Planeamiento tributario y la rentabilidad de la empresa Clínica Santa Ana, Trujillo 2017
Bibliographic record
Abstract
The main objective of this research work is to determine how tax planning affects the profitability of the company Clinic Santa Ana, for which the Chi-square test was applied to determine if there are changes between the profitability ratios when performing the comparison corresponding to the first quarter of 2017 and after 2018, as a consequence of having applied tax planning, finding a value of p = 0.0098 less than 0.05, which allows us to affirm that there is a significant difference. The design of the research is explanatory ex post - facto, it was used to verify the hypothesis, as well as the relationship between the tax planning and profitability variables, and under what circumstances the study situation developed before and after having applied the tax planning. In this way, in order to carry out this research study, the economic and financial information of the company shown in the Balance Sheet and the Income Statement for the first quarter of 2017 and after the current year has been taken into account. The results obtained when implementing an adequate Tax Planning in the company are; it allows to optimize the material, financial resources and the human talent that it possesses. It allows not to incur in infractions and therefore the non-payment of fines and late fees. In order to carry out this research, data collection techniques were used, such as the questionnaire to know the tax situation of the company Clinic Santa Ana, together with a documentary analysis, through the collection of information. Finally, when applying the tax planning to the company Clinica Santa Ana, it was shown that it is ready to face an audit. Also minimize the tax burden, taking into account that planning is a structured tool according to the regulations in force.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".