Hypotheses were tested to distinguish between three possible causes of variation: (i) habitat complexity, (ii) intrinsic factors of each area (regardless of the presence/absence of shells), and (iii) their interaction.
The general idea goes back to English biologist Conrad Waddington, who observed that the wild-type populations of fruit fly in the lab tend to show less variation (i.
The FA can be expressed as: z_{ji} = a_{f1} f_{1i} + a_{f2} f_{2i} + a_{f3} f_{3i} + cdots + a_{fm} f_{mi} + e_{fi},where z is the measured variable, a is the factor loading, f is the factor score, e the residual term accounting for errors or other source of variation, i the sample number and m the total number of factors.
The FA can be expressed as: Z_{ij} = a_{f1} x_{1i} + a_{f2} x_{2i} + a_{f3} x_{3i} + cdots + a_{fm} x_{mi} + e_{fi} (2 where Z is the measured variable, a is the factor loading, f is the factor score, e is the residual term accounting for errors or other source of variation, i is the sample number and m is the total number of factors.
In FA, the basic concept is expressed as in Eq. (6): z_{ji} = a_{f1} f_{1i} + a_{f2} f_{2i} + a_{f3} f_{3i} + cdots + a_{text{fm}} f_{text{mi}} + e_{text{fi}}, (6 where z is the measured value of a variable, a the factor loading, f the factor score, e the residual term accounting for errors or other sources of variation, i the sample number, j the variable number and m the total number of factors.
The FA can be expressed as: z ji = a f 1 f 1 i + a f 2 f 2 i + a f 3 f 3 i + … + a fm f mi + e fi (1 where z is the component score, a is the component loading, f is the factor score, e is the residual term accounting for errors or other source of variation, i is the sample number and m is the total number of variables.
SCM measures the capacitance variation with a small voltage variation, i.e., dC/dV.
The residual variation, i.e. the within subject variation, was allowed to vary by group.
It includes the evaluation of movement variation (i.e. movement repertoire size), a parameter reflecting the integrity of cortical connectivity.
Due to between-litter variation (i.e., litter effects), the statistical assumption of independent observations is untenable.
