Originally posted at http://www.howtomeasureanything.com, on Sunday, September 06, 2009 9:20:07 PM, by sujoymitra17.
“While using Lens Model (multiple regression), I am getting negative scores for a few parameters and positive scores for few others. I am computing the score using the formula:
- <Coeff of parameter-1>*Val of parameter-1+<Coeff of parameter-2>*Val of parameter-2….+Intercept.
Since few parameters are showing -ve scores and others +ve (considering few have -ve correlation-coeff and others have +ve correlation-coeff), how do I formulate weights?”
I’m a little confused by your message. The coefficients in a regression model ARE the “weights”. The output of a regression analysis includes the coefficients. A regression analysis is how the weights are computed for a Lens model (the former is a tool for the later, they are not the same thing).
Are you actually performing a least-squares best-fit linear regression analysis? Are you using the regression tool in Excel? Just making a formula with parameters and coefficients is not a linear regression.
Getting negative coefficients is not necessarily a problem, since that actually makes sense for many situations (examples of negative coefficients include criminal convictions and income, body fat and life expectancy, driving speed and mileage, etc.) If you are doing an actual regression, then getting negative values is not a problem. It can even be an expected outcome.
Perhaps you can describe what you are attempting to do in more detail.
Doug HubbardRead More
Welcome to the Errata thread in the book discussion of the How to Measure Anything Forum. An author goes through a lot of check with the publisher but some errors manage to get through. Some my fault, some caused by the typesetter or publisher not making previous changes. I just got my author’s copies 2 days ago (July 20th) about 2 weeks before it gets to the stores. But I already found a couple of errors. None should be confusing to the reader, but they were exasperating to me. Here is the list so far.
1) Dedication: My oldest son’s name is Evan, not Even. My children are mentioned in the dedication and this one caused by wife to gasp when she saw it. I don’t know how this one slipped through any of the proofing by me but this is a big change priority for the next print run.
2) Preface, page XII: The sentence “Statistics and quantitative methods courses were still fresh in my mind and I in some cases when someone called something “immeasurable”; I would remember a specific example where it was actually measured.” The first “I” is unnecessary.
3) Acknowledgements, page XV: Freeman Dyson’s name is spelled wrong. Yes, this is the famous physicist. Fortunately, his name is at least spelled correctly in chapter 13, where I briefly refer to my interview with him. Unfortunately, the incorrect spelling also seems to have made it to the index.
4) Chapter 2, page 13: Emily Rosa’s experiment had a total of 28 therapists in her sample, not 21.
5) Chapter 3, Page 28. In the Rule of Five example the samples are 30, 60, 45, 80, and 60 minutes so the range should be 30 to 80- not 35 to 80.
6) Chapter 7: Page 91, Exhibit 7.3: In my writing, I had a habit of typing “*“ for multiplication since that is how it is used in Excel and most other spreadsheets. My instructions to my editor were to replace the asterisks with proper multiplication signs. They changed most of them throughout the book but the bottom of page 91 has several asterisks that were never changed to multiplication signs. Also, in step 4 there are asterisks next to the multiplication signs. This hasn’t seemed to confuse anyone I asked. People still correctly think the values are just being multiplied but might think the asterisk refers to a footnote (which it does not).
7) Chapter 10: Page 177-178: There is a error in the lower bound of a 90% confidence interval at the bottom of page 177 . I say that the range is “79% to 85%”. Actually, the 79% is the median of the range and the proper lower bound is 73%. On the next page I show an error in the column headings of Exhibit 10.4. I say that the second column is computed by subtracting one normdist() function in Excel from another. Actually, the order should be reversed so that the first term is subtracted from the first. As it is now, the formula would give a negative answer. Taking the negative of that number gives the correct value. I don’t think this should confuse most readers unless they try to recreate the detailed table (which I don’t expect most to do). Fortunately, the downloadable example spreadsheet referred to in this part of the book corrects that error. The correction is in the spreadsheet named Bayesian Inversion, Chapter 10 available in the downloads.
