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This is a list of statistical procedures which can be used for the analysis of categorical data, also known as data on the nominal scale and as categorical variables. General tests [ edit ]
Black = unfiltered data; red = data averaged every 10 points; blue = data averaged every 100 points. All have the same trend, but more filtering leads to higher r 2 of fitted trend line. The least-squares fitting process produces a value, r-squared ( r 2 ), which is 1 minus the ratio of the variance of the residuals to the variance of the ...
96% confidence bands around a local polynomial fit to botanical data. A confidence band is used in statistical analysis to represent the uncertainty in an estimate of a curve or function based on limited or noisy data. Similarly, a prediction band is used to represent the uncertainty about the value of a new data-point on the curve, but subject ...
Given a sample from a normal distribution, whose parameters are unknown, it is possible to give prediction intervals in the frequentist sense, i.e., an interval [a, b] based on statistics of the sample such that on repeated experiments, X n+1 falls in the interval the desired percentage of the time; one may call these "predictive confidence intervals".
The Chou–Fasman method is an empirical technique for the prediction of secondary structures in proteins, originally developed in the 1970s by Peter Y. Chou and Gerald D. Fasman. [ 1 ] [ 2 ] [ 3 ] The method is based on analyses of the relative frequencies of each amino acid in alpha helices , beta sheets , and turns based on known protein ...
Gann watched for important tops and bottoms to form on a daily, weekly, or monthly chart and drew his angles from these changes in trend. When the trend is up and the price stays in the space above an ascending angle without breaking below it, the market is strong; when the trend is down and the price remains below a descending angle without breaking above it, the market is weak.
Forecasting is the process of making predictions based on past and present data. Later these can be compared with what actually happens. Later these can be compared with what actually happens. For example, a company might estimate their revenue in the next year, then compare it against the actual results creating a variance actual analysis.
Centered moving average methods utilize the data found in the single moving average methods by taking an average of the median-numbered data set. However, as the median-numbered data set is difficult to calculate with even-numbered data sets, this method works better with odd-numbered data sets than even. [13]
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