Determine the Relationship between Population, Residents Income and the Price of Real Estate using Linear Regression
Bibliographic record
Abstract
People are constantly looking for the perfect spot to live, but the price of a home isn't always in a reasonable range for them to afford it. For example, in Montreal, the price of a home is relatively higher, while in Northern New Brunswick, it is relatively lower. As is known to all, the population and income of people are different in each region. As a result, there could be a connection between the size of the population, the amount of money individuals make, and the cost of real estate. To help people to find the proper room to live, it is very meaningful to determine the factors that influence the price of a home. To start with, the table of population and peoples income in 18 different regions is listed. According to the table, the graphs of the relationship between population, peoples revenue and housing costs are sketched by using R studio. Finally, using linear regression, the conclusion that the more people live in a region, the higher price of a home is. Besides, the more income people have in a region, the higher price of a home is.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".