Wikipedia:Reference desk/Archives/Mathematics/2013 February 25

= February 25 =

Linear Regression with Known Uncertainty in Y-Variable
Hi Everyone! I am a TA for an introductory Biology class. In a recent lab, the students had to perform an experiment involving osmosis, the rate of which was measured using scale (change in mass over time). However, for the negative control several students got a minute rate of diffusion (increase mass over time), this change in mass was incredibly small (less than the error associated with the scale). My goal is to explain that a change of 0.2g over a period of time when the known error in the scale is ±0.2g is insignificant. In effect H0=slope of 0 p>0.05. However, when I do a linear regression and correlation test of the students data the data is shown to be significant (reject H0). I feel if uncertainty/error were accounted for, this would not be possible. How can one demonstrate this? I'm currently using SAS for the analysis. The variables are Time and Mass I've given an example data set below. I'm willing to accept the error in the time value is negligable (± 1s) since the measurements are in minutes. Below is the student data. I'm using proc reg in SAS. Last note, I'm trying to prove this statistically to provide concrete explanations for my students instead of "feel good" or "just so" responses, especially since a good few of them were AP students and should understand the math. Data

Time (min)    Mass (g) 0             10.5 1              10.5 2              10.6 3              10.6 4              10.6 5              10.7

proc reg data=data; model t=m; run;

Output from SAS: Analysis of Variance: Source         DF     Sum of Squares     Mean Square     F Value     Pr>F Model           1       0.02414             0.02414        23.05     0.0086 Error           4       0.00419             0.00105 Corrected Total 5       0.02833

Root MSE         0.03237     R-Squared  0.8521 Dependent Mean  10.58333      Adj R-Sq  0.8151 Coeff Var        0.30583

Parameter Estimates: Variable    DF    Parameter Est. Stan. Err. t value  Pr>|t| Intercept    1         10.49048       0.02343     447.82     <.0001 T            1         0.03714        0.00774     4.80       0.0086   <---Problem? If all the mass data points are ±0.2 is this really significant? or is it more likely H0 (slope of 0) is true. Currently its reject H0. How to change/fix. I'm also curious for running future analysis on my own data in SAS.

Thanks for the help. 68.2.223.135 (talk) 01:42, 25 February 2013 (UTC)


 * Sorry, I'm having a hard time seeing what goes with what in your SAS output. What is the regression coefficient? What is the t-statistic? What is the p-value for the t-statistic? In any event, I don't think the amount of measurement error in the dependent variable (mass) is relevant to the question of significance -- the measurement error in the dependent variable will affect the R2 and nothing else. Duoduoduo (talk) 02:25, 25 February 2013 (UTC)


 * I strongly recommend that you talk this over with the professor who runs the course. I think you have a serious misunderstanding of the nature of an experimental control, which is unlikely to get cleared up by an answer here on Wikipedia.  It wouldn't be good for you to inflict your misunderstanding on your students. Looie496 (talk) 02:53, 25 February 2013 (UTC)


 * @ Duoduoduo and Looie496 Since this is a question regarding SAS and it appears neither of you have used SAS, your responses haven't been very helpful; however, I will attempt to clarify some of the above post to help out.

@ Duoduoduo The t-statistic is under t-value and the p-value for said t-stastistic is under pr>|t|. The parameter estimates are (parameter est.) are the coefficients in the Y=a+bX equation (or M=a+bT in this case). I agree that the measurement error in the dependent variable will decrease the R2 value; however, this value is also related to the p-value. Think multivariate modeling (I know this is a simple linear regression, but perhaps my mistake is attempting to apply a complex subject tot a simple matter) when using a stepwise or backward regression, the p-value is used to determine which variables to include or exclude (lower p-value higher relevance). In this case, the variable T seems to be relevant to a change in mass; however, with the high known error it is unlikely that these data points are actually different. If included in the model the R2 value would decrease as the correlation of the model decreased and the p-value would increase suggesting the variable T is less significant (to the point of exclusion). Thus, since this is a single variable model, the lack of this variable provides an equation with just an intercept (which is significant since this is the weight of the bag). In the SAS output, the intercept is relevant (p-value <.0001); however, so too is the value of b (parameter estimate of T). This suggests that the data does not have a slope of 0 as the variable T is relevant.

