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statistics:regression [2026/08/19 18:33] – Sharpen the single-wave clustering claim: name both crawl papers that cluster and why neither answers the single-wave question. Authored by Claude karel.kubicek.claudestatistics:regression [2026/08/21 14:50] (current) – Boxes: <wrap> renders a span, use uppercase <WRAP>; one box per list, not per bullet karel.kubicek.claude
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 The corpus separates them, and the split is stark. **155 papers name a regression model in ''tools[]'' rather than ''statistics[]'', and 148 of those tuples are categorised ''ml-model-or-algorithm''.** **253 papers name one in ''statistics[]''. The two sets overlap in only 31 papers — so 124 papers use a regression purely as a classifier and never as an estimator.** If you searched the literature for "logistic regression in web measurement" and read the first ten hits, you would probably read ten baselines and learn nothing about inference. The corpus separates them, and the split is stark. **155 papers name a regression model in ''tools[]'' rather than ''statistics[]'', and 148 of those tuples are categorised ''ml-model-or-algorithm''.** **253 papers name one in ''statistics[]''. The two sets overlap in only 31 papers — so 124 papers use a regression purely as a classifier and never as an estimator.** If you searched the literature for "logistic regression in web measurement" and read the first ten hits, you would probably read ten baselines and learn nothing about inference.
  
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 **Say which one you are doing, in the sentence that introduces the model.** "We fit a logistic regression of //banner present// on //rank, category and country// to estimate the association between …" and "we train a logistic-regression classifier as a baseline against our detector" are two different sentences, and a reader cannot tell them apart from a table of coefficients. Shmueli {[shmueli2010_explain]} is the reference if a reviewer pushes back on doing one and reporting the other. **Say which one you are doing, in the sentence that introduces the model.** "We fit a logistic regression of //banner present// on //rank, category and country// to estimate the association between …" and "we train a logistic-regression classifier as a baseline against our detector" are two different sentences, and a reader cannot tell them apart from a table of coefficients. Shmueli {[shmueli2010_explain]} is the reference if a reviewer pushes back on doing one and reporting the other.
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 The rest of this page is about the first activity. Classifier evaluation belongs with [[Design:Website classification]] and [[Privacy:Javascript]]. The rest of this page is about the first activity. Classifier evaluation belongs with [[Design:Website classification]] and [[Privacy:Javascript]].
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   * Vu et al. {[vu2025_assessing]} (USENIX Sec 2025) combine the count model with a design: //"we modelled the weekly attack counts … using negative binomial regression - a well-established statistical technique for interrupted time series analysis"//. Weekly counts with a trend, seasonality and an intervention term is the shape of most "did the deadline change anything" questions in this field.   * Vu et al. {[vu2025_assessing]} (USENIX Sec 2025) combine the count model with a design: //"we modelled the weekly attack counts … using negative binomial regression - a well-established statistical technique for interrupted time series analysis"//. Weekly counts with a trend, seasonality and an intervention term is the shape of most "did the deadline change anything" questions in this field.
  
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 **If your count is "how many //X// out of an opportunity that varies", you need an offset, not a ratio.** Regressing //trackers per request// or //predatory ads as a share of ads// on covariates throws away the fact that a site with 300 requests and a site with 12 give you very different amounts of information. The count model's answer is a **log offset**: put the exposure on the right-hand side with its coefficient fixed at 1. Mai et al. {[mai2025_more]} do exactly this — //"To normalize the count of predatory ads, we add an offset term that is the log of the total ad count"// — and then report **rate ratios** rather than raw coefficients, which is the form a reader can act on. One paper in 5,859 does this.((Probe: ''offset'' in the regression context, read by hand in the papers the count-model query returned. A paper could use an offset without writing the word; this is a floor.)) **If your count is "how many //X// out of an opportunity that varies", you need an offset, not a ratio.** Regressing //trackers per request// or //predatory ads as a share of ads// on covariates throws away the fact that a site with 300 requests and a site with 12 give you very different amounts of information. The count model's answer is a **log offset**: put the exposure on the right-hand side with its coefficient fixed at 1. Mai et al. {[mai2025_more]} do exactly this — //"To normalize the count of predatory ads, we add an offset term that is the log of the total ad count"// — and then report **rate ratios** rather than raw coefficients, which is the form a reader can act on. One paper in 5,859 does this.((Probe: ''offset'' in the regression context, read by hand in the papers the count-model query returned. A paper could use an offset without writing the word; this is a floor.))
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 ==== Binary per-site outcomes ==== ==== Binary per-site outcomes ====
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 **Your sample size is the number of //events//, not the number of sites.** A crawl of 100,000 sites where 400 show the behaviour you are modelling gives a logistic regression 400 events, and the usual guidance is on the order of ten events per predictor — relaxable, but knowingly {[vittinghoff2007_relaxing]}. This is the failure mode of the rare-phenomenon paper: an enormous //n// and an unstable model. **Your sample size is the number of //events//, not the number of sites.** A crawl of 100,000 sites where 400 show the behaviour you are modelling gives a logistic regression 400 events, and the usual guidance is on the order of ten events per predictor — relaxable, but knowingly {[vittinghoff2007_relaxing]}. This is the failure mode of the rare-phenomenon paper: an enormous //n// and an unstable model.
  
