4.1. In a 2x2 table (say categorical X=0 or 1 and categorical response Y=0 or 1). If X and Y are independent, the the odds ratio (for Y) comparing X=1 to X=0 will : a. Be greater than 1. b. Be less...


4.1. In a 2x2 table (say categorical X=0 or 1 and categorical response Y=0 or 1). If X and Y are<br>independent, the the odds ratio (for Y) comparing X=1 to X=0 will :<br>a. Be greater than 1.<br>b. Be less than 1.<br>c. Be equal to 1.<br>d. Be equal to 0.<br>4.2. Given a GLM, the saturated model will have residual deviance equal to:<br>a. 1.<br>b. 0.<br>c. The null deviance.<br>d. 0<br>V<br>4.3. If overdispersion is present in a generalized linear model, then the variance estimates from the<br>model will be:<br>a. Under estimated.<br>b. Over estimated.<br>c. Unaffected by the over dispersion.<br>d. Estimated to be zero.<br>

Extracted text: 4.1. In a 2x2 table (say categorical X=0 or 1 and categorical response Y=0 or 1). If X and Y are independent, the the odds ratio (for Y) comparing X=1 to X=0 will : a. Be greater than 1. b. Be less than 1. c. Be equal to 1. d. Be equal to 0. 4.2. Given a GLM, the saturated model will have residual deviance equal to: a. 1. b. 0. c. The null deviance. d. 0 V 4.3. If overdispersion is present in a generalized linear model, then the variance estimates from the model will be: a. Under estimated. b. Over estimated. c. Unaffected by the over dispersion. d. Estimated to be zero.

Jun 11, 2022
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