A thought came to me today, via the subconscious mind.
Imagine for example, a camera from Polaroid (this is just an example). Someone buys this from the grey market, manufactured in fact by Polaroid but without warranty at a reduced price. Another person buys this from a regular store, with bill and warranty. Does the price difference at which these two people have bought the cameras indicate anything? I think it does: the quality of the product, from the horse’s mouth.
Nonsense? Let me explain. Cost of manufacturing is decided by the total expense in manufacturing a product, divided by the total number of units manufactured. The cost of selling and after sales support is added to it, calculated similarly – finally the desired profit is added to arrive at the selling price.
My assumption here is that when purchasing something without a bill, the company only charges manufacturing price, with a marginal markup for profit. However, when purchasing in an authorised showroom, the full price, as I explained above is charged. Hence, the difference of those two prices is the cost of after sales support. Another assumption is that both of these are equally affected by the economical aspects of demand and supply. The higher this cost is, it means a higher percentage of units require servicing or fixing. Which translates to lower quality. Another assumption is that both of these prices are equally affected by the economical aspects of demand and supply, and therefore if we take the difference, it indicates as discussed.
Price @ showroom (with warranty included)
Price @ grey market
To me this indicates Brand A is more ‘stable’. Would be interested in doing this as an experiment: someone could take it up as an MBA project. Please let me know if you are interested.
How to compare scores rated under different tests, or by different people? Find out, and use the ready spreadsheet to crunch your own numbers.
Assume that we have feedback on team members from two different project managers.
Can we say that W performs better in team A compared to team B? It looks like yes, he works better but analyse the scores a bit deeper. Project manager B has rated all team members lower than manager A. It may be that he is using tighter scoring.
In a different situation, it may be that two teams (of different people) have gone through two different tests, and we want to compare the people against others. Or, a university might want to compare people passed out in 2001 with those passed out in 2009.
The question is, how do we compare people when the scores that we have do not use the same basis.
The answer is: normalization. We fix the mean score, and the degree of deviation that we would like to see. For a 100 marks test, we may want 50 as the score and a 20% deviation. Now, we will compare this with the actual mean and the deviation of each of the sets, and modify the scores as needed. Please refer to the attached spreadsheet which helps you do this.
In the given example, A has a mean of 73, and a deviation of 13. B has a mean of 67 and a deviation of 11. Let us bring both to a mean of 70 and a deviation of 15.
So we note that A is indeed better in project A, but only slightly. Also from the initial figures we might have concluded that U is better in Project A, but actually the reverse is true.
Mathematically this process is called Normalization and is useful in fitting scores to a bell curve. Read more about it here if you are interested. However the spreadsheet attached is sufficient to get you started.
The current economical state, at least in India seems to me to be like a forest fire. A forest fire burns out all the dying vegetation that is blocking the sunlight and makes conditions suitable for new growth. It resets everything back to where it was. It’s a new beginning.
In times of growth and good earning, we settle down comfortably and over a period, our intrinsic cost increases. When I say intrinsic cost it also includes costs that are incurred by third parties, directly or indirectly for us. For example, if my office sponsors transport facility for me without charging me for it, it’s included in the intrinsic cost.
At times like this, each cost is re-thought about. “Do we really need it?” When the office cuts transport people think “Can I work closer to where I live?” or “Can I shift my residence closer to office?”. In good times there was never need to ask these questions, but now there is. Overall the cost goes down. People are doing something they should have done anyways, but did not just for comfort.
There is mass upheaval, but there is also rationalisation. If too many people have joined the IT industry (more than what is intrinsically needed), some will join the biotech sector for example, if that is an emerging sector. Again, it something that should have happened anyway.
So the point is, upheaval and rationlisation are good. While these move us out of our comfort zones at the moment, they are better for the long term.
Technical analysis is a collection of methods to predict future stock prices taking into account only the past and present data – prices and (optionally) volume. There is no consideration given to the fundamentals of the company or the industry under consideration.
I personally use technical analysis(TA) only after I have zeroed in onto a company that I want to invest in. That is, I use it to determine when to enter (buy) and when to exit (sell). Over and above that – I do not execute blindly the output from TA. I look at the market sentiment, company and industry timing etc. Even so, technical analysis does help me big time with the decision making.
How is this spreadsheet different from others available online?
This sheet doesn’t require you to interpret the graph. While the graph is still created, which you can check, or create additional graphs based on the data, its not really required – you can read the interpretations directly. The sheet gives you buy/sell advice directly based on the indicator you select.
The sheet does not require you to buy MS Excel if you do not have it. It works fine with Openoffice Portable. I have already talked about portable apps – this one is free as well.
As the project manager of a software project, I need to provide invoicing information every month end. I used to find this task rather boring, especially since I had to count hours and provide numerical data in multiple formats: one for the invoicing system in use by the company, another for the finance team and yet another as per client specifications (since he wanted it on a week basis, rather than on month basis as used by the company wide system).
So the same figures, in different formats – can be done, but was boring each month.
All you have to do is to setup the sheet once, with your employee details. After that, select the holiday periods using colors, and press Update. All the calculations will be done using the colors that you have given to the cells. Its easy to understand and verify: all one has to do is the check the colors – since the rest is automatic there cannot be a mistake. This excel sheet can easily be made to give out the “figures” in any format. Try it out, and post comments. Invoicing is no longer a boring job: in fact this colours approach has lot many more uses.
Note on licensing: this spreadsheet uses third party code, which I have permission to distribute – please do not distribute this spreadsheet or derivatives. The license applicable elsewhere on this blog is not applicable to this spreadsheet.
How to detect problems pertaining to randomness of numbers using Benford’s law. The theory can be used to detect fraud, evasion of rules etc.
Suppose you have some financial data – let us say all the vouchers paid by the company in a given month, and you want to run some tests to determine if there are any anomalies. For example, are the employees beating the approval process by entering say $24.99 vouchers if the limit is $25, or if any fraud is being committed. One way to do this is to use Benford’s law.
Benford’s law states that in a given list of numbers generated naturally (for example stock prices or census figures), the probability of a number starting with 1 is 30.1%. The probability of a number starting with 2 is 17.6% and so on – it keeps decreasing as the numbers increase. The rationale behind it is explained as: it takes a 100% increase to take a number from 100 to 200. However, it takes only a 50% change to go from 200 to 300. 100% increase is more difficult to do (and thus has less probability of happening) than a 50% increase.
In this way, the probability of having a number starting with digit d is given by log(1+1/d), log to base 10. More information is available here. Its usually extended to the first two digits for analysis in the real world.
Download from here a spreadsheet (called Numeric Truth) to carry out this analysis for you. All you have to do is to paste your data into the green cells. After that, on the first sheet it will show the results of first digit analysis, and on the second sheet, two digit analysis. Have a look at the graph, the variances for the individual digits, and the total variance. That should give you a starting point for your analysis/audit.
Licensing and information about the blog available here.