1539 search results for "Regression"

Confidence interval for predictions with GLMs

November 4, 2011
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Confidence interval for predictions with GLMs

Consider a (simple) Poisson regression . Given a sample where , the goal is to derive a 95% confidence interval for given , where is the prediction. Hence, we want to derive a confidence interval for the prediction, not the potential observation, i.e. the dot on the graph below > r=glm(dist~speed,data=cars,family=poisson) > P=predict(r,type="response", + newdata=data.frame(speed=seq(-1,35,by=.2))) > plot(cars,xlim=c(0,31),ylim=c(0,170)) > abline(v=30,lty=2)...

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How much does "Beta" change depending on time?

November 1, 2011
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How much does "Beta" change depending on time?

You may often use "Beta" to measure the market exposure of your portfolio because it's easy to calculate.Since I have been wondering how much "Beta" change depending on time, more precisely writing, data-set and the period of return time series, I...

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Bootstrapping a Single Statistic (k=1) The following example…

November 1, 2011
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Bootstrapping a Single Statistic (k=1)

The following example…

Bootstrapping a Single Statistic (k=1) The following example generates the bootstrapped 95% confidence interval for R-squared in the linear regression of miles per gallon (mpg) on car weight (wt) and displacement (disp). The data source is mtcars. The...

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Using Sparse Matrices in R

October 31, 2011
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Using Sparse Matrices in R

Introduction I’ve recently been working with a couple of large, extremely sparse data sets in R. This has pushed me to spend some time trying to master the CRAN packages that support sparse matrices. This post describes three of them: the Matrix, slam and glmnet packages. The first two packages provide data storage classes for

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Testing for…

October 31, 2011
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Testing for…

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...

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Learning R: Project 1, Part 2

October 30, 2011
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Learning R: Project 1, Part 2

So it's been a week since I started down this path.  I worked most of this out over last weekend, went to a conference, had hectic week at work, and then realized I lost my work.  Gah.I'll be posting my general thoughts on R later.  Most...

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Bayesian ideas and data analysis

October 30, 2011
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Bayesian ideas and data analysis

Here is another Bayesian textbook that appeared recently. I read it in the past few days and, despite my obvious biases and prejudices, I liked it very much! It has a lot in common (at least in spirit) with our Bayesian Core, which may explain why I feel so benevolent towards Bayesian ideas and

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Modelling with R: part 5

October 30, 2011
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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...

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Predictability of stock returns : Using acf()

October 27, 2011
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Predictability of stock returns : Using acf()

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...

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Pair trading strategy : how to use "PairTrading" package

October 25, 2011
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Pair trading strategy : how to use "PairTrading" package

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...

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