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  2. Pivot table - Wikipedia

    en.wikipedia.org/wiki/Pivot_table

    A pivot table is a table of values which are aggregations of groups of individual values from a more extensive table (such as from a database, spreadsheet, or business intelligence program) within one or more discrete categories. The aggregations or summaries of the groups of the individual terms might include sums, averages, counts, or other ...

  3. Count data - Wikipedia

    en.wikipedia.org/wiki/Count_data

    The statistical treatment of count data is distinct from that of binary data, in which the observations can take only two values, usually represented by 0 and 1, and from ordinal data, which may also consist of integers but where the individual values fall on an arbitrary scale and only the relative ranking is important. [example needed]

  4. Microsoft Excel - Wikipedia

    en.wikipedia.org/wiki/Microsoft_Excel

    A chart created with data from a Microsoft Excel spreadsheet that only saves the chart. To save the chart and spreadsheet save as .XLS. XLC is not supported in Excel 2007 or in any newer versions of Excel. Dialog .xld: Used in older versions of Excel. Archive .xlk: A backup of an Excel Spreadsheet Add-in (DLL) .xll

  5. Spreadsheet - Wikipedia

    en.wikipedia.org/wiki/Spreadsheet

    The Spreadsheet Value Rule. Computer scientist Alan Kay used the term value rule to summarize a spreadsheet's operation: a cell's value relies solely on the formula the user has typed into the cell. [48] The formula may rely on the value of other cells, but those cells are likewise restricted to user-entered data or formulas.

  6. Comma-separated values - Wikipedia

    en.wikipedia.org/wiki/Comma-separated_values

    Comma-separated values (CSV) is a text file format that uses commas to separate values, and newlines to separate records. A CSV file stores tabular data (numbers and text) in plain text , where each line of the file typically represents one data record .

  7. Kernel density estimation - Wikipedia

    en.wikipedia.org/wiki/Kernel_density_estimation

    Kernel density estimation of 100 normally distributed random numbers using different smoothing bandwidths.. In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights.