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This chaptertakesthe conceptoffitting a straightline and extends to (d) fitting a it polynomialand (D)fitting a variablethat is a linear function of two or more independent variables will then show how suchapplications be generalized applied a We can and to Finally, we will illustratehow optimizationtechniques be broadergroup of problerns can usedto implementnonlinearregression Specilicobjectives and topicscoveredare o o o o Knowing how to implementpolynomial regression Knowing how to implementmultiple linear regression Understanding formulation of the generallinear least-squares the model Understanding how the generallinear least-squares model can be solvedwith MATLAB usingeitherthe normalequations left division or Understanding how to implementnonlinearregression with optimization techniques

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inescapable, the patient trembles, struggles to escape, and displays the full picture of terror The patient s affect, emotional reaction, and visceral and somatic motor responses are altogether appropriate to the content of the hallucinations We have seen a patient slash his wrists and another try to drown himself in response to hallucinatory voices that admonished them for their worthlessness and the shame they had brought on their families But the abnormality in these circumstances is one of disordered perception and thinking, and we have no reason to believe that there is a derangement of the mechanisms for emotional expression There also occurs a state dif cult to classify of overwhelming emotionality in patients who are in severe acute pain The patient s attention can be captured only brie y, but within moments there is a return to an extreme state of angst, groaning, and anger We have encountered this with spinal subdural hemorrhage, subarachnoid hemorrhage, explosive migraine, trauma with multiple fractures, and intense pelvic, renal, or abdominal pain, all understandable as responses to extralimbic stimuli

In Chap13, a procedurewas developedto derive the equationof a straightline using the least-squares criterion Some data, although exhibiting a marked pattern such as seen in FiglrtfispoorlyrepresentedbyastraightlineForthesecases,acurvewouldbebetter suited to fit the dataAs discussed Chap 13, one method to acconrplish in this objective is to use transformations Another alternativeis to fit polynomialsto the datausingpolynotnial regression procedurecan be readily extendedto fit the data to a higher-order The least-squares polynomial For example,suppose polynomial or quadratic: that we fit a second-order ,y:uotalr+ a z r :+ e (141)

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Table 25-2 Causes of pseudobulbar affective display Bilateral strokes (lacunes in the cerebral hemispheres or pons most often, and after several strokes in succession) Binswanger diffuse leukoencephalopathy (Chap 34) Amyotrophic lateral scerlosis with pseudobulbar palsy Progressive supranuclear palsy Multiple sclerosis with bilateral corticobulbar demyelinative lesions Bilateral traumatic lesions of the hemispheres Hypoxic-ischemic encephalopathy Pontine myelinolysis Wilson disease

I4 FIGURE I (b i h regression)l n d i c o i i otn o io p o r o b o l os for leost-squores (o)Doto is ill-suited lineor ihot preferoble

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