Teaching/Learning Multiple Regression Using Historical and Modern Family Data
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.
Bibliographic record
Abstract
To deal with the new concepts involved when moving up from simple to multiple regression, I have found that it helps to use readily-understood real-world datasets that involve an engaging question, measurements that students can personally relate to, such as those involving themselves and their families, and just two regressors. I describe, provide copies of, and suggest possible didactic uses of “two-regressor” datasets involving family data. The late-19th century datasets, which gave rise to the very term “regression,” involve easily measured variables relating to students and their families, two weakly-correlated parental regressors, and a written protocol that would allow a modern version to be quickly assembled by today’s students. The recent datasets involve a less easily measured but easily understood Y variable that can be modeled within the ordinary or the Poisson (generalized) linear model regression framework, two readily obtained but very strongly-correlated parental regressors, and an engaging example of the striking difference between the regression coefficients in the two “1-regressor-at-a-time” and the one “2-regressors-at-once” regression models.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it