@ Looie496 First, I thought this was supposed to be a friendly discussion forum. Attacking the integrity of someone you don't know without proving any credentials yourself seems rather foolhardy/arrogant. Anyway, to your point - Negative controls are groups where no phenomenon is expected. They ensure that there is no effect when there should be no effect. Hence in an osmosis experiment using DT bags immersed in DI water, our negative control is a DT bag filled with DI water submersed in DI water. One would expect no net gain of weight in the DT bag as this solution should be isotonic (no concentration gradient). The data above suggest that water flows into the bag over time (ie something happened). However, the error on the scale being used is of such a great extent that one could simply rationalize "these data are most likely the same." I would like to know how to include the measurement error into the model in SAS to prove this to be true. The P-value of this variable within the model should be such (p>0.05) that we reject the significance of the variable (even if the R2 value shows a good fit).

Hope that clears some of the confusion up. I would appreciate a response from someone who has worked with SAS and can answer the question I posted. Thanks! 68.2.223.135 (talk) 21:25, 25 February 2013 (UTC)


 * I don't know SAS, so I can't help you there, but this document discusses your use case (starting on page 8). Specifically, equation 13 tells us that if the y-errors are assumed to be +/- 0.2, then the slope uncertainty on your numbers evaluate as +/- 0.048.  Given your parameter estimate of 0.037, such an error would indeed tell you that the slope is consistent with zero.  Dragons flight (talk) 21:53, 25 February 2013 (UTC)


 * I didn't attack your integrity, I questioned your understanding. It is worrisome that you don't recognize the difference.  It is absolutely false that negative controls should show no effect.  Their purpose is to show effects that are caused by the measurement process rather than by the thing that is being measured.  I repeat that you should talk this over with the course director. Looie496 (talk) 02:13, 26 February 2013 (UTC)


 * Honestly Looie, I read your prior comment as unhelpful criticism as well. The original poster correctly recognized that a canned statistical test wasn't considering measurement uncertainty, and he realized that failing to consider such uncertainties had the potential to lead to misleading conclusions.  He asked for help in showing if/how that might be the case.  That's the essence of the question.  He also provided a bit of background that the data in question happened to come from a test run as a negative control.  You somehow read that bit of background as indicating some profound ignorance on his behalf, and focused entirely on that secondary aspect in framing a response that didn't even touch on the main thrust of his question.  Worse, your first response said essentially "you're wrong" and didn't even bother to explain why, which made it very unhelpful to the poster.  Perhaps there are some things that this TA is confused about (and accounting for measurement uncertainty is certainly one of them), but when I read the question, I'm prepared to give him the benefit of the doubt that he isn't making profound errors in thinking about experiment design.  Dragons flight (talk) 04:08, 26 February 2013 (UTC)


 * The Wikipedia Ref desks really aren't suited to this sort of issue -- I'll just say that if I was running that course, I would very much like this TA to come talk to me about this problem. Looie496 (talk) 06:04, 26 February 2013 (UTC)

@ 68.2.223.135: You presented your SAS output in unreadable form, not lined up conveniently. I said it's hard to read. Then CiaPan changed it to readable form, and then you condescended to explain the readable form to me. Did you even read it when you typed it and notice that for example t-statistic was not under "t-value"?

As for your response to my not very helpful comments, may I point out that you are a TA in an intro course, whereas I taught doctoral students for 25 years, including supervising numerous empirical dissertations. I hope you'll show more respect to your professor that you're TAing for, and to whoever you write a thesis or dissertation under. Take my word for it or look it up in a stat book: noise in the dependent variable does not bias the t-statistic. If your errors in dependent variable measurement are correlated with the independent variable "time", then there would be bias. Now of course your constant term is extremely significant; that's usually the case in regression, and it doesn't have any particular meaning. As for your slope coefficient (coefficient of time), if it's significant then it's significant. Just looking at your data makes it not surprising that the slope is significant: as time goes up, your mass never goes down and sometimes goes up. Maybe there's some systematic mistake in conducting the experiment that leads to data errors that are correlated with time. Or (and this can happen) maybe you've entered the data in reverse order (maybe by conflating "time passed" with "time remaining"). I would also note that the document linked to above by Dragons Flight, on p. 8 that he cites, talks about what happens if absolute measurement error varies by data point, perhaps because relative error is constant. But from what you say the absolute error seems to be constant. If that's the case, then there's nothing you can do about it. The slope estimate and its p-value are as statistically good as you're going to get. Finally, I hope your actual regressions have a lot more data points than what you showed in your example -- even though the significance formula takes into account how few data points you have, I would never trust something with less than, say, 20 or 30 degrees of freedom. Duoduoduo (talk) 15:38, 26 February 2013 (UTC)

@All:

