The LTS Estimator Tool helps you calculate long-term savings for your investments.

## How to Use the LTS Estimator Calculator

Fill in all the required parameters in the form above:

**Water Volume (liters):**Enter the total volume of water in liters.**Experience Factor (0.5 to 2):**Enter a value between 0.5 (least experience) and 2 (most experience).**Substrate Type Factor:**Enter the following value based on your substrate type:- Sand: 1.1
- Gravel: 1
- Soil: 0.9

**Bioload Factor:**Enter the bioload type factor:- Low: 0.8
- Medium: 1
- High: 1.2

**Lighting Intensity Factor:**Enter the lighting intensity factor:- Low: 0.8
- Medium: 1
- High: 1.2

**CO2 Injection Factor:**Enter if you use CO2 injection:- No: 0.8
- Yes: 1.2

**Plant Density Factor:**Enter the plant density factor:- Sparse: 0.8
- Moderate: 1
- Dense: 1.3

## Explanation of Results

The result given by the calculator provides an estimated Long-Term Sustainability (LTS) score for your aquarium setup. This score gives you an idea of how sustainable your aquarium setup should be based on the available parameters.

## Limitations

- This calculator provides an approximation; actual results may vary based on other unforeseen factors.
- The factors used in the calculator are generalized and may not adhere to all specific cases.
- Ensure all input values are correct and accurately represent your setup to obtain reliable results.

## Use Cases for This Calculator

### Use Case 1: Calculating Mean for LTS Estimator

Calculate the mean value of a dataset for the Long-Term-Suppliers (LTS) estimator. This step is important as it helps in determining the central tendency of the data, which is essential for accurate estimation.

### Use Case 2: Determining Sample Standard Deviation

Compute the sample standard deviation of the data points to assess the spread of values. This is crucial for the LTS estimator as it accounts for the variability within the dataset, ensuring reliable estimates.

### Use Case 3: Outlier Detection and Removal

Identify outliers in the dataset that could potentially skew the LTS estimator results. Removing outliers improves the accuracy of the estimation by ensuring that extreme values do not unduly influence the outcome.

### Use Case 4: Implementing LTS Estimator Algorithm

Apply the LTS estimator algorithm to the cleaned dataset to calculate robust estimates. This algorithm helps in mitigating the impact of outliers and noise, providing more stable and accurate results.

### Use Case 5: Confidence Interval Calculation

Determine the confidence interval for the LTS estimator to quantify the uncertainty associated with the estimates. Knowing the range within which the true parameter lies enhances the reliability of the estimation process.

### Use Case 6: Assessing Residuals for LTS Estimator

Evaluate the residuals of the LTS estimator to check the quality of the model fit. Examining residuals helps in validating the assumptions underlying the estimation process and identifying any potential issues.

### Use Case 7: Cross-Validation for LTS Estimator

Perform cross-validation techniques to assess the predictive performance of the LTS estimator. Cross-validation helps in evaluating how well the estimator generalizes to new data, ensuring its robustness.

### Use Case 8: Sensitivity Analysis of LTS Estimator

Conduct sensitivity analysis to understand the impact of different parameters on the LTS estimator results. Analyzing sensitivity helps in identifying the factors that significantly influence the estimation process.

### Use Case 9: Visualization of LTS Estimator Output

Create visualizations such as plots or graphs to represent the LTS estimator results effectively. Visual aids enhance the interpretation of the estimates and facilitate communication of the findings to stakeholders.

### Use Case 10: Continuous Refinement of LTS Estimator

Iteratively refine the LTS estimator model based on feedback and new data inputs. Continuous improvement ensures that the estimator stays relevant and reliable in addressing evolving business needs.