The Determinant and Annova way

DAY#3

ASSIGNMENT 1a:

Fit ‘lm’ and comment on the applicability of ‘lm’

Plot1: Residual vs Independent curve.

Plot2: Standard Residual vs independent curve.

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

> file

  mileage groove

1       0 394.33

2       4 329.50

3       8 291.00

4      12 255.17

5      16 229.33

6      20 204.83

7      24 179.00

8      28 163.83

9      32 150.33

> x<-file$groove

> x

[1] 394.33 329.50 291.00 255.17 229.33 204.83 179.00 163.83 150.33

> y<-file$mileage

> y

[1]  0  4  8 12 16 20 24 28 32

> reg1<-lm(y~x)

> res<-resid(reg1)

> res

         1          2          3          4          5          6          7          8          9

 3.6502499 -0.8322206 -1.8696280 -2.5576878 -1.9386386 -1.1442614 -0.5239038  1.4912269  3.7248633

> plot(x,res)

 As the plot is parabolic, the regression cannot be performed.

Assignment 1 (b) -Alpha-Pluto Data

Fit ‘lm’ and comment on the applicability of ‘lm’.

Plot1: Residual vs Independent curve.

Plot2: Standard Residual vs independent curve.

Also do:

Qq plot

Qqline

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

> file

   alpha pluto

1  0.150    20

2  0.004     0

3  0.069    10

4  0.030     5

5  0.011     0

6  0.004     0

7  0.041     5

8  0.109    20

9  0.068    10

10 0.009     0

11 0.009     0

12 0.048    10

13 0.006     0

14 0.083    20

15 0.037     5

16 0.039     5

17 0.132    20

18 0.004     0

19 0.006     0

20 0.059    10

21 0.051    10

22 0.002     0

23 0.049     5

> x<-file$alpha

> y<-file$pluto

> x

 [1] 0.150 0.004 0.069 0.030 0.011 0.004 0.041 0.109 0.068 0.009 0.009 0.048

[13] 0.006 0.083 0.037 0.039 0.132 0.004 0.006 0.059 0.051 0.002 0.049

> y

 [1] 20  0 10  5  0  0  5 20 10  0  0 10  0 20  5  5 20  0  0 10 10  0  5

> reg1<-lm(y~x)

> res<-resid(reg1)

> res

         1          2          3          4          5          6          7

-4.2173758 -0.0643108 -0.8173877  0.6344584 -1.2223345 -0.0643108 -1.1852930

         8          9         10         11         12         13         14

 2.5653342 -0.6519557 -0.8914706 -0.8914706  2.6566833 -0.3951747  6.8665650

        15         16         17         18         19         20         21

-0.5235652 -0.8544291 -1.2396007 -0.0643108 -0.3951747  0.8369318  2.1603874

        22         23

 0.2665531 -2.5087486

> plot(x,res)

> qqnorm(res)

 

> qqline(res)

 

Assignment 2: Justify Null Hypothesis using ANOVA

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

> file

   Chair Comfort.Level Chair1

1      I             2      a

2      I             3      a

3      I             5      a

4      I             3      a

5      I             2      a

6      I             3      a

7     II             5      b

8     II             4      b

9     II             5      b

10    II             4      b

11    II             1      b

12    II             3      b

13   III             3      c

14   III             4      c

15   III             4      c

16   III             5      c

17   III             1      c

18   III             2      c

> file.anova<-aov(file$Comfort.Level~file$Chair1)

> summary(file.anova)

            Df Sum Sq Mean Sq F value Pr(>F)

file$Chair1  2  1.444  0.7222   0.385  0.687

Conclusion: P Value  = 0.687

Since, the p - value is high, we cannot reject the null hypothesis. Thus we can say that all the types of chairs are not different.

Day-2 The Regression way

 The Day started with building concept to go for regression analysis. The platform is set with problems using matrix ,selecting columns and rows, multiplying of matrices and  finally regression and distribution.

Assignment-1

Assignment 1:

Create 2 matrices of 3*3 and select 1 column in matrix 1 and matrix 2 merge them into another matrix using cbind command.

command:

z1<-c(1:9)

dim(z1)<-c(3,3)

z2<-c(10:18)

dim(z2)<-c(3,3)

x<-z1[,3]

y<-z2[,1]

z3<-cbind(x,y)

z3

 

Assignment 2:

multiply matrix 1 and matrix 2

z1%*%z2

Assignment 3:

Read historical data of indices from NSE site from Dec 1 2012 to Dec 31 2012. Find Regression and Residuals.

 To read NSE file 

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

reg<-lm(high~open,data=nse)

To find residuals

residuals(reg)

Assignment 4:

Generate a Normal distribution data and plot it.

x=seq(70,130,length=200)

y=dnorm(x,mean=100,sd=10)

plot(x,y)

The Analyst Way

  

Assignment 1: Plotting Histogram

Assignment 2: Plotting point and line diagram 

                           

                                                 

                                           Assignment 3: Scatter Diagram for High and Low data

Assignment 4: Volatility of Max and Min of share value

Code:

>zcol3<-z[,3]

>zcol4<-z[,4]

mergeddata<-c(zcol3,zcol4)
> summary(mergeddata)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
   4888    5660    5723    5758    5884    6021
> range(mergeddata)
[1] 4888.20 6020.75