Business Application IT Lab

IT Business Application lab Assignment#2

Session 2:
Date:15th Jan,2013

Today we have learnt about creation,inverse,transpose and multiplication of matrices.Then we moved on to
regression and residual analysis by taking NSE historical data for NIFTY index for a certain period.Finally we had an introductory idea about how to plot normally distributed curve.

Assignment 1: 

Create two matrices of say size 3 X 3 and select the column 1 from one matrix and column 3 from second matrix. After selecting the columns in objects say x1 and x1  merge these two columns using cbind to create a new matrix .

Solution:

To create a matrix:

x <- c[1:9]

dim(x) <- c(3,3)

y <- c[10:18]

dim(y) <- c(3,3)

To select a column

z1 <- x[ ,3]

z2 <- y[ ,2]

z3<- cbind(z1,z2)

Output:

Assignment 2:

Multiply both the matrices.

Solution:

z <- x %*% y

Output:

Assignment 3:

Read historical data of NIFTY indices from NSE for the period 1st Dec 2012 to 31st Dec 2012. Find regression and residuals

Solution:

To read the csv file:

nse <- read.csv(file.choose(),header=T)

For finding the regression and residuals the following commands are used

reg <- lm(High ~ Open , data = nse)

residuals(reg)

Output:

Assignment 4:

Generate a normal distribution data and plot it.

Solution:

For creating the ND following commands are used:

x<-rnorm(40,0,1)

y<-dnorm(x)

For plotting the data

plot(x,y)

Output: