This panel provides the results from the [Multivariate Analysis](article:29), with several Large Language Models assisting you in interpreting the coefficients of the model and linking them to possible explanations found in the context documents.

## Report generated by Large Language Models

This report has been generated by several Large Language Models to clearly explain the statistical results using the context documents. A concise summary is provided in the first section, followed by an in-depth dive into specific key findings, and finally, a technical explanation of the model used.

![ai_assistant.png](TRGES8wQZwrL)

## Relative Risk Multipliers

The relative risk multipliers constitute the main results of the model and form the foundation of the report generated in the previous section. In this section, you can directly access the statistical results.

![risks_multipliers.png](R1jwg6MMgBo0)

### Values

The risk multipliers are computed from a Cox Proportional Hazards model. **They indicate how much a specific covariate affects the likelihood of equipment failure**. A risk multiplier of 1 indicates no effect on the hazard. Values above 1 imply increased risk, while values below 1 imply decreased risk.

- For **categorical covariates**, each category has its own risk multiplier compared to an arbitrary reference category, which is set as the baseline with a risk multiplier of 1.
- For **numerical covariates**, a risk multiplier of 2 means that a one-unit increase in this covariate doubles the hazard, assuming all other factors are held constant. Similarly, a risk multiplier of 0.5 means the hazard is halved.

**Note:** Since reference values for categorical covariates are chosen arbitrarily, the displayed values should be interpreted relative to each other.

The different levels of blue indicate the significance of the impact. The significance of a statistical result is based on the p-value, which represents the probability that the observed effect occurred by chance. Smaller p-values indicate a lower probability that the effect is due to chance.

### Statistical Significance

By hovering over the bars in the graph, you can see the level of statistical significance:

1. **Highly Significant (p-value < 0.01):** A p-value smaller than 0.01 indicates a highly significant result. There is very strong evidence that the observed effect is not due to random chance. In most cases, a p-value below 0.01 is considered robust and highly reliable.

2. **Significant (p-value < 0.05):** A p-value between 0.01 and 0.05 is labeled as significant. The impact is likely real, but the result is slightly less certain than with a highly significant p-value.

3. **Moderately Significant (p-value < 0.3):** A p-value between 0.05 and 0.3 is considered moderately significant. This range indicates weaker evidence of an effect. There is some evidence of an effect, but the result is not strong enough to draw firm conclusions.

4. **Not Significant (p-value >= 0.3):** A p-value of 0.3 or higher is classified as not significant. This suggests that the observed effect could easily be due to random variation, and there is insufficient evidence to indicate a meaningful impact.