Electronics > Metrology

USA Cal Club Round 3

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RandallMcRee:

--- Quote from: jjoonathan on July 08, 2023, 04:17:33 pm ---The kit has arrived in North Carolina and I'm logging away!  ;D

One of the banana jacks had a strain relief fatality. The connection is redundant so it isn't an immediate problem, but since I am not very far down the volt nut rabbit hole I thought I'd check in before trying to fix it. Can I just strip back the wire, trim its partner to match, and re-seat the terminals? If I need to apply special anti-corrosion schmoo or perform ppm-preserving rituals :scared: I want to make sure to do it right.

--- End quote ---

Fatigue on the banana jacks? Not surprising!
Any voodoo is in the *lack* of voodoo. That is to say, as far as I know, we just rely on the cable being pure copper and the jack being gold-plated along with some good mechanical connection. So, yes, go ahead and fix as you proposed. You do not need to apply any de-oxit/cramolin so long as you do not smear the cable with your hair grease! :)
(That was a joke)

Thanks,
Randall

SilverSolder:

I've gotten to the point of looking at the data from the experiment.

This being 2023, I asked ChatGPT for some ideas on how to analyze the numbers - here's what GPT-4 suggests:

Analyzing your data can be done in several ways. For multi-dimensional data, you can use a pairplot or scatterplot matrix, which will give you a two-dimensional scatterplot for each pair of your four variables (voltmeters). This way, you can visualize any pairwise relationships in your data.

Here are some techniques you could use:

1. Pairplot or Scatterplot Matrix: This is a great way to visualize multidimensional data. Each scatterplot in the matrix represents the relationship between two of your variables, and the diagonal of the matrix often contains histograms or density plots of each variable. In Python, you can easily create this kind of plot using the seaborn library's pairplot function.

2. Correlation Matrix: A correlation matrix would show how correlated each pair of variables is. This would be a way to quantify the relationship that you see in the scatterplot matrix. Correlation values range from -1 (perfect negative correlation) to 1 (perfect positive correlation), with 0 indicating no correlation.

3. Time-Series Plots: You can create separate time-series plots for each of the DMMs. Plotting the readings over time would help identify any drift or other time-related patterns in the readings.

4. Box Plots: These can provide a summary of the central tendency, dispersion and skewness in your data, and can also help to identify outliers.

5. Histograms and Density Plots: These could be useful to visualize the distribution of the readings from each DMM.

6. Statistical Tests: Depending on your goals, there are a variety of statistical tests you could use to compare the measurements from the different DMMs. For example, a paired t-test or a one-way ANOVA could be used to test if there is a statistically significant difference in the means of the measurements from the different DMMs.

7. Regression Analysis: If you logged the temperature and humidity, you could also perform a multivariate regression analysis to see how these factors relate to the measurements from the DMMs.

8. Allan Deviation: As discussed earlier, Allan deviation could be used to assess the stability of the DMMs over time.

Remember that visualizations can often help to reveal patterns or relationships in your data that aren't immediately obvious from the raw numbers, so it's a good idea to try several different kinds of plots and analyses. Once you have a sense of what's going on in your data, you can then use more focused statistical tests or models to quantify those patterns or test specific hypotheses.

So the question is -  how good of an engineer is ChatGPT?  Is there a better way to do this?

rhb:

--- Quote ---I've gotten to the point of looking at the data from the experiment.

This being 2023, I asked ChatGPT for some ideas on how to analyze the numbers - here's what GPT-4 suggests:

Analyzing your data can be done in several ways. For multi-dimensional data, you can use a pairplot or scatterplot matrix, which will give you a two-dimensional scatterplot for each pair of your four variables (voltmeters). This way, you can visualize any pairwise relationships in your data.

Here are some techniques you could use:

1. Pairplot or Scatterplot Matrix: This is a great way to visualize multidimensional data. Each scatterplot in the matrix represents the relationship between two of your variables, and the diagonal of the matrix often contains histograms or density plots of each variable. In Python, you can easily create this kind of plot using the seaborn library's pairplot function.

2. Correlation Matrix: A correlation matrix would show how correlated each pair of variables is. This would be a way to quantify the relationship that you see in the scatterplot matrix. Correlation values range from -1 (perfect negative correlation) to 1 (perfect positive correlation), with 0 indicating no correlation.

3. Time-Series Plots: You can create separate time-series plots for each of the DMMs. Plotting the readings over time would help identify any drift or other time-related patterns in the readings.

4. Box Plots: These can provide a summary of the central tendency, dispersion and skewness in your data, and can also help to identify outliers.

5. Histograms and Density Plots: These could be useful to visualize the distribution of the readings from each DMM.

6. Statistical Tests: Depending on your goals, there are a variety of statistical tests you could use to compare the measurements from the different DMMs. For example, a paired t-test or a one-way ANOVA could be used to test if there is a statistically significant difference in the means of the measurements from the different DMMs.

7. Regression Analysis: If you logged the temperature and humidity, you could also perform a multivariate regression analysis to see how these factors relate to the measurements from the DMMs.

8. Allan Deviation: As discussed earlier, Allan deviation could be used to assess the stability of the DMMs over time.

Remember that visualizations can often help to reveal patterns or relationships in your data that aren't immediately obvious from the raw numbers, so it's a good idea to try several different kinds of plots and analyses. Once you have a sense of what's going on in your data, you can then use more focused statistical tests or models to quantify those patterns or test specific hypotheses.

So the question is -  how good of an engineer is ChatGPT?  Is there a better way to do this?

--- End quote ---

All of those are sound basic tests.  I did this in the context of running an "orphan home for lost problems" at  large oil companies including  super-majors.  Those  are all basic, *essential* things to be done *before* you proceed with more detailed and focused methods.  In my case, those were design and write the code, apply it to the data and then proceed to the next such step in the process.  The suggestions are all part of  the "exploratory data analysis phase" that should be applied to *all* data as the first step.

ChatGPT makes up data, so it's not reliable as a lawyer recently discovered.  He asked for some additional citations which he included in his brief.  He got a stern rebuke when the judge discovered the citations were fabricated by chatGPT.

Gnuplot is the best tool for doing this.  I don't think it does Alan deviation or ANOVA,  but the Marquard-Levinburg implementation is the best L2 solver I've used.

Have Fun!
Reg