Thursday, July 26, 2012

Best Free Statistical Softwares for Data Analysis and Download Links

Today, I bring you a list of 3 of the best analytical software for statisticians. What is most surprising is that you don't have to pay "a shilling" to get any of them. i.e They are totally free!!!! Most less-comprehensive analytical tools don't even come this easily, but anyone (including myself), would be happy to get this totally free of any charge. There are other free statistical software, but these are the 3 that I selected as excellent standouts. They are Epi Info, Scilab, and R .

Software                                                      Function                                                       Link

Epi Info                     Questionnaire Analysis in Public Health,                                 Epi Info™
                                 Biostatistics, Medical Statistics e.t.c Has an extraordinary
                                 Mapping tool. Perfect for test on Contingency Tables &
                                 Logistic Regression.           

Scilab                       Mathematical and Statistical analysis like Integration,                   Scilab 
                                Differentiation, Matrices, Vectors, ANOVA, Regression etc.
                                Has interface similar to that of MATLAB & can be considered
                                a mini-MATLAB.

R                             One of the best statistical tools for Data Analysis and Linear Algebra.        R
                                Perfect for Matrix and Vector analysis and constructing. Good plotting
                                tool & Random Number Generator for most statistical distributions
                                also embedded.

There are also many others that are really cool and flexible. I will be posting them very soon.
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Tuesday, May 1, 2012

Thought for the day.

Russians have made a lot of contribution in probability, stochastic & functional analysis as it relates to statistics.

Indian have contributed much in Inferential Statistics.

American have contributed immensely in almost all areas.

Do we give it to the Americans?

Your thoughts.

Lovely day!!!

Men may soon outlive women

Women have had a longer life expectancy than men, ever since records began, but this may be set to change in the not-too-distant future.

An adviser to the Office for National Statistics (ONS) has predicted that by the time today's 12-year-old boys reach the age of 30, they should be able to enjoy a life expectancy of just over 87 years.

This means they will be matching the life expectancy of their female counterparts or the first time.

And they may even outlive females born at the same time soon after, according to Professor Leslie Mayhew, a statistician at City University, London.

Professor Mayhew believes that men's increasingly healthy lifestyles, falling smoking rates and decreasing number of deaths from heart attacks will bring their life expectancy in line with that of women.

In an interview with the Sunday Times, he said: 'There has been a huge decline in the numbers working in heavy industry, far fewer males smoke than before and there is much better treatment for heart disease, which tends to affect more males than females.'

Figures from the British Heart Foundation show that one in five male deaths and one in eight female deaths in 2009 were from coronary heart disease.


Who is the Father of Modern Statistics?

A) Sir Ronald Fisher

B) Rao Calyampudi Radharikrishnan

C) David Blackwell

D) Others

Note: The options above are the author's point of view there are many distinguished Statisticians around the World too many to mention.


A. V. Oladugba and C. E. Ogbonnaya
Department of Statistics
University of Nigeria, Nsukka

The purpose of this work is to find out if the same conclusion will be reached when “actual value” and “estimated missing value” of some orthogonal block designs are compared using three imputation of estimating missing value methods (the mean method, least squares method and regression imputation method), and also to see if there is any significant difference in the three imputation methods chosen. Estimation of missing values using these imputation methods do not void the result obtained from statistical analysis since the conclusion reached using any imputation method agrees with the conclusion reached using the complete data. From the result obtained in the significance test, we conclude that the difference in estimates for missing observations obtained using the mean method, least squares method and regression imputation is not significant. This implies that the difference in estimates obtained using the three methods are ignorable.

For further details, contact the author on

Further works on Estimation of Missing Data in Latin Square & Graceo-Latin Squares coming up.