Regression Differences in the typical values across the dataset might initially be dealt with by constructing a regression model using certain explanatory variables to relate variations in the typical value to known quantities. There should then be a later stage of analysis to examine whether the errors in the predictions from the regression behave in the same way across the dataset. Thus, the question becomes one of the homogeneity of the distribution of the residuals, as the explanatory variables change. See
regression analysis.
Time series The initial stages in analyzing a time series may involve plotting values against time to examine the series' homogeneity in various ways: stability across time as opposed to a trend, stability of local fluctuations over time.
Combining information across sites In
hydrology, data series across a number of sites composed of annual values of the within-year annual maximum river flow are analysed. A common model is that the distributions of these values are the same for all sites apart from a simple scaling factor, so that the location and scale are linked in a simple way. There can then be questions of examining the homogeneity across sites of the distribution of the scaled values.
Combining information sources In
meteorology, weather datasets are acquired over many years of record, and, as part of this, measurements at certain stations may cease occasionally while, at around the same time, measurements may start at nearby locations. There are then questions as to whether, if the records are combined to form a single longer set of records, those records can be considered homogeneous over time. An example of homogeneity testing of wind speed and direction data can be found in Romanić
et al., 2015.
Homogeneity within populations Simple population surveys may assume that responses will be homogeneous across the whole population. Assessing the homogeneity of the population would involve examining whether the responses of certain identifiable
subpopulations differ from those of others. For example, car owners may differ from non-car owners, or there may be differences between different age groups. == Tests ==