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Line 80: − The difference between an app finding a relationship between two variables through correct and incorrect means maybe very difficult to detect. However, the graphs it produces should include most of the following: Interpolation for irregular time series. Change point or breakpoint detection. Outlier detection. Smoothing. Cycles decomposition using a model like ARIMA. Ex. kayak season is in the summer or lunch is at exactly 1pm. Not consistently timed event shape detection like dinner is anywhere between 4 and 10pm and causes a particular 2 hour spike in glucose (I know this is a bad example but ...). Removal of effect of variables found to correlate with this one to show residuals. +
Finding relations between variables in time series (edit)
Revision as of 01:17, 6 July 2023
, 01:17, 6 July 2023→What to expect from the complete analysis tool
=== What to expect from the complete analysis tool ===
=== What to expect from the complete analysis tool ===
User without experience in statistical analysis will not be able to tell the difference between correctly computed correlations and poorly computed ones. However, a genuinely complete analysis produces graphs which should include most of the following: Interpolation for irregular time series. Change point or breakpoint detection. Outlier detection. Smoothing. Cycles decomposition using a model like ARIMA. Ex. kayak season is in the summer or lunch is at exactly 1pm. Detection of repeated shapes implying similar events that are not cyclical; like dinner is anywhere between 4pm and 10pm and causes a particular 2 hour spike in glucose. Removal of effect of variables found to correlate with this one to show residuals.
[[Category:Data analysis]]
[[Category:Data analysis]]