 

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