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Symbolic data analysis (SDA) is an extension of standard data analysis where symbolic data tables are used as input and symbolic objects are made output as a result. The data units are called symbolic since they are more complex than standard ones, as they not only contain values or categories, but also include internal variation and structure.
The types of questions (e.g.: closed, multiple-choice, open) should fit the data analysis techniques available and the goals of the survey. The manner (random or not) and location (sampling frame) for selecting respondents will determine whether the findings will be representative of the larger population .
Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values.
Place this template at the bottom of appropriate articles in statistics: {{Statistics}} For most articles transcluding this template, the name of that section of the template most relevant to the article (usually where a link to the article itself is found) should be added as a parameter. This configures the template to be shown with all but ...
In addition, the choice of appropriate statistical graphics can provide a convincing means of communicating the underlying message that is present in the data to others. [1] Graphical statistical methods have four objectives: [2] The exploration of the content of a data set; The use to find structure in data; Checking assumptions in statistical ...
Statistics is often/usually a separate department and major in American universities, and we don't use a template for history, or for large subfields like American history (see the Missouri Compromise article for how specific their infobox is), and for math the infoboxes are usually subfields like Template:topology or Template:analysis-footer.
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
Convolutional neural network – a convolutional neural net where the convolution is performed along the time axis of the data is very similar to a TDNN. Recurrent neural networks – a recurrent neural network also handles temporal data, albeit in a different manner. Instead of a time-varied input, RNNs maintain internal hidden layers to keep ...