Application of Linear Regression in Predicting Education Level and Income of Residents (Case Study: Desa Padang Cermin)
Bibliographic record
Abstract
Linear regression methods were used to predict education and income levels in Padang Cermin Desa. Padang Cermin Desa has twelve hamlets and a total of 13,055 residents, with 6,135 men and 6,920 women, and 2545 households. The aim of this study is to raise government and community awareness of the importance of education for welfare and a better life in the future. Using existing data, this study can provide a clear picture of the relationship between education levels and income as well as relevant recommendations to improve the quality of life in Padang Cermin Desa. This research uses a quantitative case study design with secondary data collected through hamlet heads and semi-structured interviews. The linear regression equation Y 36900147.57 + 2516320.971X is based on the MAPE value with a result of 28.25% and an accuracy rate of 71.75%. In the case study of applying linear regression to predict the education level and income of residents, the following are some conclusions: The prediction results show that people with elementary school education are estimated to have an income of 421,897,256.1, while people with secondary school education are estimated to have an income of 311,179,133.4. People who completed a senior high school education are estimated to have the highest income of IDR 457,125,749.7, showing the importance of secondary education. Higher education and vocational education still have the potential to be improved, although the income of the population with education is estimated at IDR 64,579,678.25 for Diploma I/II/III and IDR 79,677,604.08 for Diploma IV/Strata I.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".