QUESTION 1 Q01. A wildlife analyst gathered the data in the following table to develop a logistic regression model to predict the gender of bears. He used GENDER as the dependent variable and WEIGHT as the independent variable. For GENDER, he used male=0 and female=1. WEIGHT CHEST LENGTH GENDER 3461'. 45.0 67.5 0 416 54,0 720 0 220 41.0 700 1 351] 49.0 685 0 332 44.0 73.0 0 M] 320 63.0 1 436 48.0 3'20 0 132 33.0 61.0 1 35 48.0 640 1 13] 35.0 59.0 0 202 me 63.0 1 365 5110 705 0 Estimate the logistic regression model. Based on the computer output, at 5% level of signicance we can conclude that in the population WEIGHT is a signicant predictor of GENDER. 0 True 0 False QUESTION 2 Q02. Based on raw data provided in Question '1. the absolute difference in mean weight between male and female cases is . O D 0 133.3, with the difference favoring females 0 133.3, with the difference favoring males 0 cannot calculate (there is insufcient information) QUESTION 3 Q03. Based on raw data provided in Question '1. without controlling for the effect of anyr other variable the odds of being a female (as opposed to being a male) are . O 1.400 O 0.714 O 0.336 O 0.093 QUESTION 4 Q04. Based on raw data provided in Question \"I, without controlling for the effect of an).r other variable the odds of being a male {as opposed to being a female} are . O 0.714 O 0.093 O 1.400 O 0.336 QUESTION 5 Q05. Based on your logistic regression output from Question 1, at 10% level of significance we can conclude that in the population WEIGHT is a significant predictor of GENDER. O True O False QUESTION 6 Q06. Estimate a logistic regression model that predicts GENDER using the remaining three variables as predictors. Your output should look similar to this: Logistic regression Number of obs 12 LR chi2 (3) 5.44 Prob > chi2 0. 1423 Log likelihood = -5. 4301717 Pseudo R2 0.3337 Gender Coef. Std. Err. Z P>1ZI [954 Conf. Interval] Weight -. 030509 - 0320401 -0.95 0. 341 -. 0933064 . 0322884 Chest . 2750884 . 463454 0.59 0.553 -. 6332647 1. 183441 Length - . 0504996 . 2553555 -0.20 0. 843 - .5509871 . 449988 cons -. 3031474 17.92756 -0. 02 0. 987 -35. 44052 34. 83422 This output suggests that all three independent variables are significant predictors of gender (use 5% level of significance). O True O False O cannot tell (there is insufficient information)QUESTION 7 Q07. Estimate a logistic regression model that predicts GENDER using the remaining three variables as predictors. Your output should look similar to this: Logistic regression Number of obs 12 LR chi2 (3) 5.44 Prob > chi2 0.1423 Log likelihood = -5. 4301717 Pseudo R2 0.3337 Gender Coef. Std. Err. Z P>|z1 (954 Conf. Interval] Weight -. 030509 . 0320401 -0.95 0.341 -. 0933064 . 0322884 Chest . 2750884 . 463454 0.59 0.553 -. 6332647 1.183441 Length -. 0504996 .2553555 -0.20 0. 843 - . 5509871 . 449988 cons -. 3031474 17.92756 -0. 02 0. 987 -35. 44052 34. 83422 This output suggests that all three independent variables are significant predictors of gender (use 10% level of significance). O True O False O cannot tell (there is insufficient information) QUESTION 8 Q08. Create a new variable called Weight2 that takes a value of 0 (Low) if WEIGHT