Question
In: Statistics and Probability
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 | −0.40 | −2.20 | |||
| (0.03 | ) | (0.01 | ) | ||
| x1 | 0.32 | 0.98 | |||
| (0.04 | ) | (0.06 | ) | ||
| x2 | −0.04 | −0.20 | |||
| (0.01 | ) | (0.01 | ) | ||
a. At the 5% significance level, comment on the
significance of the variables for both models.
| Variable | LPM | Logit |
| x1 | (Click to select) Significant Not significant | (Click to select) Significant Not significant |
| x2 | (Click to select) Significant Not significant | (Click to select) Significant Not significant |
b. What is the predicted probability implied by
the linear probability model for x1 = 4 with
x2 equal to 10 and 20? (Round
intermediate calculations to at least 4 decimal places and final
answers to 2 decimal places.)
| yˆy^ | |
| x1 = 4, x2 = 10 | |
| x1 = 4, x2 = 20 | |
c. What is the predicted probability implied by
the logit model for x1 = 4 with
x2 equal to 10 and 20? (Round
intermediate calculations to at least 4 decimal places and final
answers to 2 decimal places.)
| yˆy^ | |
| x1 = 4, x2 = 10 | |
| x1 = 4, x2 = 20 | |
Solutions
orchestra answered 1 year agoRelated Solutions
Consider a binary response variable y and an explanatory variable x. The following table contains the...
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
−0.78
−5.90
(0.03
)
(0.03
)
x
0.04
0.28
(0.07
)
(0.02
)
a. Test for the significance of the intercept and
the slope coefficients at the 5% level in both models.
Coefficients
LPM
Logit
Intercept
Slope
b. What is the...
Consider a binary response variable y and an explanatory variable x. The following table contains the...
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
−0.70
−6.60
(0.03
)
(0.03
)
x
0.04
0.18
(0.04
)
(0.03
)
a. Test for the significance of the intercept and
the slope coefficients at the 5% level in both models.
coefficient
LPM
Logit
intercept
slope
b. What is the...
17.3 Consider a binary response variable y and an explanatory variable x. The following table contains...
17.3
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
(0.04)
(0.06)
x
0.05
0.15
(0.07)
(0.06)
a.
Test for the significance of the intercept and the slope
coefficients at a 5% level in both models.
Coefficients
LPM
Logit...
Consider the following data for a dependent variable y and two independent variables, x2 and x1....
Consider the following data for a dependent variable y and two
independent variables, x2 and x1.
x1 x2 y
30 12 94
46 10 109
24 17 113
50 17 179
41 5 94
51 19 175
75 8 171
36 12 118
59 13 143
77 17 212
Round your all answers to two decimal places. Enter negative
values as negative numbers, if necessary.
a. Develop an estimated regression equation relating y to x1 .
Predict y if x1=45....
Consider the following data for a dependent variable y and two independent variables, x1 and x2.
You may need to use the appropriate technology to answer this question.
Consider the following data for a dependent variable y and two independent variables,
x1 and x2.
x1
x2
y
30
12
93
47
10
108
25
17
112
51
16
178
40
5
94
51
19
175
74
7
170
36
12
117
59
13
142
76
16
211
The estimated regression equation for these data is
ŷ = −18.89 + 2.02x1 + 4.74x2.
Here, SST = 15,276.0,...
Consider the following data for a dependent variable y and two independent variables, x1 and x2....
Consider the following data for a dependent variable y
and two independent variables, x1 and
x2.
x1
x2
y
30
12
94
47
10
108
25
17
112
51
16
178
40
5
94
51
19
175
74
7
170
36
12
117
59
13
142
76
16
209
The estimated regression equation for these data is ŷ =
−17.02 + 1.99x1 +
4.70x2.
Here, SST = 14,902.9, SSR = 13,773.1,
sb1 =
0.2470, and
sb2 =
0.9480.
(1a) Test...
Consider the following data for a dependent variable y and two independent variables, x1 and x2....
Consider the following data for a dependent variable y
and two independent variables, x1 and
x2.
x1
x2
y
30
13
95
46
10
108
25
18
113
50
16
179
40
5
95
51
20
176
74
7
170
36
12
117
59
13
142
77
16
211
Round your all answers to two decimal places. Enter negative
values as negative numbers, if necessary.
a. Develop an estimated regression equation
relating y to x1.
ŷ =_________ +___________ x1
Predict...
Ten observations on the response variable y associated with two regressor variables x1 and x2 are...
Ten observations on the response variable y associated with two
regressor variables x1 and x2 are given in the following table. The
model fitted to these observations is yi = β 0 + β 1x1i + β 2x2i +
εi , i = 1, …, n , (3) where ε’s are identically and independently
distributed as a normal random variable with mean zero and a known
variance σ ^2 = 3. Observation # y x1 x2 1 7 9 1...
Using Y as the dependent variable and X1, X2, X3, X4 and X5 as the explanatory...
Using Y as the dependent variable and X1, X2, X3, X4 and X5 as
the explanatory variables, formulate an econometric model for data
that is (i) time series data (ii) cross-sectional data and (iii)
panel data – (Hint: please specify the specific model here not its
general form).
For the table below, if Y is the dependent variable and X1 and X2 are the...
For the table below, if Y is the dependent variable and X1 and
X2 are the independent variables. Using the linear regression
equation Y=-0.45X1-1.34X2+15.67, which observation
has the largest absolute residual?
Observation number
Actual Y
x1
X2
1
4.5
6.8
6.1
2
3.7
8.5
5.1
3
5
9
5
4
5.1
6.9
5.4
5
7
8
4
6
5.7
8.4
5.4
The first observation
The third observation
The fifth observation
The second observation
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