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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 ...
A pivot need not be a statistic — the function and its value can depend on the parameters of the model, but its distribution must not. If it is a statistic, then it is known as an ancillary statistic .
For example, below is a chart of the S&P 500 since the earliest data point until April 2008. While the Oracle example above uses a linear scale of price changes, long term data is more often viewed as logarithmic: e.g. the changes are really an attempt to approximate percentage changes than pure numerical value.
It says what fraction of the variance of the data is explained by the fitted trend line. It does not relate to the statistical significance of the trend line (see graph); the statistical significance of the trend is determined by its t-statistic. Often, filtering a series increases r 2 while making little difference to the fitted trend.
Graphs that show a trend of data should illustrate the trend accurately in its context, rather than illustrating the trend in an exaggerated or sensationalized way. In short, don't draw misleading graphs. Choose a type of graph that is appropriate for the data you are illustrating. Cartesian coordinates
Power Pivot supports the use of expression languages to query the model and calculate advanced measures. Pivot tables or pivot charts may be used to explore the model once built. It is available as an add-in in Excel 2010, as a separate download for Excel 2013, and is included by default since Excel 2016.
When you are getting started, you might want to use {{Graphical timeline|help=on}} to generate a ready-made, empty template – or type {{subst:Graphical timeline/blank}} into a sandbox page, save the page, and edit the resulting code. Hopefully, the parameter names are pretty self-explanatory.
In statistical analysis, change detection or change point detection tries to identify times when the probability distribution of a stochastic process or time series changes. In general the problem concerns both detecting whether or not a change has occurred, or whether several changes might have occurred, and identifying the times of any such ...