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Record W3021474464 · doi:10.1002/0470011815.b2a09025

Linear Regression, Simple

2005· other· en· W3021474464 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEncyclopedia of Biostatistics · 2005
Typeother
Languageen
FieldMathematics
TopicAdvanced Statistical Methods and Models
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSimple (philosophy)Linear regressionMathematicsSimple linear regressionValue (mathematics)Regression analysisStatisticsStatistical inferenceRegressionProper linear modelVariable (mathematics)Regression diagnosticInferenceApplied mathematicsEconometricsPolynomial regressionComputer scienceArtificial intelligenceMathematical analysis

Abstract

fetched live from OpenAlex

Abstract Simple linear regression involves finding the best‐fitting curve of a suitable functional form that relates the value of an explanatory variable, X , and the mean value of a response variable, Y , given X . The goals of regression modeling are to determine whether Y and X are associated in some systematic way, and to estimate or predict the value of Y , or its mean, corresponding to a known value of X . We describe estimation, statistical inference, and diagnostic checking of the assumed model and any associated unknown parameters, giving due importance to their statistical and scientific interpretations. An example concerning the relationship between systolic blood pressure and age in men illustrates these concepts concretely.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.077
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.051
GPT teacher head0.408
Teacher spread0.357 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it