!MUCH RELAXED DAY!

           The session was scheduled at 9 in the morning but delayed due to  some reasons.The morning session left us free without any work as a part of the group went to the lab to  get  a review of the experiments.The time was spent by  browsing the net,posting news on the HPC blog.Suddenly I thought of my laptop and went to the hostel to get it  to  fix the wireless network  connection.I took the help of my team mates to  delete the windows but we could not proceeed due some technical problems.Then sir instructed us to reinstallthe entire thing,but we could not complete the task.

          The afternoon session cannot be called as a class but it was much more like an interactive session.We were given an idea about the free software and the importance of source code.The wikipedia defines Free Software as-Free software, software libre or libre software is software that can be used, studied, and modified without restriction, and which can be copied and redistributed in modified or unmodified form either without restriction, or with restrictions that only ensure that further recipients have the same rights under which it was obtained and that manufacturers of consumer products incorporating free software provide the software as source code.

          Next we were introduced to linear regression problem,the approach to find the optimum solution and were asked to do the program in octave to find the values of constants and the guess value.As i browsed through the net I could gel the direct formula anh with the help of a few for loops the task could be easily performed.

          In statistics, linear regression is an approach to modeling the relationship between a scalar variable y and one or more explanatory variables denoted X. A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0).

The best fit line associated with the n points (x₁, y₁), (x₂, y₂), . . . , (x_n, y_n) has the form

y = mx + b

where

slope = m

=

n(xy) - (x)(y) n(x2) - (x)2

intercept = b

=

y - m(x) n

Here, means "the sum of." Thus

xy = sum of products = x₁y₁ + x₂y₂ + . . . + x_ny_n x = sum of x-values = x₁ + x₂ + . . . + x_n y = sum of y-values = y₁ + y₂ + . . . + y_n x² = sum of squares of x-values = x₁² + x₂²+ . . . + x_n²