Infographics 

Visual.ly is a one-stop shop for the creation of data visualizations and infographics, bringing together various persons based on shared interests.

This tool fetches data from certain period of activities happened so far.

  

As a part of this assignment, I came across many top sites which help create a good infographic resume. I came across a plethora of such sites but i chose visual.ly for a detailed study.

For those of us who wants to design a different looking resume from others, it helps a lot.

I have gone through this and build a different looking resume.

The steps to be followed to create resume through visul.ly

1       Go to the following link http://visual.ly/

2        Click on the create option à http://create.visual.ly/

3         I chose resume by Kelly

4        I chose Helen Wheels, black gradient to create my resume

5         I uploaded my details from my LinkedIn profile à http://www.linkedin.com

Pros:

- Allows choosing between 4-5 themes.

- Options to tweet, share on FB, Pin and share on other social media sites

- Provides option to download as PDF, mail to your email ID.

- Easy Accessibility.

- Different gradient versions.

- Ease of data access, no need to edit/enter any data.

Cons -:

- Doesn't allow playing around with the format of the resume.

- Less options to customise the graphics.

- Limited number of themes to select.

IT Lab session 8

FRIDAY, 15 MARCH 2013

                              IT Lab session 8

We will be doing Panel Data Analysis of "Produc" data

We will be analysing on three types of model :
      Pooled affect model
      Fixed affect model
      Random affect model 

Then we will be determining which model is the best by using functions:
       pFtest : for determining between fixed and pooled
       plmtest : for determining between pooled and random
       phtest: for determining between random and fixed

Commands:

Loading data: 
> data(Produc, package ="plm")
> head(Produc)

Pooled Affect Model 

> pool <- plm(log(pcap)~ log(hwy) + log(water) + log(util) + log(pc) + log(gsp) + log(emp) + log(unemp) , data =Produc, model=("pooling"), index = c("state","year"))
> summary(pool)

Fixed Affect Model:

> fixed <- plm(log(pcap)~ log(hwy) + log(water) + log(util) + log(pc) + log(gsp) + log(emp) + log(unemp) , data =Produc, model=("within"), index = c("state","year"))

> summary(fixed)

Random Affect Model:

> random <- plm(log(pcap)~ log(hwy) + log(water) + log(util) + log(pc) + log(gsp) + log(emp) + log(unemp) , data =Produc, model=("random"), index = c("state","year"))

> summary(random)

Comparison

The comparison between the models would be a Hypothesis testing based on the following concept:

H0: Null Hypothesis: the individual index and time based params are all zero

H1: Alternate Hypothesis: atleast one of the index and time based params is non zero

Pooled vs Fixed

Null Hypothesis: Pooled Affect Model

Alternate Hypothesis : Fixed Affect Model

Command:

> pFtest(fixed,pool)

Result:

data:  log(pcap) ~ log(hwy) + log(water) + log(util) + log(pc) + log(gsp) +      log(emp) + log(unemp) 
F = 56.6361, df1 = 47, df2 = 761, p-value < 2.2e-16
alternative hypothesis: significant effects 

Since the p value is negligible so we reject the Null Hypothesis and hence Alternate hypothesis is accepted which is to accept Fixed Affect Model.

Pooled vs Random

Null Hypothesis: Pooled Affect Model

Alternate Hypothesis: Random Affect Model

Command :

> plmtest(pool)

Result:

        Lagrange Multiplier Test - (Honda)

data:  log(pcap) ~ log(hwy) + log(water) + log(util) + log(pc) + log(gsp) +      log(emp) + log(unemp)

normal = 57.1686, p-value < 2.2e-16
alternative hypothesis: significant effects 

Since the p value is negligible so we reject the Null Hypothesis and hence Alternate hypothesis is accepted which is to accept Random Affect Model.

Random vs Fixed

Null Hypothesis: No Correlation . Random Affect Model

Alternate Hypothesis: Fixed Affect Model

Command:

 > phtest(fixed,random)

Result:

        Hausman Test

data:  log(pcap) ~ log(hwy) + log(water) + log(util) + log(pc) + log(gsp) +      log(emp) + log(unemp)

chisq = 93.546, df = 7, p-value < 2.2e-16
alternative hypothesis: one model is inconsistent 

Since the p value is negligible so we reject the Null Hypothesis and hence Alternate hypothesis is accepted which is to accept Fixed Affect Model.

Conclusion: 

So after making all the comparisons we come to the conclusion that Fixed Affect Model is best suited to do the panel data analysis for "Produc" data set.

Hence , we conclude that within the same id i.e. within same "state" there is no variation.