IRI
Statistical Analysis Tutorial
Table of Contents
Measures of Central Tendency
Running and Weighted Averages
Climatologies and Standardized Anomalies
Data Homogeneity
Data Stationarity
Measures of Dispersion
Correlation
Climate Indices
Frequency Distributions
Singular Value Decomposition
Interpolation Techniques
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Table of Contents


Measures of Central Tendency Running and Weighted Averages Climatologies and Standardized Anomalies
Data Homogeneity Data Stationarity Measures of Dispersion
Correlation Climate Indices Frequency Distributions
Singular Value Decomposition Interpolation Techniques .


  1. Measures of Central Tendency
    2.
    1. Introduction
    2. Mean
    3. Median
    4. Trimmed Mean
    5. Trimean
    6. Interpreting Measures of Central Tendency
  2. Running and Weighted Averages
    3.
    1. Running Average
    2. Weighted Average
  3. Measures of Dispersion
    4.
    1. Introduction
    2. Range
    3. Standard Deviation
    4. Root Mean Square Anomaly / Root Mean Square
    5. Interquartile Range
    6. Median Absolute Deviation
    7. Trimmed Variance
  4. Data Homogeneity
    5.
    1. Introduction to Homogeneous Data
    2. Example: Analyzing the Homogeneity of a Dataset
  5. Data Stationarity
    1. Introduction
    2. Approaches to Nonstationary Data
    3. Analyzing and Transforming Cyclo-Stationary Data
  6. Climatologies and Standardized Anomalies
    7.
    1. Introduction
    2. Climatologies
    3. Composites
    4. Standardized Anomalies
  7. Correlation
    8.
    1. Introduction
    2. Pearson Product-Moment Correlation
    3. Spearman Rank Correlation
    4. Lagged Correlation
    5. Autocorrelation
    6. Significance Tables and Correlation
  8. Climate Indices
    9.
    1. Introduction
    2. Example: Creating the Southern Oscillation Index
    3. Example: Creating the Niņo 3 Index
  9. Frequency Distributions
    10.
    1. Introduction
    2. Histograms
    3. Cumulative Frequency Distributions
  10. Singular Value Decomposition
    11.
    1. Introduction
    2. Analysis of Singular Value Decomposition
    3. Outline of Key Points
    4. Examples:
      1. SVD Analysis of North Atlantic Sea Surface Temperature Anomalies
      2. SVD Analysis of North Atlantic Mean Sea Level Pressure Anomalies and Their Relation to the NAO
      3. Correlation of a SVD Time Series of Reanalyzed Mean Sea Level Pressure Anomalies with a SVD Time Series of Reconstructed SST Anomalies in the North Atlantic

    5. Disadvantages of Unrotated Singular Value Decomposition
    6. Rotated Singular Value Decomposition
    7. Advantages / Disadvantages of Varimax Rotation
    8. Varimax Rotation of East Pacific Sea Surface Temperature Data
  11. Interpolation Techniques
    1. Introduction
    2. Linear Interpolation
    3. Cressman Analysis
    4. Weaver Analysis
      1. Example: Interpolate Monthly Surface Temperature Data
      2. Example: Regrid Normalized Difference Vegetation Index Data for South America
     

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