My intention was not to have this post become a "bull session". That said, my academic pursuits are not in statistics. For clarification on my background and why attempting to explain misunderstanding to students in concrete terms is important to me, I am pursuing a doctorate in education with an emphasis on science education and policy. My degree is not in statistics (though I find it to be highly relevant to my field, hence my attempts to learn). I am certain my minimal understanding of statistics is of such a limited extent that others who would post on here can destroy anything I would attempt to say or do. However, the point was to determine if it was possible, in SAS, to incorporate absolute measurement error into the model to show the original conclusion drawn from the data was invalid. My desire to use SAS for this function is that SAS is a program I am attempting to learn. The data referenced in this post is an example of student work whereby the student concluded (from a linear regression in MS Excel) that the data proved flow of water into a DT bag under the condition of zero concentration gradient (ie the negative control for the experiment). Using this, the student also concluded the negative control was a failure and then went on to analyze the rest of their results without discussing the possibility that their data for the negative control didn't support their conclusion when absolute uncertainty was considered (or the fact they have very limited data to begin with, but this is a FRESHMAN level undergraduate bio class). The reason I use the negative control data from one student is because I thought everyone who read the post would be able to get on board with what I presented, that the slope of that line should be zero and given the data (with known absolute error) one could draw that conclusion (ideally with concrete stats analysis).

@Doudoudou: As for the un-helpfulness of your post, your comment on your credentials versus mine also seems irrelevant to the topic. If your comment wasn't helpful, then it wasn't helpful. Citing your authority as having taught doctoral students doesn't change that. With that in mind I do respect that you are attempting to be helpful and appreciate your expertise. In that light, my apologies for my post whereby I misinterpreted your source of misunderstanding. I concede it is likely the output was unreadable in the format I provided and thank CiaPan for correcting it. My comment to you was not intended to be condescending (though your above comment to me appears to be of that nature) I was attempting to explain the output to you (per my understanding of your post) and to anyone else that may come along with an attempt to answer. I agree, 4 degrees of freedom (n-2) for this experiment is suspect, so too is the fact that it is run by undergraduate freshmen who likely have never used a non-digital scale. Another consideration is that the test run is constrained by lab time (they get 2.5 hrs to do a quiz, listen to my lecture, perform the pre-lab from their lab books, design the experiment and run it) I'm absolutely certain there are plenty of non-quantifiable errors I could cite as the source of their data/conclusions being suspect. The goal is to try to demonstrate that even if their data is correct, the conclusion from this data is not. Also, while doing this I want to provide more concrete reasons for their conclusion being incorrect than "just so" stories. Perhaps the way I'm thinking about attempting to explain the problem is the reason for the confusion (and perhaps my general weakness in statistics). So if you'll indulge me this analogy: Say one were measuring a response (y-variable) over several groups who received a treatment measured over time (x-variable). Say too the averages of these responses were consistent with the student data I provided above. And that the analysis of variance showed that the 95% confidence interval for each mean just happened to be equivalent to the measurement error in the above experiment. Would you not conclude looking at that data set that even though there is a slight trend, that perhaps those averages really aren't different and no real conclusion could be made from this experiment? That's kinda how I thought of the problem. Does that help? Again, I'm not trying to be condescending in this post only trying to clarify. Hopefully, further conversation in this post can be more productive than previous attempts. Again, I apologize for any sense of condescending within my post toward you.


 * My credentials are irrelevant? Then why did you criticize someone else (Looie496) for not providing his credentials? As for "If your comment wasn't helpful, then it wasn't helpful", my comment was helpful, if you bothered to take it seriously, because it was the correct answer to your question. I'll say it once more (third time), even though it's not the answer you were hoping for. Your question is the analysis of variance showed that the 95% confidence interval for each mean just happened to be equivalent to the measurement error in the above experiment. Would you not conclude looking at that data set that even though there is a slight trend, that perhaps those averages really aren't different and no real conclusion could be made from this experiment? The answer is that there's no basis for that conclusion. Regardless of whether the error variance is tiny or huge, if the p-value for the slope is, say, .0086, then if you ran such regressions from the same data generating process numerous times and if the null of no effect were true, then you would only get a result for the estimated slope so far away from zero 0.86% of the time. Duoduoduo (talk) 20:46, 26 February 2013 (UTC)


 * In that you you cite them for the purpose of belittling me, Yes! They Are. Think about when and how you use them. You cite your credentials as an source of absolute authority (against my being only a TA). Do you not remember this is Wikipedia!? You could be anyone, you choose to remain anonymous; therefore, stating you have credentials is irrelevant to your argument. Justification by appeal to authority (especially when the authority cannot be verified) is an incredibly weak argument. On you wikipedia users page, you state Always ask “How do we know that?” ; demand rigorous evidence would you end your argument when someone else's argument is simply an appeal to an unseen authority? I don't think you would.

