Analyzing climate data from 1850 to 2015 - A White Paper from XLSTAT




The aim of this analysis is to describe, visualize and forecast temperature changes on a global scale. To do this, we use XLSTAT time series analysis. Analyzing temporal data is a specific branch of data and statistical analysis. This type of data is called time series data. We will present various methods that can be used to analyze this type of data.

The data used here are monthly air and ocean temperatures from 1850 to 2015.


  1. Introduction

  2. Visualizing time series data

  3. Descriptive analysis of time series data

  4. Decomposition of Seasonality in a time series

  5. The Holt-Winters Method

  6. Testing for stationarity

  7. Time Series differencing

  8. Test for Stationarity on the Series Following Differencing

  9. ACF and PACF on the Series Following Differencing for the ARIMA Model

  10. ARIMA Model

  11. Conclusion


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This module focuses on Analysis of Variance, but this technique makes assumptions about the underlying distributions in our data

This course covers the excellent features in XLSTAT for investigating, visualising and modelling data sets with measurements on many variables.

Learn the basics of descriptive statistics, multivariate data analysis, tests and modeling with XLSTAT Basic+

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