"This presentation will demonstrate that the quantile distribution of the readings is likely not exponential due to significant errors in the datasets. These errors can be attributed to the failure of CONDE engineers and contractors to properly engineer the sensors. Conducting experiments during active business operations is highly detrimental. Consequently, management must prioritize the perfection of IoT engineering as their investment is at risk, not CONDE's."

testing

As we progressed with our analysis, we realized that CONDES and the other IT engineers failed to meet the expected output from their sensors. The presentation reveals errors in the Data Quality (DQ) and statistics of the sensor readings. Such results are highly unlikely according to engineering theory and could potentially lead to mortality in the products these sensors are meant to serve. This situation underscores the need to replace these sensors. While sensors can be accurate in ideal conditions, various factors can affect their performance, especially in vertical farming applications. Given the current erroneous data, the situation becomes even more critical. If we aim for accuracy, which is crucial in this context, we must replace these faulty sensors with properly engineered alternatives. This task should be entrusted to accredited professionals rather than experimentalists who often fall short of the required standards. By doing so, we can ensure the reliability and precision necessary for successful vertical farming operations.

This dashboard uses a dataset to analyze various factors that may affects one's healthy weight level and determines that what one consumes is crucial to maintaining a healthy weight -- even more than how much physical activity one does.

reproducible research peer assessment 2