Consider a binary response variable y and two explanatory variables x1 and x2. The following table contains the parameter estimatesof the linear probability model (LPM) and the logit model, with the associated p-values shown in parentheses.Logit-1.50(0.03)0.86(0.04)-0.20(0.03)LPM-0.30VariableConstant0.020.35(0.03)-0.03(0.06)X1X2a. At the 5% significance level, comment on the significance of the variables for both models.LogitSignificantSignificantVariableLPMSignificantSignificantx2b. What is the predicted probability implied by the linear probability model for x = 5 with x) equal to 19 and 22? (Round intermediatecalculations to at least 4 decimal places and final answers to 2 decimal places.)X1 = 5, x = 19X1 = 5, x5 = 22c. What is the predicted probability implied by the logit model for x = 5 with x2 equal to 19 and 22? (Round intermediate calculationsto at least 4 decimal places and final answers to 2 decimal places.)5, X2= 19.X15, X2 = 22

P-values and coefficients of a model tell you which relationships in your model are statistically…

Consider a binary response variable y and two explanatory variables x1 and x2. The following table contains the parameter estimates of the linear probability model (LPM) and the logit model, with the associated p-values shown in parentheses. Variable LPM Logit -1.50 Constant -0.30 0.02 (0.03) X1 0.35 0.86 (0.03) -0.03 (0.06) (0.04) X2 -0.20 (0.03) a. At the 5% significance level, comment on the significance of the variables for both models. Variable LPM Significant Significant Logit Significant Significant X1 X2 b. What is the predicted probability implied by the linear probability model for x = 5 with x2 equal to 19 and 22? (Round intermediate calculations to at least 4 decimal places and final answers to 2 decimal places.) X1 = 5, x2 - 19 X1 - 5, x2 = 22 c. What is the predicted probability implied by the logit model for x = 5 with x2 equal to 19 and 22? (Round intermediate calculations to at least 4 decimal places and final answers to 2 decimal places.) X1- 5, x2 - 19 X1 5, x2 = 22

Consider a binary response variable y and two explanatory variables x1 and x2. The following table contains the parameter estimates of the linear probability model (LPM) and the logit model, with the associated p-values shown in parentheses. Variable LPM Logit -1.50 Constant -0.30 0.02 (0.03) X1 0.35 0.86 (0.03) -0.03 (0.06) (0.04) X2 -0.20 (0.03) a. At the 5% significance level, comment on the significance of the variables for both models. Variable LPM Significant Significant Logit Significant Significant X1 X2 b. What is the predicted probability implied by the linear probability model for x = 5 with x2 equal to 19 and 22? (Round intermediate calculations to at least 4 decimal places and final answers to 2 decimal places.) X1 = 5, x2 - 19 X1 - 5, x2 = 22 c. What is the predicted probability implied by the logit model for x = 5 with x2 equal to 19 and 22? (Round intermediate calculations to at least 4 decimal places and final answers to 2 decimal places.) X1- 5, x2 - 19 X1 5, x2 = 22

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter10: Statistics

Section10.1: Measures Of Center

Problem 9PPS

See similar textbooks

Similar questions

A pumping station operator observes that the demand for water at a certain hour of the day can be modeled as an exponential random variable with a mean of 100 ft^3 / s. a.) What is the maximum water-producing capacity that the station should keep on line for this hour so that the demand will exceed this production capacity with a probability of only 0.01?
Stock y has a beta of 1.2 and an expected return of 11.5. Stock z has a beta of .80 and an expected return of 8.5 percent
Suppose model (XY, XZ, YZ) holds in a 2 x 2 x 2 table, and the common XY conditional log odds ratio at the two levels of Z is positive If the XY and YZ conditional log odds ratios are both positive or both negative, show that the XY marginal odds ratio is larger than the XY conditional odds ratio.
Consider a simple logistic model in which the response variable is attendance at a conference (yes = 1; no = 0), and the sole independent variable is a college degree (degree = 1; no degree = 0). The estimated odds ratio for the college degree variable is 1.25. What is its proper interpretation?
Given is the PDF of random variable X, where c = 1 and fx(x) = x^2 0<=x<=c. Determine the: 1. value of d to make the PDF valid 2. expected value of X 3. variance of X
Suppose that the relevant equilibrium model is CAPM with unlimited borrowing and lending at a riskless rate of interest. Compute the missing values (a*, b*, c*, d*, e*) in the following table, showing all intermediate steps. Expected Standard Residual Asset Return Deviation Beta Variance A 0.08 0.1 a* 0 B 0.12 b* 2 0.49 C c* d* 1 0 D 0.05 e* 0 0.36
Derive the following; show complete solutions. with parameter (r, λ) a. MGF of Gamma Distribution b. Expectation of Gamma Distribution c. Variance of Gamma Distribution
If X is Poisson random variable with parameter λ, compute E[1/(X + 1)]
Stock A has a beta of 1.2 and Stock B has a beta of 1. The returns of Stock A are ______ sensitive to changes in the market as the returns of Stock B.
Researchers interested in lead exposure due to car exhaust sampled the blood of 52 police officers subjected to constant inhalation of automobile exhaust fumes while working traffic enforcement in a primarily urban environment. The blood samples of these officers had an average lead concentration of 124.32 µg/l and a SD of 37.74 µg/l; a previous study of individuals from a nearby suburb, with no history of exposure, found an average blood level concentration of 35 µg/l. Test the hypothesis that the downtown police officers have a higher lead exposure than the group in the previous study. Interpret your results in context. Based on your preceding result, without performing a calculation, would a 99% confidence interval for the average blood concentration level of police officers contain 35 µg/l? Based on your preceding result, without performing a calculation, would a 99% confidence interval for this difference contain 0? Explain why or why not.
Let X be a Poisson random variable with E(X) = 3. Find P(2 < x < 4).
If X is a uniformly distributed random varibale with a=9 and b=16, then Calculate the variance of X? Round to three decimal places