Most Recent Tibco TCP-SP Exam Dumps

A heat map is a visualization type that is similar to a cross table, but instead of displaying numbers, it displays colors that represent the values in the cells. A heat map can be used to show the distribution of data across two or more categories using a color gradient. A heat map can also be colored by a third column, which can be selected from the Color by drop-down list in the Cross Table Properties dialog. A heat map can have different color schemes applied to different color scheme groupings, which are containers for one or more axis values that share the same color scheme. To learn more about coloring in cross tables and heat maps, seeColoring in Cross Tables and Heat Maps.Reference:Cross Table Properties -- Colors,Coloring in Cross Tables and Heat Maps,Specifying text color and background color for cross table cells,How to set a custom color scheme for a particular column.

BinByEvenDistribution() is a binning function that creates a binned column where each bin has the same number of unique values as the others. The last bin may have more unique values than the others. The first argument is the column to bin and the second argument is the number of bins. This function can be used on a numeric type column to group the values into equal frequency bins. The other binning functions are either not valid (BinByTimeSpan(), BinByNumbers()) or not applicable to a numeric type column (BinByDateTime(), BinBySubstring()).Reference:Binning Functions,How to Use Binning

Before creating an information link, you need to setup a data source that connects to the database where the data tables are stored. A data source defines the connection parameters, such as the server name, port number, user name, password, and database name.You can create a data source using the Data Source Manager tool in the Spotfire Analyst client1.Once you have a data source, you can use the Information Designer tool to create column, filter, and procedure elements that will be the building blocks of your information link2.You can then add these elements to your information link and specify the join path, filters, prompts, and other options3.Reference:

Data Source Manager - TIBCO Software

Information Designer - TIBCO Software

Creating an Information Link - TIBCO Software

A Spotfire analysis is saved as a file with the extension.dxp, which stands for Data eXploration Project. This file contains the data, visualizations, pages, filters, markings, and other settings of the analysis. A .dxp file can be opened in Spotfire Analyst or Spotfire Web Player, or uploaded to Spotfire Server for sharing and collaboration.Reference:Extending Spotfire - TIBCO Software,File data sources - TIBCO Software,Extensions - TIBCO Software

Normalization is a transformation that adjusts the values in one or more columns of a data table to a common scale, such as between 0 and 1, or with a mean of 0 and a standard deviation of 1. Normalization can be useful for comparing data from different sources, removing outliers, or preparing data for further analysis.Spotfire provides several methods for normalizing data, which are briefly described below1:

Z-score calculation: This method transforms the values in each column by subtracting the mean and dividing by the standard deviation. The resulting values have a mean of 0 and a standard deviation of 1, and are also known as standard scores or z-scores. This method is useful for comparing data that have different units or scales, or for identifying outliers that deviate from the mean by more than a certain number of standard deviations.

Normalize by standard deviation: This method transforms the values in each column by dividing by the standard deviation. The resulting values have a standard deviation of 1, but the mean is not changed. This method is useful for comparing the variability or dispersion of data across columns, or for reducing the effect of outliers on the mean.

Normalize by mean: This method transforms the values in each column by dividing by the mean. The resulting values have a mean of 1, but the standard deviation is not changed. This method is useful for comparing the relative magnitude or proportion of data across columns, or for scaling the data to a common unit.

Normalize by mode: This method transforms the values in each column by dividing by the mode, which is the most frequent value. The resulting values have a mode of 1, but the mean and standard deviation are not changed. This method is useful for comparing the frequency or popularity of data across columns, or for scaling the data to a common unit.

Normalize by median: This method transforms the values in each column by dividing by the median, which is the middle value when the data are sorted. The resulting values have a median of 1, but the mean and standard deviation are not changed. This method is useful for comparing the central tendency or location of data across columns, or for scaling the data to a common unit.

To add a normalization transformation to a data table, either when loading a new data table or after the data is already in Spotfire, follow the steps described in the references23.Reference:

Details on Normalization

Spotfire Tips & Tricks: Normalize/Standardize your data with Spotfire

How to Use the Map Chart