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Unformatted text preview: Stat 665 (Spring 2011) Kaizar Solutions 6 Exercises 40 points total 1. (8 points) Problem 4.8 (a) From the R output: Coefficients: Estimate Std. Error z value Pr(>z) (Intercept)3.6947 0.88024.198 2.70e05 *** wtkg 1.8151 0.3767 4.819 1.45e06 *** The prediction equation is: logit ( ) = 3 . 69 + 1 . 82 X where X is the weight of the crab in kg. (b) Substituting X=1.2, 2.44, and 5.2 into the above prediction equation, and then taking the inverse of the logit (using the predict function in R) gives probabilities of 0.180, 0.676, and 0.997, respectively. (c) When = 0, logit ( ) = 0, which occurs when 1 . 82 X = 3 . 69, or when the weight = 3.69/1.82 = 2.04kg. (d) The linear approximation of the slope in probability is (1 ) = 1 . 8151 * . 25 = . 454. That is, for every increase in one kg weight (near 2.04kg), the probability of having a satellite increases by about 0.45. To convert to an increase in 0.1kg, I need to convert the meaning of . If is the increase in log odds for every one kg increase in weight, then 0 . 1 is the increase in log odds for every 0.1 kg increase in weight. Thus, the estimated increase in probability of having a satellite for every increase in 0.1 kg is 0.1*0.454 = 0.045. Similarly, the extimated increase in probability of having a satellite for every increase in one SD of weight is 0.58*0.454= 0.263. (e) A 95% CI for is 1 . 96 * . 38: (1.076834, 2.553455). Exponentiating gives a 95% CI for the increase in odds of a satellite for every one kg increase in weight: (2.94 12.85). (f) From the output above, we can see that the Wald pvalue is 1.45e06. Thus, I reject the null hypothesis that the coefficient on weight is zero. That is, I conclude that weight does have a significant association with the odds of having a satellite. To find the LRT statistic, we subtract the residual and null deviances: Null deviance: 225.76 on 172 degrees of freedom Residual deviance: 195.74 on 171 degrees of freedom 1 LRT = 225.76  195.74 = 30.02. I compare this to a 2 1 distribution to get the pvalue = 4.273103e08. Thus, I reject the null hypothesis that the coefficient on weight is zero. That is, I conclude that weight does have a significant association with the odds of having a satellite. 2. (6 points) Exercise 4.9, parts ad (a) Output from R: Estimate Std. Error z value Pr(>z) (Intercept) 1.0986 0.6667 1.648 0.0994 . colorfactor20.1226 0.70530.174 0.8620 colorfactor30.7309 0.73380.996 0.3192 colorfactor41.8608 0.80872.301 0.0214 * Looking at Table 3.2, I can interpret the meanings of each of the colors: 1=light medium (LM), 2=medium (M), 3=dark medium (DM), 4=dark (D)....
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This note was uploaded on 07/26/2011 for the course STA 665 taught by Professor Staff during the Spring '10 term at Ohio State.
 Spring '10
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