sta108_handout4

sta108_handout4 - Hc-^d*"t + Dia,gnostios J t a txi: i...

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Hc-^d*"t + J ta N(0, o2). npependent Dia,gnostios txi: Model: Y : go * fuXa4i': 1, .'., n, where €1, '.',€n &T€ i Possible departures from the modei 1. Regression function is not linear 2. The errors (i.e., e1 's) do not have a conslant variance' 3. The errore term are not independent. 4' The model fits all but one or more outlying observatiops' 5. The error terms are not normally distributecl. 6. One or several important predictor variables are urissipE' Diagnostics: graphical methods fFor graphical methods, one may use the resid.uals: et : Y-Y or semistudentized residuals ei ffi) 1. PIot of residuals against predictor variables (useful for examining departures 1, 2 and 4). 2. plot of absolute or squared predictor variables (usefui departure 2). 3. Plot of resid.uais fittecl values 4. Plot of residuals time or other sequence for examining departure 3). 5. Plot of residuals against omitted predictor variabies ($sefut for examining departure 6). 6. Boxplot or stem-leaf plot or histogram of re,siduals, nfrmai probability plot of residuals (usefui for examining departure 5).
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This note was uploaded on 09/28/2011 for the course STA STA 108 taught by Professor Jiang during the Summer '09 term at UC Davis.

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sta108_handout4 - Hc-^d*"t + Dia,gnostios J t a txi: i...

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