Also, considering the question I posed in my hypothetical - if 95% CI overlap, one cannot conclude with certainty that the values are themselves different. I'm referencing Tukey's Studentized Range (HSD) here. As such if the hypothetical I provided were correct, I would conclude from an HSD analysis that the measurements were not significantly different. You are correct above to point out that the p-value for the slope would not change. Utilizing an imaginary data set for the hypothetical I provided, the p-value still showed significance (though it changed slightly upward, probably a mis-entry somewhere on my part); however, the R2 value was reduced to 0.013. I would say between an HSD test showing no significant difference between measurements and an incredibly small R2 value that the data is at worst not linear and at best not different. I do concede to your points from before that the p-value wouldn't change if error in the dependent variable were included in the model; however, your answers have been unhelpful in that they haven't provided explanations. Similar to Looie496, you begin by taking an accusatory stance and end with an air arrogant condescending. I have learned something (about statistics none the less) from attempting to deal with this attitude of yours; however it has been incredibly difficult. If I could, may I make a request of you (and Looie496, should he/she wish to participate)? Riddle me this, what was the purpose of your posts other than to boast about your "status" and condescend toward another human being? 68.2.223.135 (talk) 03:27, 27 February 2013 (UTC)


 * What was the purpose of my posts? I'm an unpaid volunteer here, trying to help people who want help. You claimed that I have no SAS experience, and I pointed out otherwise. You wrote condescendingly to me, and I responded by pointing out that your condescension was unwarranted. I offered my credentials, not because they prove absolute authority, but because you criticized someone for disagreeing with you without offering their credentials. I offered help, and you responded by calling my efforts not very helpful. If you want help, stick to asking for help rather than going on the attack. Other questioners on this reference desk seem to have no problem with doing it that way. Duoduoduo (talk) 15:31, 27 February 2013 (UTC)

Duoduoduo: from "How do I Answer a Question?" at the top of this page... "The best answers directly address what the questioner asked (without tangents), are thorough, are easy to read, and back up facts with wikilinks and links to sources. Please assume good faith, especially with users new to Wikipedia. Don't edit others' comments and do not give any medical or legal advice." [emphasis added] As I have mentioned several times my original intent in my first response to your response was not intended as condescending. I have also accepted and apologized for my part in that misunderstanding. All I have received in return from you is contrary to what ideally should be a response, per Wikipedia. I can tell these past posts for some reason elicit both anger and a need in you to prove your "superiority" over me. OK, fine. You claim to be a professor emeritus of economics who is highly published and believes "the meaning of life is to live a happy life." (from your wiki user page). So here, go be happy! You're smarter than me (in your field), you've published more than me! You're obviously a superior being. But you obviously have a lot to learn about happiness and letting things go. Perhaps a counselor, or a psychiatrist could help you. I doubt you'll find resolution to these problems in Wikipedia. 149.169.201.148 (talk) 19:07, 27 February 2013 (UTC)

