Online Survey For Data Analysis
Online Survey For Data Analysis
Analytics play a pivotal role in the data flow scheme within a retail organization. A typical retailer generates more than thousands of data points through POS machine. It is difficult for a retailer to make strategic decisions based on this raw data.
Now I see many researchers grappling with overwhelm at managing and analyzing enormous data sets.
To start off with, the data list that you use must be in the flat file format or data list feature that is available in excel 2003 onwards. This means that all field names should not be based on data values. In other words, don't have January, February, etc. Instead use the label 'month'. Once you have set the data up this way, you can pivot the data as if the months are the field names by adding the months field to the column part of the pivot.
Another useful feature is the option to show row and column totals. You can switch this feature on and off. It is useful if you are tracking year to date figures or running totals throughout the year, without it having to be a separate exercise.
Having properly designed layout in the spreadsheet would save a lot of time in data entry using some of the build in functions in MS Excel. An effective data entry would result in data accuracy and integrity which is very critical for an improvement project.
I won't go into details, but we can take the derivative of the error function with respect to each latent vector in order to find changes to the vectors that will make them closer match the results of all of the games earlier in the season. I repeat this until there isn't any changes that will improve the error (batch gradient descent, for the detail-oriented folks out there).
The examples (taken from live studies) in quantitative and qualitative data analyses clearly show the difference between the two. Quantitative data analysis is about correct application of a statistical or mathematical tool while qualitative data analysis is correctly picking up a stray voice and visualize its impact on the whole analysis. For example, only one person has reported sweet taste of ice cream. Now, is it true for all or only for one person? This must be validated through another set of analysis or study.
A data mining technique may then search through this large data set and extract a previously unknown relationship between income levels, peoples existing debt and their ability to get a loan.
There is also another important factor to consider in data analysis and acquisition, and that is data recovery. It is quite natural in very tense working environments, and quite often data gets replaced or entirely deleted in odd circumstances. There have been incidents in the history where this type of data loss has led to the loss of millions of dollars.
Flash charts are always a better alternative to reporting engines. They can be used with all types of database and with greater flexibility. They facilitate extensive customization, which in turn helps in tailoring the appearance of charts. A significant advantage of using Flash charts is that they can be easily published on web.
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