Please answer c, d,e 12:46Styles6. The following two regression models are Probit and Logit respectively:Pr(Y 11X)(B+B₁ × X₁ + B₂ × X)Pr(Y-11X)-F(B+B₁ × X₁ + B₂ × X₂)(a) what functions do and F represent?(b) How do Probit and Logit ensure that the predicted probabilities are always between 0 and 1?(c) Sketch a graph of the Y-(Z) function. (Z on the horizontal axis, Y on the vertical axis)(d) What estimation method is used to estimate the coefficients in a Probit/Logit model?(e) What are the two measures of fit for models with binary dependent variables?FocusXRA B

Given function Pr(Y=1|X)=ϕ(β0+β1X1+β2X2)---Probit modelPr(Y=1|X)=F(β0+β1X1+β2X2)---Logit model

6. The following two regression models are Probit and Logit respectively: P(Y-112)-(B+B₁ × X₁ +₂× X₂) P(Y-11)-F(B+B xX₁ + ₂ × X₂) (a) what functions do and Frepresent? (b) How do Probit and Logit ensure that the predicted probabilities are always between 0 and 17 (c) Sketch a graph of the Y-(Z) function. (Z on the horizontal axis, Y on the vertical axis) (4) What estimation method is used t stimate the coefficients in a Probit Logit model? (e) What are the two measures of fit for models with binary dependent variables?

6. The following two regression models are Probit and Logit respectively: P(Y-112)-(B+B₁ × X₁ +₂× X₂) P(Y-11)-F(B+B xX₁ + ₂ × X₂) (a) what functions do and Frepresent? (b) How do Probit and Logit ensure that the predicted probabilities are always between 0 and 17 (c) Sketch a graph of the Y-(Z) function. (Z on the horizontal axis, Y on the vertical axis) (4) What estimation method is used t stimate the coefficients in a Probit Logit model? (e) What are the two measures of fit for models with binary dependent variables?

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

Chapter4: Estimating Demand

Section: Chapter Questions

Problem 4E

See similar textbooks

Similar questions

The forecasting staff for the Prizer Corporation has developed a model to predict sales of its air-cushioned-ride snowmobiles. The model specifies that sales S vary jointly with disposable personal income Y and the population between ages 15 and 40, Z, and inversely with the price of the snowmobiles P. Based on past data, the best estimate of this relationship is S=kYZP where k has been estimated (with past data) to equal 100. If Y=11,000,Z=1,200, and P=20,000, what value would you predict for S? What happens if P is reduced to $17,500? How would you go about developing a value for k? What are the potential weaknesses of this model?
A multiple regression model, K = a + bX + cY + dZ, is estimated regression software, which produces the following output: a. Are the estimates of a, b, c, and d statistically significant at the 1 percent significance level? b. How much of the total variation is explained by this regression equation? c. Is the overall regression equation statistically significant at the 1 percent level of significance? d. If X equals 50, Y equals 200, and Z equals 45, what value do you predict K will take?
Given the estimated multiple regression equation ŷ = 6 + 5x1 + 4x2 + 7x3 + 8x4 what is the predicted value of Y in each case? a. x1 = 10, x2 = 23, x3 = 9, and x4 = 12 b. x1 = 23, x2 = 18, x3 = 10, and x4 = 11 c. x1 = 10, x2 = 23, x3 = 9, and x4 = 12 d. x1 = -10, x2 = 13, x3 = -8, and x4 = -16
Consider the simple regression model: y=0.56+1.56x+u Using this and assuming the estimated Var(y)=0.64 and the estimated Var(x)=3.07, what is the estimated Var(x+y)?
Determine the mode choice (personal vehicle or bus system) for the following regression model: Utility Function: Umode – (8.333 x 10-4)*(Access time in sec) – (6.667 x 10-4)*(Wait Time in sec) – (5.00 x 10-4)*(Riding time in sec) – (1.40)*(Cost, $) PARAMETER PERSONAL VEHICLE CITY BUS SYSTEM MODE CONSTANT -0.01 -0.07 ACCESS TIME (SECS) 300 600 WAITING TIME (SECS) 0 900 RIDING TIME (SECS) 1,500 6,000 COST (DOLLARS) $1.50 $1.00
True or False For a linear regression model including only an intercept, the OLS estimator of that intercept is equal to the sample mean of the independent variable.
Question 15 When the R2 of a regression equation is very high, it indicates that all the coefficients are statistically significant. the intercept term has no economic meaning. a high proportion of the variation in the dependent variable can be accounted for by the variation in the independent variables. there is a good chance of serial correlation and so the equation must be discarded.
a)What is multicollinearity?Discuss causes and consequences of multicollinearity for OLS estimation. Suggest possible remedial measures.b)Suppose you are estimating parameters of the following regression model: Ŷt= 9941 + 0.25 X2t+ 15125 X3t (6114) (0.121) (7349) R2= 0.87, RSS = 10310 (The figures in parentheses are the estimated standard errors. RSS are residual sum of squares.) (i) Comment on the explanatory power of the regression. (ii)Using t-tests show whether individual coefficients are significantly different from zero at 5% level of significance. (iii)Test whether the coefficient of X2issignificantly different from 1 at 5% level of significance .(iv)Carry out an appropriate test to check ifcoefficients are jointly significant.
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.
Given the regression equation Y = 100 + 10X a. What is the change in Y when X changes by +3? b. What is the change in Y when X changes by -4? c. What is the predicted value of Y when X = 12? d. What is the predicted value of Y when X = 23? e. Does this equation prove that a change in X causes a change in Y?
In general, what is true about the relationship between the Sum of Squared Residuals in the restricted and unrestricted model? a. SSRr = R-squared * SSRur b. SSRr < SSRur c. SSRr > SSRur d. SSRr = SSRur
what are the key features , Strength and limitation of following model? and when which model should be used? Ordinary Least Squares Logit regression model Probit regression model