4.3. If overdispersion is present in a generalized linear model, then the variance estimates from themodel will be:a. Under estimated.b. Over estimated.c. Unaffected by the over dispersion.d. Estimated to be zero.4.4. A logistic regression model is fit with the response Y of being cureda disease (1=yesand 0=no) and the explanatory variable X being the dosage level of the treatment (in milligrams,continuous). An approximate 95% confidence interval for e1 (where B1 is coefficient on X) is (1.40,1.65). Which of the following is a correct interpretation of the interval?a. 95% confident that the true odds ratio for comparing X+1 to X (1 unit dose increase to regulardose) is between 1.4 and 1.65.b. 95% probability that the true odds ratio for comparing X+1 to X (1 unit dose increase toregular dose) is between 1.4 and 1.65.c. 95% confident that the estimated odds ratio for comparing X+1 to X (1 unit dose increase toregular dose) is between 1.4 and 1.65.d. 95% confident that the true odds ratio for comparing X+1 to X (1 unit dose increase to regulardose) is between e1.4 and e1.65.4.5. We have a single categorical covariate X with levels 0 or 1 and a single categorical covariate Wwith levels 0 or 1. A model is fit with X and W main effects only (logit(µ;) = Bo + BịX; + B2W;)and the output from R states each estimated coefficient has a p-value z 0.5 for testing if B; = 0 forj=1,2. But the deviance test of B1 = B2 = 0 returns a p-value of 0.0001. Choose the correct choice.a. Both X and W should not be in the model.b. Both X and W should be in the model.c. X or W (but not both) should be in the model.4.6 If overdispersion is not present in these data, the estimated dispersion parameter for thequasipoisson family should be approximately equal to what value?а. 1.b. 0.c. The null deviance.d. 0

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Answered: 4.3. If overdispersion is present in a…

Calculus For The Life Sciences

2nd Edition

ISBN:9780321964038

Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.

Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.

Chapter2: Exponential, Logarithmic, And Trigonometric Functions

Section2.CR: Chapter 2 Review

Problem 111CR: Respiratory Rate Researchers have found that the 95 th percentile the value at which 95% of the data...

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Respiratory Rate Researchers have found that the 95 th percentile the value at which 95% of the data are at or below for respiratory rates in breath per minute during the first 3 years of infancy are given by y=101.82411-0.0125995x+0.00013401x2 for awake infants and y=101.72858-0.0139928x+0.00017646x2 for sleeping infants, where x is the age in months. Source: Pediatrics. a. What is the domain for each function? b. For each respiratory rate, is the rate decreasing or increasing over the first 3 years of life? Hint: Is the graph of the quadratic in the exponent opening upward or downward? Where is the vertex? c. Verify your answer to part b using a graphing calculator. d. For a 1- year-old infant in the 95 th percentile, how much higher is the walking respiratory rate then the sleeping respiratory rate? e. f.
Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
For the following table of data. x 1 2 3 4 5 6 7 8 9 10 y 0 0.5 1 2 2.5 3 3 4 4.5 5 a. draw a scatterplot. b. calculate the correlation coefficient. c. calculate the least squares line and graph it on the scatterplot. d. predict the y value when x is 11.

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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.