The first challenge should be interpreted as follows: 

There is a logistic robot (demo: https://miau.my-x.hu/miau/304/robotkar.MOV).

This robot is functioning - seemingly correctly.

Question: are we capable of deriving all of the rules behind the visible surface?

Testing scenarios are already prepared: https://miau.my-x.hu/miau/320/testing_task1.xlsx

Tasks: interpreting all test scenarios (XLSX: 1;...;12), understanding data/structures, deriving the rules of the black box system, defining substitution-characters/colours for the used pattern: ?(????) <-- detailed solutions for place-ID (A;B;C;D)

Not correct!

In Scenario #9 (Experiment #1), the input is:

• a = blue
• b = yellow
• c = red
• d = blue

Since there is no green cube in the input, there is no doubt that green should not appear as the first output in this Scenario. In other words, with no green present, the only possible color for the first output (cell E27) is yellow this time.

The correct solution is:

Color Layer:
Cell(E27)_old_all=?(????) <--Cell(E27)_new_colour=yellow
Cell(E28)_old_all=?(????) <--Cell(E28)_new_colour=blue
Cell(E29)_old_all=?(????) <--Cell(E29)_new_colour=red
Cell(E30)_old_all=?(????) <--Cell(E30)_new_colour=not-used

Number Layer:
Cell(E27)_old_all=?(????) <--Cell(E27)_new_number=1
Cell(E28)_old_all=?(????) <--Cell(E28)_new_number=2
Cell(E29)_old_all=?(????) <--Cell(E29)_new_number=1
Cell(E30)_old_all=?(????) <--Cell(E30)_new_number=not-used=0

Letters Layer:
Cell(E27)_old_all=?(????) <--Cell(E27)_new_letters=(b)
Cell(E28)_old_all=?(????) <--Cell(E28)_new_letters=(ad)
Cell(E29)_old_all=?(????) <--Cell(E29)_new_letters=(c)
Cell(E30)_old_all=?(????) <--Cell(E30)_new_letters=not-used=()

Based on the above rules that I have drawn from my observations, I solved the scenarios #10, #11 and #12

The first solution layer (Colours) is incorrect because:

• In Experiment #1, Scenario #9, there is no green colour present in the "Input9" column.
• This means the assignment:
  Cell(E27)_new_colour = green is incorrect.

Solution

Numbers

Letters

Cell(E27)_old_all=?(????) <-- Cell(E27)_new_letters=(b)  

Cell(E28)_old_all=?(????) <-- Cell(E28)_new_letters=(ad)  

Cell(E29)_old_all=?(????) <-- Cell(E29)_new_letters=(c)  

Cell(E30)_old_all=?(????) <-- Cell(E30)_new_letters=not-used=() 

Final Answer:
The first solution layer (colours) was incorrect due to the green value. The correct assignment should be yellow instead. Numbers and letters layers were correct. 

I have updated my Excel demo. Here’s a summary of the changes I made:

1. Filtering Incorrect Attempts:

   • I added a new column called Is_Correct in the raw_data sheet. This column flags an attempt as “Yes” only if the New_Color exactly matches the expected value for the corresponding cell (for example, for E27 the expected color is “yellow”, for E28 “blue”, for E29 “red”, and for E30 “not-used”).
   • This ensures that if a student submits an incorrect guess (e.g., “green” for E27), it is marked “No” and will not be counted in the pivot table.

2. Refined Pivot Table:

   • In the pivot table, I now aggregate only those rows where Is_Correct is “Yes”.
   • Additionally, I added an Overall Correct? column that indicates “Yes” only if the student has at least one correct attempt for each of the required cells (E27, E28, E29, and E30).
   • This change prevents the irrational monitoring effect where an incorrect (green) value might inflate the count.

3. Unlimited Guesses:

   • Each guess is stored as a separate record with a unique Attempt_Number, so the system can handle unlimited guesses without overwriting previous attempts. This allows for a complete analysis of the students’ progression toward the correct solution.

As you said, 

"We are waiting for an Excel-demo with real or realistic data AND first of all with at least a meaningful pivot-output, where we have rules for relevant interpretations concerning this output." 

I just tried to figure out how we could store all this data, allowing for unlimited potential guesses per student, in the DEMO.xlsx file using a Pivot Table to create a simple output. I believe all necessary explanations are included in the attached file.

Concept B is the most directly related to the displayed statistics of the e-car because it has the highest R², the highest Adjusted R², and a significant p-value for the (power) variable. This makes it the best model among the three concepts.

I done by using Regression in order to goes a step further by modeling the relationship and predicting outcomes.

Based on the regression analysis performed on the displayed statistics of the e-car, Concept B demonstrates the most direct relationship with the energy consumption data. This conclusion is supported by the following key findings:

1. Highest R² Value: Concept B has the highest R² value (0.2262), indicating that it explains the most variance in energy consumption compared to Concepts A and C.

2. Significant Predictor: The power variable in Concept B has a statistically significant p-value (0.0365), meaning it has a meaningful impact on energy consumption.

3. Highest Adjusted R²: Concept B also has the highest Adjusted R² (0.1021), which accounts for the number of predictors and confirms a better model fit.

4. Overall Model Significance: Concept B's regression model has the lowest Significance F value (0.0579), which is close to the 0.05 threshold, suggesting it is the most significant model overall.

In contrast, Concept A shows moderate explanatory power but lacks significant predictors, while Concept C has a very low R² and no significant variables, making it the least related to the displayed statistics.

Key Observations:

1. Concept B has significantly lower values compared to Concepts A and C.

• This suggests that Concept B might represent efficiency (e.g., energy consumption per unit distance).

Based on the data:

• Concept A: Likely represents total energy consumption, as it increases with higher power and longer time.
• Concept B: Likely represents efficiency, as it reflects energy consumption per unit distance.
• Concept C: Likely represents peak energy usage, as it spikes during high power or high-speed conditions.

Group Data by Speed Ranges:

• Created speed ranges (e.g., low, medium, high) and calculate the average values of Concepts A, B, and C for each range.
• Example:
  • Low Speed: 0–20 km/h
  • Medium Speed: 20–40 km/h
  • High Speed: 40+ km/

 Final Answer

Based on the analysis:

• Concept B (Efficiency) is the most directly related to the displayed statistics of an e-car. This is because it reflects energy consumption per unit distance, which is a key metric for electric vehicles.
• The hidden formula for Concept B is likely related to Energy / Distance.

Here, I've worked in this project by observing other student worked on correlations, and then I tried to derive the hidden calculation formulas that you asked other students about. To do that I used some reproducible methods including correlation analysis (as I mentioned before), decision-tree-style logic, and regression-inspired techniques, ensuring clarity and reproducibility as per Knuth-based principles.

Which concept can be derived based on the displayed information units of an e-car? (concept testing and/or specialties of the cryptography) --> 

Regression: Reveals how raw data cells (e.g., speed in specific intervals) impact outcomes, enabling smarter engineering and strategy. 

Cryptographic Hashing: Ensures data integrity for reliable analysis.

Please, try to detect/explore new anomalies in the Moodle system! 

Demo: https://miau.my-x.hu/miau/320/moodle_testing/Moodle_testing.docx

Moodle anomaly: Moodle e-Portfolio Navigation Issue

Direction Error