Consider a binary response variable y and two explanatory variables x1 and x2. The following table...
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 | |||
| Constant | −0.60 | −2.50 | |||
| 0.02 | (0.03 | ) | |||
| x1 | 0.28 | 0.99 | |||
| (0.06 | ) | (0.06 | ) | ||
| x2 | −0.06 | −0.30 | |||
| (0.03 | ) | (0.06 | ) | ||
a. At the 5% significance level, comment on the
significance of the variables for both models.
| Variable | LPM | Logit |
| x1 | (Click to select) Not significant Significant | (Click to select) Not significant Significant |
| x2 | (Click to select) Significant Not significant | (Click to select) Not significant Significant |
b. What is the predicted probability implied by
the linear probability model for x1 = 9 with
x2 equal to 16 and 22? (Round
intermediate calculations to at least 4 decimal places and final
answers to 2 decimal places.)
| y^ | |
| x1 = 9, x2 = 16 | |
| x1 = 9, x2 = 22 | |
c. What is the predicted probability implied by
the logit model for x1 = 9 with
x2 equal to 16 and 22? (Round
intermediate calculations to at least 4 decimal places and final
answers to 2 decimal places.)
| y^ | |
| x1 = 9, x2 = 16 | |
| x1 = 9, x2 = 22 | |
Homework Answers
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Consider a binary response variable y and two explanatory
variables x1 and x2. The following table...
Consider a binary response variable y and two explanatory variables xy and x2. The following table contains the parameter estimates of the linear probability model (LPM) and the logit model, wit...
Consider a binary response variable y and two explanatory variables xy 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. Constant .40 -2.30 x1 x2 0.06 (0.03) 0.36 0.90 (0.03)(0.07) -0.03-0.10 (0.02) (0.01) a. At the 5% significance level, comment on the significance of the variables for both models. Logit gnificant 0 (Not significant x1 x2 b. What is the predicted probability implied...
Can someone help me with the work either by hand or using R? Thanks! Consider a...
Can someone help me with the work either by hand or using R?
Thanks!
Consider a binary response variable y and an
explanatory variable x. 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
Constant
−0.69
−6.30
(0.06)
(0.06)
x
0.06
0.21
(0.04)
(0.06)
a. Test for the significance of the intercept and
the slope coefficients at the 5% level in both...
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Consider a binary response variable y and two explanatory variables xy 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. Constant .40 -2.30 x1 x2 0.06 (0.03) 0.36 0.90 (0.03)(0.07) -0.03-0.10 (0.02) (0.01) a. At the 5% significance level, comment on the significance of the variables for both models. Logit gnificant 0 (Not significant x1 x2 b. What is the predicted probability implied...
Can someone help me with the work either by hand or using R?
Thanks!
Consider a binary response variable y and an
explanatory variable x. 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
Constant
−0.69
−6.30
(0.06)
(0.06)
x
0.06
0.21
(0.04)
(0.06)
a. Test for the significance of the intercept and
the slope coefficients at the 5% level in both...
Consider a multiple regression model of the dependent variable y on independent variables x1, X2, X3, and x4: Using data with n 60 observations for each of the variables, a student obtains the following estimated regression equation for the model given: y0.35 0.58x1 + 0.45x2-0.25x3 - 0.10x4 He would like to conduct significance tests for a multiple regression relationship. He uses the F test to determine whether a significant relationship exists between the dependent variable and He uses the t...
4. Testing for significance Aa Aa Consider a multiple regression model of the dependent variable y on independent variables x1, x2, X3, and x4: Using data with n = 60 observations for each of the variables, a student obtains the following estimated regression equation for the model given: 0.04 + 0.28X1 + 0.84X2-0.06x3 + 0.14x4 y She would like to conduct significance tests for a multiple regression relationship. She uses the F test to determine whether a significant relationship exists...
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