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Showing posts from October, 2008

Single Factor Analysis

What is Single Factor Analysis(SFA)? I really won't go into scientifically defining the terminology because it can get very complicated as the mathematical tools get varied.  So I am going to say in very layman terms, what exactly does SFA mean and how do we carry on the analysis.  Factor Analysis is an analysis where you take two different variables like : 1.  Dependent Variables(DV)    2. Independent Variable(IV) .  Then you vary Independent Variable and check the response on the Dependent Variables. The main objective is to prove or disprove that, the change in IV has effect on DV.  Therefore, usually in SFA we go by having a hypothesis, that there is a relationship and may prove it wrong or right during analysis. Example I The concentration of the CO2 ( [Co2]) has an effect on the temperature of Earth[T]. The above statement is the  hypothesis. It may sound old, as it is the proven fact. But it wasn't back in 1824. And the interesting fact is that the Global Warming was not

Mathematical Reality

Why do we use so much of mathematics in analytics, and is there any way to justify this method? The answer is very straight and simple. Mathematics is taken as one of the most truly logical language. No doubt. In addition, analytics means analysing the facts and logically following the clues in the facts to come to a practical conclusion. Its this process that exploits the power of mathematics, to structure the logic in the most appropriate way. The mathematical processing makes the logic in physical world more understandable and playable. Thus, giving analyst a tremendous power to handle the facts. If there is a time, than I would suggest going through a book called " A Mathematician's Apology " by    G.H. Hardy .  It is the most humblest book I have come across. The book is about a Mathematician who have chosen to devote his whole life on mathematics. There, he argues the difference between the Mathematical Reality and Physical Reality. Let me explain it in my words. In

The Difference

What kind of difference am I talking about? Well, one very important difference between the under developed and the developed country. I came across this difference long time back during my student years, but found it very interesting when it was talked about in one of the debate, in BBC radio. The difference lies in perspective of the science student, in the developing country and the developed  country.  The program in the BBC radio had very interesting point to make. They had two teams of science student- one from UK and the other from some Asian country. Both of them were asked why they chose to study science. Remarkably the views were very very different. And off-course it had lot to do with the economic conditions of the country.  The  students from under developed country told that, they hope to bring the changes in the country, by pursuing science. Some wanted to be engineers, some doctors and some wanted to go to medicine. In one view, it seems very likely that they would thin

Missing Value Approximation

What do we do when the data you have has a missing values ? There are very few options like: 1. Delete the data 2. Make the missing value zero 3. Approximate 4. Ignore and so on Now obviously, options 2 and 4 are not the likely way of dealing with the missing data.  When we make the missing value ZERO, you are unknowingly creating a new data which may be either valid or not. For example, what if the ZERO value is one of the likely possible value of the variable that you are dealing with? In that case we have a wrong set of pattern.  Further, we simply cannot ignore the data. Ignoring is some how similar to deleting. When we delete the data, we are decreasing our sample number, hence decreasing the power of our statistical tools. As we know, the statistics work best when there are large number of data. In addition, there are other difficulties with deleting a data, which we will get to latter on. Therefore, it seems very probable that we approximate the value. But we have to be very car

Entrepreneurship II

lets continue to discuss on this topic of entrepreneurship more. With the world moving so fast in innovations and technology, I ask, can we play any role in it? Many of us do not think that we could have something as big as Microsoft or Sony or IBM . But wait a minute, are we purposely demeaning ourselves. Have we tried hard enough to reject the idea of building any new technology. Are we even encouraging the one, who are trying their best to do so? So lets analyze it. Is money the biggest hurdle in creating a technology ? My answer is - very rarely. From my point of view, technology does not mean building an expensive machine, it simply means coming with an efficient solution. I have not set any boundary for technology. Its an idea or a faithful solution whose sole purpose is to upgrade the standards of the work. No doubt the capital is also necessary. Its the fact that all the innovation have started with a dream to solve a particular problem. It will be cliche for me to repeat tho

Entrepreneurship I

This time I am asking a very difficult and an important question to myself. Being an Electronic Engineer , I have a strong inclination to technology and innovation. But when I look around, in my country(Nepal), I get disappointed. I rarely see innovations happening. Then a very important question arises- Where are we all heading towards? What are our priorities and how are we planning to achieve them, not as an individual but as a country. Since my childhood, I have been told that the strength of our country lies in its Agriculture , Water Resources and Tourism . But are we the leaders in the above field? Do we dominate the world in those fields and the answer is NO. Am I asking for these changes too early? I fear that these may be our illusions to keep the hope around. I also use to hear, " Nepal ko dhan Hariyo Ban ", which means "Green Forests are the riches of Nepal". But its been ages, that I have not heard it being said. Some how, those riches are lost. Are

Teaching II

The things that I believed while I was teaching was that, no class must go by without student learning anything new. Every class must have something new to the students, otherwise it would be waste of their time. Usually, there are two things that contradict with each other during a class , one is called : Exams point of view or preparation for exams and other is the teaching new concepts and ideas and their relationships.  On my terms, I always made student practise physics numerical questions on the class. This gave me and them the opportunity to work out the concepts and their application. In this particular class, there was nothing new to learn but there was a thing to learn about ways of applying things that the student learnt. So confusing , isn't it. To clarify let me give you all an example: Imagine you are in the cockpit of and airplane and the pilot tells you everything about each of the buttons and controls in the cockpit. Now let us also assume, that you have really mem

Teaching I

Its almost been 3/4 years that I started to teach A Level Physics . I started with a reason to share all the things that I had learnt during my A level days. That seemed more convincing to me for doing so. But I had to manage time for my own  studies , as well. For the third person, it looked as if I was walking on two separate roads. But from my perspective, I saw that the path was one and only one.  I kept studying my engineering courses and preparing lectures. But it did not happen the way it usually does. Mostly, preparing a lecture would mean holding a pen and paper and listing the points to be taught. It might sound stupid and odd but my most lectures were prepared on my mind. I usually prepared my class, as I was cycling to campus, or sometime while listening to music and most of the time while day dreaming. It is really odd thing to say but it was usually this way, that I learnt to teach. If I had gone the other way, by looking at the books an preparing a note then I would ha

Time Series Data

Time Series Data is one of the most common type of data that requires analysis. The above data is a typical example of a time series data (data has been taken from ). In the graph we can see the variation in the Customer Price Index of US with respect to time. Now, this information would be very interesting from an economic point of view.  But how do you go on analysing the data? What different calculations or operations would be required to be performed? Before going into it I would like to share another example of time series data.  The data below (photo of oscilloscope)is taken from one of my previous research on DC motor . The graph shows the variation of voltage across an electric component.   Both of the above examples are the time series data. The data is obviously the output of the system, where an independent variable is being inputted. Now this particular study, is very interesting from the economic point of view and from the signal point of view( in case o