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…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

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3. A Ross MAP team is trying to estimate the revenues of major-league baseball teams during the regular season using a regression model. Currently, the independent variables include stadium capacity, the number of weekend games, the number of night games, and the number of Wins (out of 162 regular season games). One of your team members suggests that the model also should include the number of losses as it provides additional explanatory power. Assume that ties are not possible; so every game results in exactly one team winning and the other team losing. Which of the following statements is the most likely conclusion of the new regression model? (a) R2 will increase, adjusted R2 will decrease, and Serror will decrease. (b) R2 and adjusted R2 will increase, and serror will decrease. (c) R, adjusted R2, and Serror will increase. (d) We cannot trust the regression output as some variables are highly correlated, resulting in multicollinearity. Answer to Question 3:
4. We would like to determine how a man's weight can be used to predict his systolic blood pressure. We measure the weights (in pounds) and the systolic blood pressures of a sample of 17 men. The data are plotted on a scatterplot and it can be seen that a linear relationship is a reasonable assumption. The least squares regression line is calculated to be ŷ = 125.561 + 0.145x. The ANOVA table (with some values missing) is shown below: |Source of Variation | df | Sum of Squares Mean Square F Regression Error Total 26.46 617.82 (a) We would like to test whether a linear relationship exists between a man's weight and his systolic blood pressure. We will conduct an analysis of variance to test Ho: B1 = 0 vs. Ha: B1 + 0 at the 5% level of significance. Enter all missing values in the ANOVA table. Calculate the value of the test statistic, find the P-value of the test and provide a properly worded conclusion. (b) What is the value of the correlation between weight and systolic blood pressure…
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1.
. Interpret your result from part (a) if the assumptions for regression inferences hold. Choose the correct interpretation below. A. Presuming that the variables age and price for Corvettes satisfy the assumptions for regression inferences, the standard error of the estimate provides an estimate for the common population standard deviation, σ, of ages for all Corvettes of any particular price. B. Presuming that the variables age and price for Corvettes satisfy the assumptions for regression inferences, the standard error of the estimate provides an estimate for the slope, β1, of the population regression equation for ages for all Corvettes of any particular price. C. Presuming that the variables age and price for Corvettes satisfy the assumptions for regression inferences, the standard error of the estimate provides an estimate for the common population standard deviation, σ, of prices for all Corvettes of any particular age. D. Presuming that…
An article in Technometrics by S. C. Narula and J. F. Wellington ["Prediction, Linear Regression, and a Minimum Sum of Relative Errors" (Vol. 19, 1977)] presents data on the selling price and annual taxes for 24 houses. The data are shown in the following table. Xis taxes paid. Taxes (Local, School), Price/1000 County)/1000 Sale 25.9 4.9176 29.5 5.0208 27.9 4.5429 25.9 4.5573 29.9 5.0597 29.9 3.8910 30.9 5.8980 28.9 5.6039 35.9 5.8282 31.5 5.3003 31.0 6.2712 30.9 5.9592 (a) Assuming that a simple linear regression model is appropriate, obtain the regression equation applicable and compute for the coefficient of determination. (b) Test the significance of the correlation coefficient. (c) Find the mean selling price given that the taxes paid is 6.111. (d) Construct a 95% Confidence Interval for the mean selling price computed in (C). Use the editor to formot your answer
Find and interpret a 95% confidence interval for how the average weight changes for a one hour increase in sleep per night. Find and interpret a 95% confidence interval for the average weight given 6.5 hours of sleep per night is obtained.
5. Indicate if each of the following statements about the simple linear regression model is true or false and explain why. (a). The simple regression line estimated by the method of Least Squared passes through the sample mean of dependent and independent variable. (b). The slope estimator of the Least Squares regression line is free of measure. (c). The sample covariance between the residuals from the least square regression and the explanatory variable is zero. (d). If the values of x more widely spread out, the estimate of the slope coefficient is less concise. (e). Adjusted R-squared will always increase when an additional independent variable is added into the regression.
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.