Exploratory Data Analysis: Quantile-Quantile Plots for New York’s Ozone Pollution Data

3 Responses to Exploratory Data Analysis: Quantile-Quantile Plots for New York’s Ozone Pollution Data

  1. anspiess says:

    Using the ‘fitDistr’ function of the ‘propagate’ package gives me the following order of best fitted distributions to ‘ozone':

    library(propagate)
    res <- fitDistr(ozone)
    res$aic

    Distribution AIC
    16 Johnson SU -838.1165
    4 Log-normal -834.4381
    11 Generalized Trapezoidal -828.3748
    12 Gamma -812.3913
    19 4P Beta -803.8396
    18 3P Weibull -802.6010
    8 Triangular -801.4067
    14 Laplace -799.1498
    3 Generalized normal -797.8691
    15 Gumbel -797.0433
    13 Cauchy -796.9815
    5 Scaled/shifted t- -795.3024
    6 Logistic -783.7429
    1 Normal -775.1892
    2 Skewed-normal -773.1892
    9 Trapezoidal -760.0612
    7 Uniform -720.5523
    10 Curvilinear Trapezoidal -718.7252
    20 Arcsine -694.8609
    21 von Mises -578.6548
    17 Johnson SB -566.5763

    with JohsonSU and log-normal on first/second place. Haven't checked the qqplots for that though…

    Cheers,
    Andrej

    • Thanks for taking the initiative to conduct this analysis and share the results with us, Andrej! I appreciate you mentioning this function in the past, and it’s nice to see it in action!

      I like to use multiple tools to explore a data set, and it’s best to combine the wisdom of multiple tools to gain that exploratory inference. Thanks for demonstrating another tool that I will to my bag of tricks!

Your thoughtful comments are much appreciated!

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