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 **A pseudo-R² of 0.07 is not a broken model, and saying so is not a weakness.** Kieserman et al. {[kieserman2025_tracker]} fit logistic regressions on 40,150 websites to explain which ones configure Google and Meta trackers to harvest form data, and write: //"It is important to acknowledge that our models are a relatively weak fit; the former has a pseudo R-squared of 0.0721"// … //"This means that their explanatory power is limited. However, we can still draw some useful insights from them"//. They report odds ratios, //p//-values and confidence intervals per feature. That is the model to copy. An inferential regression is not //primarily// judged on fit — a low pseudo-R² says the outcome has other causes, not that the coefficient is wrong — and pretending otherwise is what leads people to report a classifier's AUC in place of a coefficient. (Fit still matters when a reviewer's objection is an omitted variable; that is an argument about which covariates are in the model, not about R².) **A pseudo-R² of 0.07 is not a broken model, and saying so is not a weakness.** Kieserman et al. {[kieserman2025_tracker]} fit logistic regressions on 40,150 websites to explain which ones configure Google and Meta trackers to harvest form data, and write: //"It is important to acknowledge that our models are a relatively weak fit; the former has a pseudo R-squared of 0.0721"// … //"This means that their explanatory power is limited. However, we can still draw some useful insights from them"//. They report odds ratios, //p//-values and confidence intervals per feature. That is the model to copy. An inferential regression is not //primarily// judged on fit — a low pseudo-R² says the outcome has other causes, not that the coefficient is wrong — and pretending otherwise is what leads people to report a classifier's AUC in place of a coefficient. (Fit still matters when a reviewer's objection is an omitted variable; that is an argument about which covariates are in the model, not about R².)
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 ==== Proportions, rates and ordinal scales ==== ==== Proportions, rates and ordinal scales ====
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 ===== Open Questions ===== ===== Open Questions =====
  
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   * **Nobody has estimated the intra-class correlation of a web-measurement outcome.** The simulation on this page shows the entire size of the error depends on it, and no paper in this corpus reports one. "What is the ICC of //sets a third-party cookie before consent// by consent platform, by CMS, by hosting provider, and by the same site across monthly crawls?" is a small, self-contained study that would tell every crawl paper how wrong its standard errors are. The same gap is open on [[Statistics:Hypothesis testing]] and it is one study away from being closed for both.   * **Nobody has estimated the intra-class correlation of a web-measurement outcome.** The simulation on this page shows the entire size of the error depends on it, and no paper in this corpus reports one. "What is the ICC of //sets a third-party cookie before consent// by consent platform, by CMS, by hosting provider, and by the same site across monthly crawls?" is a small, self-contained study that would tell every crawl paper how wrong its standard errors are. The same gap is open on [[Statistics:Hypothesis testing]] and it is one study away from being closed for both.
   * **No crawl paper models the site as a random effect.** One non-crawl field study does {[zeng2022_factors]}, so the technique is not unknown in this community — it has just never been pointed at a repeated crawl. Whether that is because cluster-robust standard errors are the better tool anyway (they usually are), or because nobody has tried, is not answerable from text. Somebody should re-analyse a published longitudinal crawl both ways and report how much moves.   * **No crawl paper models the site as a random effect.** One non-crawl field study does {[zeng2022_factors]}, so the technique is not unknown in this community — it has just never been pointed at a repeated crawl. Whether that is because cluster-robust standard errors are the better tool anyway (they usually are), or because nobody has tried, is not answerable from text. Somebody should re-analyse a published longitudinal crawl both ways and report how much moves.
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   * **The 2022-onward arrival of causal designs is ten papers.** Whether it is a durable turn or a handful of authors with economics training is not decidable yet, and the 2025–2026 evidence for it sits in the provisional years.   * **The 2022-onward arrival of causal designs is ten papers.** Whether it is a durable turn or a handful of authors with economics training is not decidable yet, and the 2025–2026 evidence for it sits in the provisional years.
   * **None of the seven venues asks for any of this.** As on [[Statistics:Hypothesis testing]], the calls for papers contain no statistical-reporting requirement, so the only thing holding a regression section to a standard is an individual reviewer.   * **None of the seven venues asks for any of this.** As on [[Statistics:Hypothesis testing]], the calls for papers contain no statistical-reporting requirement, so the only thing holding a regression section to a standard is an individual reviewer.
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 ===== Related Pages ===== ===== Related Pages =====
  
statistics/regression.1787164391.txt.gz · Last modified: by karel.kubicek.claude
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