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  2. Error bar - Wikipedia

    en.wikipedia.org/wiki/Error_bar

    Main page; Contents; Current events; Random article; About Wikipedia; Contact us; Help; Learn to edit; Community portal; Recent changes; Upload file

  3. Matplotlib - Wikipedia

    en.wikipedia.org/wiki/Matplotlib

    Matplotlib-animation [11] capabilities are intended for visualizing how certain data changes. However, one can use the functionality in any way required. These animations are defined as a function of frame number (or time). In other words, one defines a function that takes a frame number as input and defines/updates the matplotlib-figure based ...

  4. JavaScript - Wikipedia

    en.wikipedia.org/wiki/JavaScript

    This is an accepted version of this page This is the latest accepted revision, reviewed on 8 February 2025. High-level programming language Not to be confused with Java (programming language), Javanese script, or ECMAScript. JavaScript Screenshot of JavaScript source code Paradigm Multi-paradigm: event-driven, functional, imperative, procedural, object-oriented Designed by Brendan Eich of ...

  5. Category:Articles with example JavaScript code - Wikipedia

    en.wikipedia.org/wiki/Category:Articles_with...

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  6. Python (programming language) - Wikipedia

    en.wikipedia.org/wiki/Python_(programming_language)

    Twisted is a framework to program communications between computers, and is used (for example) by Dropbox. Libraries such as NumPy, SciPy and Matplotlib allow the effective use of Python in scientific computing, [209] [210] with specialized libraries such as Biopython and Astropy providing domain-specific functionality.

  7. Propagation of uncertainty - Wikipedia

    en.wikipedia.org/wiki/Propagation_of_uncertainty

    For example, the 68% confidence limits for a one-dimensional variable belonging to a normal distribution are approximately ± one standard deviation σ from the central value x, which means that the region x ± σ will cover the true value in roughly 68% of cases. If the uncertainties are correlated then covariance must be taken into account ...

  8. 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.