Statistical Consultants Ltd


Statistical Programming / Data Processing Services

While working with Excel spreadsheets or text files, you may encounter scenarios where in order to get a job done, you need to make many repetitive actions which could take hours, days, or even weeks to perform manually.  Statistical Consultants Ltd provides programming services which can greatly speed up such processes.  Programming is often a necessary step needed to be taken before a statistical analysis can be performed. Customised programs written by Statistical Consultants Ltd would be run for the client and/or given to the client to run (along with a set of easy to follow instructions).  

Data Extraction Example - Extracting data from an untidy text file

Information in an untidy text file needs to be stored into a spreadsheet.  For example:
untidy text file

tidy spreadsheet
One option would be to copy and paste the information manually.  Although this would be a good idea for small amounts of data, it would be very time consuming if there were thousands of entries.
In situations where there is a lot of data to be processed, a far quicker alternative would be to have a customised program written which reads that information, processes it into a tidy data set, and then saves that data set as a spreadsheet.

If there are inconsistencies in the text file (like the alignment of the product numbers in the above example), it may still be possible for a program to be written.  In such a case, it would just be a matter of expanding the program to cater for those inconsistencies.

Automated Statistical Processes

In a consulting project which involves a statistical analysis (e.g. data analysis, statistical modelling, forecasting, data-mining etc), it may be possible for a program to be written which instantly repeats the same analysis to an updated data set.

For example, a program could be written which takes the following steps:
  1. The data is processed so that it can be read by and analysed using statistical software.  This could involve reformatting/editing values, imputing missing values or deriving new data.
  2. A statistical model would be fitted to the data.
  3. The model would be diagnosed to see if it is still suitable.
  4. The model would then be used to generate predictions, estimations, graphs and/or other information.


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