The determinant of loan disbursed by commercial bank in Malaysia/ Nurul Ain Izyanti Darno
Bibliographic record
Abstract
Commercial bank is one of the keeping money framework in Malaysia. They are a few sorts of commercial bank in Malaysia which is open segment, private segment, and remote. The common parts of the commercial bank is to supply money related administrations to common open, businesses and companies. This parts is exceptionally vital in arrange to guaranteeing financial, social soundness, and the supportability of the economy. There are two essential work of the commercial bank which is they acknowledge different sorts of store from open particularly from its clients, counting sparing account stores, repeating account stores, and settled stores. The second work is to supply advances and progresses of different shapes, counting and overdraft offices and cash credit. This two function is likely have their own claim of relationship. In this research, I would like to learn more about the factor that influence the amount of loan disbursed by commercial bank in Malaysia by using the data from the 1st quarter of year 2000 until the 4th quarter of the year 2017 with the total of 72 observation. The data that being used is the dependent variables which is the loan disbursed by commercial bank, and also the independent variables which is the Malaysian GDP, Malaysia's Inflation Rate, Saving Deposit and also the nonperforming loan. This data will be tested to achieve the objective of this research which is to investigate the relationship between the dependent variables and the independent variables. The test that important to run, was the regression analysis as it can answer the relationship between dependent variables and also independent variables. In this research, GDP, inflation rate, and also saving deposit have positive relationship with the loan disbursed and only non-performing loan has the negative relationship with the loan disbursed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".