1 EconomicsFour hundred driver's license applicants were randomly selected and asked whether they passed their driving test (Pass,1) or failed their test (Pass, 0); data were also located on their gender(Male, 1 if male and 0 if female) and their years of driving experience (Experience,, in years). The following table summarizes the results from several probit, logit and linear probability modelsDependent variable: Pass.Regression ModelRegressorExperienceMaleMale x ExperienceConstantUse the results in column (2) to answer the following questions.is the coefficient on Experience significant at any reasonable level?Probit(1)0.031(0.006)0.716(0.128)Logit LPM(2)(3)0.048 0.006(0.015) (0.004)Probit Logit LPM(4)(5)(6)OA The coefficient on Experience is significant at the 10%, but not at the 5% or 1% significance level.a. The coefficient on Experience is significant at the 1% significance level.OC. The coefficient on Experience is not significant at any reasonable level.OD. The coefficient on Experience is significant at the 5%, but not at the 1% significance level.John has 16 years of driving experience. What is the predicted probability that he will pass the test?-0.335 -0.627(0.164) (0.304)1.0570.773 1.283 2.191(0.223) (0.037) (0.125) (0.243)Probit(7)0.042(0.157)-0.079 -0.176(0.038) (0.259)The predicted probability that John will pass the test is 0.872(Round your response to three decimal places)Katherine is a new driver (zero years of experience). What is the predicted probability that she will pass the test?The predicted probability that Katherine will pass the test is(Round your response to three decimal places)0.901(0.026)-0.013(0.019)0.803(0.201)

Economics Four hundred driver's license applicants were randomly selected and asked whether they passed their driving test (Pass, 1) or failed their test (Pass, 0); data were also located on their gender (Male, 1 if male and0 if female) and their years of driving experience (Experience,, in years). The following table summarizes the results from several probit, logit and linear probability models Dependent variable: Pass. Regression Model Regressor Experience Male Male x Experience Constant Probit (1) 0.031 (0.006) Logit LPM (2) (3) 0.048 0.006 (0.015) (0.004) Probit Logit (4) (5) -0.335 -0.627 (0.164) (0.304) 0.716 1.057 0.773 1.283 2.191 (0.128) (0.223) (0.037) (0.125) (0.243) Probit (7) 0.042 (0.157) -0.176 -0.079 (0.038) (0.259) -0.013 (0.019) 0.901 0.803 (0.026) (0.201) LPM (6)

Economics Four hundred driver's license applicants were randomly selected and asked whether they passed their driving test (Pass, 1) or failed their test (Pass, 0); data were also located on their gender (Male, 1 if male and0 if female) and their years of driving experience (Experience,, in years). The following table summarizes the results from several probit, logit and linear probability models Dependent variable: Pass. Regression Model Regressor Experience Male Male x Experience Constant Probit (1) 0.031 (0.006) Logit LPM (2) (3) 0.048 0.006 (0.015) (0.004) Probit Logit (4) (5) -0.335 -0.627 (0.164) (0.304) 0.716 1.057 0.773 1.283 2.191 (0.128) (0.223) (0.037) (0.125) (0.243) Probit (7) 0.042 (0.157) -0.176 -0.079 (0.038) (0.259) -0.013 (0.019) 0.901 0.803 (0.026) (0.201) LPM (6)

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter4A: Problems In Applying The Linear Regression Model

Section: Chapter Questions

Problem 2E

See similar textbooks

Similar questions

Suppose that the following binary dependent models. The model is based on the driving test of the 400 randomly selected driver’s license applicants. Y=1 if passed the test, or 0 otherwise, and X1 is years of experiences, X2 is the years of educations. Logit: P(Y=1/X) = F(0.563+ 0.040X1+ 0.057X2) What are the probabilities of passing the test for a person with 10 years of experiences and 10 years of educations in each model?
Static Bayesian Games] We consider a games with 2 players. Player 1 has only asingle type; however, Player 2 can have two types I and D, each with probability 50%. Bothplayers have two actions available to them. The game has the following payoffs (P1 is the rowplayer, P2 the column player):
Suppose we use the linear probability model to estimate the effect of household income on the willingness to avail a loan product. Given a regression equation: Willingness = 22.34 -0.04HouseholdIncome, with HouseholdIncome measured in hundreds of pesos. How is -0.04 interpreted?
Determine the share (proportion) of person trips by each of two modes (Private auto and mass transit) using the multinomial logit model and given and following information
The management of a large firm would like to know something about employee morale and the other variables to which it may be related. To study these relationships, each employee in a simple random sample of 27 employees is interviewed and tested to obtain information concerning morale level and the average number of satisfying extra work social contacts experienced during a week. Whether or not the duties the employee usually performs are appropriate to the job title is also determined. The dependent variable Y is measure of morale, the quantitative independent variable, X1 is average number of social contacts, and the following dummy variable coding is used to quantify the qualitative variable: X2 = 1 if yes, X2 = 0 if no. The model including dummy variable and the interaction term was being estimated and a part of computer outputs is below: beta std error t p-value 95% CI for B Lower bound Upper bound Constant 260 134.308…
Exlplain Linear Conditionally Unbiased Estimators and the Gauss–Markov Theorem with its limitations?
A sample of subjects were asked their opinion about refurbishing the subway in New York (support, oppose). For the explanatory variables gender (female, male), religious affiliation (Protestant, Catholic, Jewish), and political party affiliation (Democrat, Republican, Independent), the model for the probability π of supporting legalized abortion, logit(pi) = alpha + betagh + betari + betapj has reported parameter estimates (setting the parameter for the last category of a variable equal to 0.0 and alpha^ = -0.11, beta^g1 = 0.16, beta^g2 = 0.0, beta^r1 = -0.57, beta^r2 = -0.66, beta^r3 =0.0, beta^p1 =0.84, beta^p2 = -1.67, beta^p3 =0.00) . Interpret how the odds of supporting refurbishment depend on gender. Find the estimated probability of supporting refurbishment for (i) male Catholic Republicans and (ii) female Jewish Democrats. If we defined parameters such that the first category of a variable has value 0, then what would beta^g2 equal? Show then how to obtain the odds ratio that…
What is sampling? Explain the differences between probability and nonprobability samples and identify the various typesof each
With Panel Data, if we assume that the individual effects vi are not correlated with the regressors Xit (i.e. E(vi|Xit) = 0), which one of the following statements is correct: The Fixed Effects estimator is not consistent. Both the OLS and the Random Effects estimators are not consistent. The OLS estimator is not consistent, but the Random Effects estimator is consistent. The OLS and the Random Effects estimator are consistent. All of the above. None of the above
Please no written by hand The assumption of normally distributed errors means that... A. errors can be ignored when doing regression modelling. B. the OLS estimators can also be assumed to be normally distributed since they are a linear functions of the errors. C. the OLS estimators can also be assumed to be normally distributed since they are BLUE. D. the OLS estimators can also be assumed to be normally distributed since they are minimum variance. E. the regression model will not be subject to specification error.
(Econmetrics) Q.1 How can you test for general misspecification of model if it would have only (any of) two independent variables?
Which of the following is true of heteroskedasticity? a) The R-squared statistic is affected by the presence of heteroskedasticity b) Heteroskedasticty causes inconsistency in the Ordinary Least Squares estimators c) The OLS estimators are not the best linear unbiased estimators if heteroskedasticity is present d) It is not possible to obtain F statistics that are robust to heteroskedasticity of an unknown form