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The Michigan leadership studies, along with the Ohio State University studies that took place in the 1940s, are two of the best-known behavioral leadership studies and continue to be cited to this day. These theories attempt to isolate behaviours that differentiate effective leaders from ineffective leaders.
The Three Levels of Leadership model attempts to combine the strengths of older leadership theories (i.e. traits, behavioral/styles, situational, functional) while addressing their limitations and, at the same time, offering a foundation for leaders wanting to apply the philosophies of servant leadership and "authentic leadership". [2]
Opinion leadership is leadership by an active media user who interprets the meaning of media messages or content for lower-end media users. Typically opinion leaders are held in high esteem by those who accept their opinions. Opinion leadership comes from the theory of two-step flow of communication propounded by Paul Lazarsfeld and Elihu Katz. [1]
Segmented regression, also known as piecewise regression or broken-stick regression, is a method in regression analysis in which the independent variable is partitioned into intervals and a separate line segment is fit to each interval. Segmented regression analysis can also be performed on multivariate data by partitioning the various ...
Functional leadership theory (Hackman & Walton, 1986; McGrath, 1962) is a theory for addressing specific leader behaviors expected to contribute to organizational or unit effectiveness. This theory argues that the leader's main job is to see that whatever is necessary to group needs is taken care of; thus, a leader can be said to have done ...
Flat memory model or linear memory model refers to a memory addressing paradigm in which "memory appears to the program as a single contiguous address space." [ 1 ] The CPU can directly (and linearly ) address all of the available memory locations without having to resort to any sort of bank switching , memory segmentation or paging schemes.
A psychographic segmentation model should be able to accurately predict the segment to which a consumer belongs with an acceptable level of confidence. Often there are trade-offs involved. For instance, a model may attain a higher level of predictability with a greater number of segments, but too many segments become unwieldy and infeasible to ...
However efficient computation and joint estimation of all model parameters (including the breakpoints) may be obtained by an iterative procedure [6] currently implemented in the package segmented [7] for the R language. A variant of decision tree learning called model trees learns piecewise linear functions. [8]