Total sales of lightweight automobiles in Mexico, 1988-2016
Bibliographic record
Abstract
The automotive industry in Mexico has been strengthened in such a way that the country is already part of the Top 15 of those that produce and sell the most cars in the world. From 2010 to 2016, it went from place 16 to 12 among the countries that sell more vehicles in the world, according to data from the International Organization of Automobile Manufacturers (OICA). Six years ago, Mexico sold 503 thousand 748 vehicles, which placed it in 16th place worldwide. The country was below Russia (1 million 912 thousand) Canada (694 thousand 349) or Spain (982 thousand 015) (HuffPost, 2017). The objective of the work was to analyze the variables that most influence the total sales of light vehicles in Mexico. To carry out the study, a multiple linear regression model of the total sales of light automobiles in Mexico was elaborated according to the exchange rate, the monthly average remuneration, the interest rate, unemployment and the inflation rate. Of the results obtained, the variation of the VTA according to the coefficient of determination (R2) was explained in 93.75% by the variables included in the equation, of which, the most statistically significant variables were the monthly average remuneration, the Unemployment rate and the exchange rate. According to the elasticities, the greatest effect on sales was the average monthly remuneration and the exchange rate. Although the interest rate and inflation are very important variables and were not significant.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".