By Avcibas, Memon, Sankur, Sayood
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Extra info for A Progressive Lossless Near-Lossless Image Compression Algorithm
The curve is bimodal in the interval a-f, unimodal in a-c and in d-f, monotonic in b-c and c-e and almost constant in c-d. In the interval b-e, the curve has a single minimum. (Ey, expected response; JC, environmental variable). 3). In both sections, we first present a model in which the explanatory variable is nominal, and then models in which the explanatory variable is quantitative, in particular models that are based on straight lines and parabolas. From the straight line and the parabola, we derive curves that are more useful in ecological data analysis.
3). In both sections, we first present a model in which the explanatory variable is nominal, and then models in which the explanatory variable is quantitative, in particular models that are based on straight lines and parabolas. From the straight line and the parabola, we derive curves that are more useful in ecological data analysis. For abundance data, we derive from them the exponential curve and the Gaussian curve, respectively, and for the analysis of presence-absence data, the sigmoid curve and the Gaussian logit curve.
In this book we use four kinds of scales: - nominal scales - ordinal scales - interval scales - ratio scales. The first scale is referred to as being 'qualitative'; the last two are referred to as 'quantitative'. e. nominal scales have fewer restrictions than ordinal scales, ordinal scales fewer than interval scales and interval scales fewer than ratio scales. A scale with fewer restrictions is 'weaker' than a scale with more restrictions, which is 'stronger'. A nominal scale is the least restrictive.