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 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 | 0.002 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".