Input Output Testing for regression Input: advertising=c(1,2,3,4,5) sales=c(1,1,2,2,4) sales.Reg=lm(sales~advertising) summary(sales.Reg) Output: > advertising=c(1,2,3,4,5) > sales=c(1,1,2,2,4) > > sales.Reg=lm(sales~advertising) > summary...

In our exercise of learning modelling in R, we have till now succeeded in doing the following:Importing the dataPreparing and transforming the dataRunning a logistic regressionCreating a decision treeSpecifically, we created a decision tree using the r...

In my previous post, I employed a rather crude and non-parametric approach to see if I could predict the direction of stock returns using the function runs.test(). Lets go a step further and try modelling this with a parametric econometric approach. The company that I choose for the study is INFOSYS (NSE code INFY). Lets start...

Mr.Ishikawa(my old friend) and I developed "PairTrading" package, and uploaded it on CRAN.This article shows you how you can use it.The pair trading is a market neutral trading strategy and gives traders a chance to profit regardless of market conditions. The idea of this strategy is quite simple. 1 : Select two stocks(or any assets) moving similarly 2 : Short...

Bill Bolstad wrote a reply to my review of his book Understanding computational Bayesian statistics last week and here it is, unedited except for the first paragraph where he thanks me for the opportunity to respond, “so readers will see that the book has some good features beyond having a “nice cover”.” (!) I simply processed

My previous post talked about how we can employ PCA on the data for multiple stock returns to reduce the number of variables in explaining the variance of the underlying data. But the idea was greeted with skepticism by many. A caveat to the applicatio...

There are several blog posts, websites (and even books) explaining the transition from using another statistical system (e.g. SAS, SPSS, Stata, etc) to relying on R. Most of that material treats the topic from the point of view of i- … Continue reading →

Support vector machines (SVM’s) are the “big iron” of the data mining world, especially suited for extreme data intensive tasks like image classification, biosequence processing, handwriting recognition, etc. Dr. Lutz Hamel, author of “Knowledge Discovery with Support Vector Machines”, presents his online course “Introduction to Support Vector Machines In R” November 18 – December 16. “Support Vector Machines in...