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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 Warmi

Mathematical Reality


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 v

Entrepreneurship II


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


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 ca