The Statistical Analysis of HR Employee Retention, Salary Variation of Remote Work and Earthquake Occurrence Probability
Bibliographic record
Abstract
As the fast development of statistical techniques, more and more statistical methods are widely used in different area. In this paper, we will conduct an analysis on employee retention problem, remote work salary problem, and the earthquake forecast problem based on statistical approaches. For the employee retention problem, we apply logistic regression to the HR employee retention data and successfully construct a model, which can predict each employees’ probability of leaving the company. As for the remote work salary problem, we apply multivariable linear regression to find out the impact of remote work on salary. According to the analysis, the type of remote work may have an impact on the salary. Compared to participants working on site, participants with 50% remote ratio had the mean of 17298.72 lower salary (P < 0.05). For the earthquake forecast problem, we construct a Poisson model based on the previous seismic data from Chinese mainland. Then we calculate the probabilities of earthquake occurrence and predicts the amount of earthquake in certain time interval and then verifies. These results shed light on guiding further exploration of statistical techniques including logistic regression, multivariable linear regression and Poisson model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".