8) Chapter 12: page 205; There should be a period at the end of the first paragraph.Read More
Originally posted to http://www.howtomeasureanything.com/forums/ on Monday, July 13, 2009 2:13:07 PM.
“I would love to see an example following upon the idea of estimating the population of all prospective clients which uses similar sampling method as recatching example. Could you do it for me?
We might need more details to work out the specific mechanics of this one, but we can discuss the concept. First, it is worth pointing out that the recatch example is just a way of using two independent sampling methods and comparing the overlap. In the case of the fish in the lake, the sampling methods were sequential (one was done after the other was done) and the overlap of the samples was determined by the tags that were left with the first sample of fish. Then when the second sample of fish was gathered, the proportion of that sample with tags would show how many fish were caught in both samples. From this and knowledge of each sample size, the entire population could be estimated.
But we don’t have to think of this as being sequential sampling where the first sampling leaves a mark of some kind (e.g. the tags on the fish) so that we see the overlap in the second sample. We can also run samples at the same time as long as we can identify individuals. People are simple enough to identify (since they have names, unique email addresses, etc.) so we don’t have to “tag” them between samples. (This is convenient, since I find that people rarely sit still while I try to apply the tag gun to their ear lobe.)
So if we had two independent sources attempt to identify prospects out of a population pool we could estimate the size of the prospect population. If two independent teams were using two different methods (perhaps two different phone surveyors or two different teams surveying people in malls), and if identification is captured, then the two teams could compare notes after the survey and determine how many individuals came up in both surveys.
The trick would be to find sampling methods that were truly independent of each other and the target population. If the population was “prospects in the city of Houston” and the sampling methods were mall surveys, then we should consider the possibility that not all prospects are equally likely to visit malls. If both survey methods were biased in the same way (tending to sample the same small subset of the target population), then the “recatch” method would underestimate the population size. If we used two completely different sampling methods (one mall survey and one phone survey) and the two methods were biased in a way that made prospects in one method less likely to be found by the other method, then the method will overestimate the total population.
As you can see, there are many variations on this method and each has challenges. The error could be high but, as I point out in the book, if it told you more than you knew before, then it can be a useful measurement.
Doug HubbardRead More
Originally posted on http://www.howtomeasureanything.com/forums/ on Monday, June 22, 2009 4:33:03 AM.
ON the page 187 there is a claim that the author has generated an example of random power law generator, but i cant find it from the examples. Can someone help me with this problem who has find the example from downloads?
Thanks for your question. I had it up briefly and discovered a flaw in the automatic histogram generation. I’m out of the country I’ll have the power law generator up by the end of June (much sooner if I find the time before I head back to the US).
Thanks for your patienceRead More
This question was originally posted on Friday, June 12, 2009 10:47:57 AM by mzaret20000 on http://www.howtomeasureanything.com/forums/.
I have been asked to provide the gross margin for client engagements. My company is a recruiting firm that operates like an internal recruiting team (meaning that we charge fixed rates as we are doing the work regardless of outcome rather than billing a percentage of compensation hired). Most of our engagements are small so they aren’t not profitable in the absence of other engagements. I’m wondering what your strategy would be to make this type of measurement.”
Thanks for your post. At first glance, your problem seems like it could just be a matter of accounting procedures (e.g. revenue minus expenses and divided by revenue with consideration for issues like how you allocate marketing costs across projects, etc.) but let me presume you might mean something that is more complex. It might not be what I strictly call a “measurement” since it sounds like you probably have the accounting data you need already and you probably do not actually need to make additional observations to calculate it. This is more of a calculation based on given data and the issue is more about what it is you really want to compute.
Since you mentioned that your projects are small and not profitable n the absence of other engagements, perhaps what you really want to compute is some kind of break-even point based on fixed costs and marginal costs of doing business. But even that’s a guess on my part.
Perhaps you could describe why you need to know this. This is a typical question I ask. What decision could be different given this information? What actions of what parties would this information guide? Once you define that I find that the measurement problem is generally much clearer.Read More