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For example, parameter data consists of the different values for varying conditions in an experiment (e.g., temperature, time). The measured data (or variables) are the measurements taken in the experiment under these varying conditions. Many statistical databases are sparse with many null or zero values.
How high, or how low, is determined by the value of the attribute (and in fact, an attribute could be just the word "low" or "high"). [1] For example see: Binary option ) While an attribute is often intuitive, the variable is the operationalized way in which the attribute is represented for further data processing .
List of fields of application of statistics; List of graphical methods; List of statistical software. Comparison of statistical packages; List of graphing software; Comparison of Gaussian process software; List of stochastic processes topics; List of matrices used in statistics; Timeline of probability and statistics; List of unsolved problems ...
"Use of data requires knowledge about the different sources of uncertainty. Measurement is a process. Is the system of measurement stable or unstable? Use of data requires also understanding of the distinction between enumerative studies and analytic problems." "The interpretation of results of a test or experiment is something else.
For example, the sample mean is an unbiased estimator of the population mean. This means that the expected value of the sample mean equals the true population mean. [1] A descriptive statistic is used to summarize the sample data. A test statistic is used in statistical hypothesis testing. A single statistic can be used for multiple purposes ...
Typically a population is very large, making a census or a complete enumeration of all the values in that population infeasible. A sample thus forms a manageable subset of a population. In positivist research, statistics derived from a sample are analysed in order to draw inferences regarding the population as a whole
In the field of statistics, these alternative interpretations allow for the analysis of different datasets using distinct methods based on various models, aiming to achieve slightly different objectives. When comparing the competing schools of thought in statistics, pragmatic criteria beyond philosophical considerations are taken into account.
Plot with random data showing heteroscedasticity: The variance of the y-values of the dots increases with increasing values of x. In statistics, a sequence of random variables is homoscedastic (/ ˌ h oʊ m oʊ s k ə ˈ d æ s t ɪ k /) if all its random variables have the same finite variance; this is also known as homogeneity of variance ...