Session 3

ASSIGNMENT 1

(a) Using milage-groove data, fit 'lm' and comment on the application of 'lm'

>>Plot residual vs independent
>>Standardized vs independent
>>qqplot and  qqline

As it is type 2 ,parabolic, we will not be able to do regression.

qqplot and qqline

(b) Using alpha-pluto.

As it is randomplot, thus we can have regression here.

 qqplot and qqline

residual value

ASSIGNMENT 2

Calculate P value

P value =0.687

as p value is more than 5%, we accept the null hypothesis .

Session 2

Session 2

(!) Create Matrix and combine two columns

Syntax
> z<-c(1:9)
> dim(z)<-c(3,3)
> z1<-c(4,6,7,8,2,3,0,1,9)
> dim(z1)<-c(3,3)
>x<-z[,3]
>y<-z1[,3]
>z3<-cbind(x,y)
>z3

2) Multiplication of two matrix

Syntax

>obj1<-z

>obj2<-z1

>obj1%*%obj2

3)Regression

Syntax

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

>high<-z[,3]

>low<-z[,2]

>reg1<-lm(high~open,data=z)

>reg1

>plot(reg1)

>residual(reg1)

>cbind(high,open)

4) To generate data for normal distribution and plot the graph

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

>z<-rnorm(150,0,1)

>y<-dnorm(z)

>plot(z,y)

IT-BAL-1st Session

Assignment 0-

Plot Zcol1 as histogram

Assignment 1-

Plot both lines and point

Assignment 2-

Plot low values as scatter plot

Assignment 3-

Maximum Volatility