Data Preparation

Prepare and refine your raw data for effective analysis

Data preparation in NumXL

Data preparation is necessary to manipulate and transform raw data so that the information content enfolded in the data set can be exposed and more easily accessed. This is the first step in any data analytics project, and it encompass many tasks.

Data cleansing is one of the most common (and time consuming) tasks in data preparation. The objectives are to ensure the data is of the proper type (e.g. numeric) and values of the observations in the sample are complete, consistent, and valid, given the problem domain.

Another step in data preparation is setting up the variables (input and output) from raw data that will directly answer the analysis questions. This may involve change scores, scaling (or transform), coding (e.g. dummy variables), and formatting. 

Using NumXL Functions, you can examine and treat your raw data for errors, set up variables with greater ease, and apply your intuition to examine the results, not the mechanics.

Transform

Transform

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Data Fitting

Data Fitting

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Missing values

Missing values

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