<ul><li>Log transformation is commonly used to normalize extremely skewed data in data analysis.</li><li>The article discusses a project analyzing energy consumption of training AI models.</li><li>The dilemma of whether to use log-transformed response variables or log link functions is explored.</li><li>Comparison of models using AIC values and diagnostic plots is detailed, favoring log-transformed models.</li><li>The article interprets coefficients in models and discusses the impact of log transformations on interpretation.</li><li>Results show log-linked models provide more sensible outcomes compared to log-transformed models.</li><li>The importance of understanding the difference between log transformation and log link in modeling is emphasized.</li><li>Log transformation may distort variation and noise in the data, affecting model reliability.</li><li>Detailed comparisons, plots, and interpretations are presented to illustrate the impact of transformation decisions.</li><li>Utilizing log-linked models can provide more accurate and meaningful results in data analysis.</li></ul>

Log Link vs Log Transformation in R — The Difference that Misleads Your Entire Data Analysis

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