@Louie496: If you were running this course (specifically this experiment), and given all the information you now have about the course (from all previous posts) what would you tell the student in question? I'm curious to know if it would be more than "well you can't make those conclusion because [insert non-quantifiable error]." If that's the case you're simply providing "just-so" stories to the students as to why such and such is/is not the case. What would your response be to a student then asking if one was to remove those error variables through a repeated experiment (under much greater control of course) and they got the same data? My problem for the students perspective isn't to rain down on them (as yours is on me) and show that they are just wrong, it is to provide concrete examples of how to deal with errors and what they mean. Students aren't going to be tested on the stat's ideas that I would present nor am I going to drastically dock them points for their naivete - such is the nature of those seeking knowledge. Likewise, I too am naive in the area of statistical analysis and would attempt to learn. Looking at the comments received thus far you are correct, perhaps the Wikipedia:Reference desk/Mathematics was not the best place to go for this question. However, I thought the point of the ref desks were to help those like myself to find answers to questions in the field they have questions in (I'm pretty sure mine being a stats question falls under mathematics?? maybe I'm too naive here as well). Also, I have discussed with both the professor and lab coordinator every problem I have ever come across. I was told to attempt to figure out how to provide such a concrete example on my own since both the lab coordinator and professor would simply look at the data and conclude that considering all the likely chances for error the data is probably not actually showing a trend. To the point about experimental controls. If the measurement variable changes under the negative control when the expected response is zero, this shows that the test will likely produce false positives (at least there is uncertainty in all positives for the test). Let us assume (taking an example from my prior experience) that I were running flow cytometry to determine leukoreduction on blood samples. Can you guess what our negative control was (we're counting leukocytes)? A sample with 0 leukocytes (known). If the flow cytometer happened to pick up several leukocytes (beyond the acceptable error, it occasionally came up with a number in the <100,00/mL range - below error and below clinical significance)then the control failed and we wouldn't use that machine for testing. In the case of drug trials a negative control (placebo) of course doesn't show absolutely no measurable outcomes; however, it establishes a baseline by which to compare the results of the treatment group. Also, placebo groups can be compared across several studies, if say one's placebo group responded drastically different to other placebo groups this is analogous to a failed control.
 * Here is what I would tell them, as far as I can say, not knowing how the measurements were made: (1) There is a slope that is significant with p < 0.01, so it needs to be treated as real.  (2) The range of values over time for the negative control is no larger than the measurement error, so unless the measurements are digital, there is a good chance that the effect is due to human error, i.e., biases in reading the instruments that change over time.  However, there is no way to know that for sure.  There are many other possibilities, and without a specific test, there is no way to know what explanation is correct.  (3) Presumably the range of variation for the positive manipulation is much greater, and the difference between manipulation and control is significant -- that's the important thing. Looie496 (talk) 03:53, 27 February 2013 (UTC)

Looie496: I want to thank you for this response. In my previous posts I very much attacked you and your responses - though to a certain extent they may have deserved it, but definitely not from my level of disrespect. For this I apologize. We appear to be on the same page for explaining concepts to a student body. To be honest my concern was not using this to explain to everyone in the class, rather to a single student who himself is incredibly arrogant (and probably smarter than he shows). The goal was to be able to reach him on a level that was not biology since he regularly tries to demonstrate his intellect using mathematics. After some time I did find a response to the original problem posted and it essentially boiled down to "You can do this with SAS, but it is exceptionally difficult and probably not worth the effort for such a data set. Regardless, the p-value may change but it will still likely be <0.05. However, the R2 value would decrease drastically demonstrating that while the variable is relevant, it is not linearly correlated." So, that appears to be the answer, which is a combination of the understanding I drew from Duoduoduo and Dragonsflight, less the actual method in SAS. I wish you well with your work in Neurology and hope that your attempt to encourage greater participation of scientists on Wikipedia is more successful than our conversation has been (No assignment of absolute blame, I am plenty to blame). 149.169.201.148 (talk) 19:07, 27 February 2013 (UTC)

@Dragonsflight: Thanks for your help, support, and benefit of the doubt. I didn't realize that when providing background for a question (that is obviously incomplete and used to provide just that...background) I would be highly scrutinized for the incompleteness of the background. All that aside, I don't know how to incorporate what you cited into SAS, but as you mentioned you too are not familiar with this program. Anyway, if you have any other insights it's greatly appreciated.

Standard deviation is related to probability, just as expectation is related to...?
The answer I'm looking for, is supposed to answer a question from the opposite direction: Expectation is related to probability, just as standard deviation is related to...? Here is a third alternative: Standard deviation is related to expectation, just as probability is related to...? 77.125.99.245 (talk) 08:54, 25 February 2013 (UTC)


 * I don't think "crossover" relations (such as "Apple is related to vegetable as cabbage is related to fruit") make much sense. I assume that you gave "variance" as the answer to the original question with the relation "is a measure of".    D b f i r s   11:05, 25 February 2013 (UTC)


 * I agree that the "third alternative" does not make sense, but the answer to the original question would more precisely be "the magnitude of deviation from the mean". I hope I'm not giving the answer to a homework question here (but I fear that I am). Looie496 (talk) 16:59, 25 February 2013 (UTC)


 * To me, those questions are very poorly worded for homework, though Looie's interpretation and answer are good. Here's an analogy that does hold up, in case you are interested: the expectation of a random variable following a probability density function is the same as the center of mass of a physical object that has that mass density distribution. In this case, the standard deviation gives the radius of gyration of the object, which is a nice way of giving a physical interpretation of the probability concept. You can do a nice illustration of the radius of gyration using a standard chalk board eraser: you can easily toss it into the air and watch it smoothly rotate around either of two axes. When you attempt the third axis, all you will get is wobbles, because...(left as an exercise for the reader ;) SemanticMantis (talk) 20:14, 25 February 2013 (UTC)