Interpreting a graph of lab data.

In summary, the conversation is about a graph of lab data showing thermal resistance versus pressure. The y values do not show a discernible trend with increasing x values, leading to the question of whether the y values are independent or dependent on x. It is suggested to repost the graph in a more accessible format and to consider factors such as measurement noise and other variables that may be influencing the data. Overall, the data appears to be too noisy to draw any significant conclusions.
  • #1
SherlockOhms
310
0
So, I've obtained a graph of lab data an it's a plot of thermal resistance versus pressure. Pressure on the x and thermal resistance on the y. If I plot all the points, there doesn't seem to be any trend. The y values are just as likely to decrease as they are to increase with increasing x values. So, even though the y values do change with increasing x, you couldn't say that there's a trend. So, for analysing this, would I say that the y values are independent of x or would I just say that the y values are dependent on x, just with no discernible trend? I've attached the excel sheet, see graph 1. Not sure if that more suited to the homework section or here because it's not so much a homework question, as a general mathematics one to do with interpreting graphs.
 

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  • Copy of Thermodynamics Analysis.xlsx
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  • #2
I would suggest that you repost that chart as a PDF or at least as an Excel 97-03 format. More people will be able to see it. Also there are many who are, rightfully, reluctant to open and unknown Office files, such as yours.
 
  • #3
Since the graph has a ".xlsx" extension rather than a ".xlsm" extension, it should not have any macros, so I am reasonably confident the file is safe and I have gone ahead and opened the file. The OP is asking about the Graph 1 tab.

If you're asking about wording, I would say there is "no statistically significant dependence (or trend)" in contact resistance vs. pressure.

If you want to go into more detail, you could say that for the blue curve only 0.1% of the variance in the values of contact resistance can be attributed to the pressure, while 99.9% of the variance is due to random error or other causes. Recall that variance is the square of the standard deviation in a set of values. The 0.1% figure comes from the r2 value (0.001) given in the data fit.

By the way, it looks like you have ignored the red data points around 99 kPa, 0.6 ohms, causing the r2 value to be an artificially high 0.23. If you include those data points, you should get an even lower value for r2 for the red data set.
 
  • #4
is there a specific question you need to answer in respect of this data, or is it just a commentary you're after? Questions I'd be asking / thinking about would include the following;

1. Are you actively controlling the pressure variable during data gathering, or are these just measurements of opportunity?
2. If they are measurements of opportunity, maybe there is some other variable that is changing during your data gathering, and that this other variable is actually more important than pressure?
3. Why is the 'red' data much noisier than the 'blue' data?
4. Have you attempted to quantify your measurement noise -by controlling/fixing the pressure variable and making repeated resistance measurements for example? Maybe this isn't possible?

As the previous poster suggests, I suspect your data is completely dominated by measurement noise to the extent that drawing any conclusions about trends is pretty much impossible
 
  • #5


Based on the information provided, it seems that there is no clear relationship between thermal resistance and pressure in the data collected. This could be due to a variety of factors, such as experimental error, a small sample size, or the nature of the materials being tested. It is important to carefully consider all possible explanations for the lack of a trend before concluding that the y values are independent of x. Further analysis and experimentation may be necessary to determine the true relationship between these variables. It may also be helpful to consult with other scientists or experts in the field to gain additional insights and perspectives on the data. Overall, it is important to approach the interpretation of graphs with a critical and open-minded mindset, and to always consider the limitations and uncertainties of the data.
 

Related to Interpreting a graph of lab data.

1. What is the purpose of interpreting a graph of lab data?

The purpose of interpreting a graph of lab data is to visually represent the results of an experiment or study in a clear and concise manner. This allows for easier analysis and understanding of the data.

2. What are the key components of a graph of lab data?

The key components of a graph of lab data include the title, axes labels, data points, and any relevant units of measurement. Additionally, a legend or key may be included to explain the different data sets represented on the graph.

3. How do you determine the trend or pattern in a graph of lab data?

To determine the trend or pattern in a graph of lab data, you should look at the overall shape of the graph and the direction in which the data points are moving. If the data points are increasing or decreasing in a consistent manner, there is likely a trend or pattern present.

4. How do you interpret the data points on a graph of lab data?

The data points on a graph of lab data represent the values collected during the experiment or study. These values can be compared to each other and analyzed to draw conclusions about the relationship between the variables being studied.

5. What are some common mistakes to avoid when interpreting a graph of lab data?

Some common mistakes to avoid when interpreting a graph of lab data include misreading or misinterpreting the axes labels, not paying attention to the scale of the graph, and making assumptions without analyzing the data thoroughly. It is important to carefully examine all aspects of the graph and consider any potential sources of error in the data.

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