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 models.
b. What is the predicted probability implied by
the linear probability model for x = 20 and x =
36? (Round intermediate calculations to at least 4 decimal
places and final answers to 2 decimal places.)
c. What is the predicted probability implied by
the logit model for x = 20 and x = 36?
(Round intermediate calculations to at least 4 decimal
places and final answers to 2 decimal places.)
Homework Answers
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Can someone help me with the work either by hand or using R?
Thanks!
Consider a...
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...
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...
Consider the sample regressions for the linear, the logarithmic, the exponential, and the log-log models. For each of th...
Consider the sample regressions for the linear, the logarithmic,
the exponential, and the log-log models. For each of the estimated
models, predict y when x equals 50. (Do
not round intermediate calculations. Round final answers to 2
decimal places.)
Response Variable: y
Response Variable: ln(y)
Model 1
Model 2
Model 3
Model 4
Intercept
18.52
−6.74
1.48
1.02
x
1.68
NA
0.06
NA
ln(x)
NA
29.96
NA
0.96
se
23.92
19.71
0.12
0.10
Model 1 Model 2 Model 3 Model...
Consider the sample regressions for the linear (Model 1), the logarithmic (Model 2), the exponential (Model...
Consider the sample regressions for the linear (Model 1), the logarithmic (Model 2), the exponential (Model 3), and the log-log (Model 4) models. For each of the estimated models, predict y when x equals 50. (Do not round intermediate calculations. Round your answers to 2 decimal places.) Response Variable: y Response Variable: In(y) Model 18.52 1.68 NA 23.92 Model 2 -6.74 NA 29.96 19.71 Model 3 1.48 0.06 NA 0.12 Model 4 1.02 NA 0.96 0.10 Intercept In(x) 102.52 Model...
Consider the following sample regressions for the linear, the quadratic, and the cubic models along with...
Consider the following sample regressions for the linear, the quadratic, and the cubic models along with their respective R2 and adjusted R2. Intercept х x2 Linear 28.53 0.12 NA NA Quadratic 28.80 0.01 0.01 Cubic 28.62 0.15 -0.02 -0.01 x3 NA R2 Adjusted R2 0.005 -0.021 0.006 -0.048 0.006 -0.077 a. Predict y for x = 2 and 4 with each of the estimated models. (Round intermediate calculations to at least 4 decimal places and final answers to 2 decimal...
In Professor Friedman's economics course the correlation between the students' total scores before the final examination and their final examination scores is r-0.56.
In Professor Friedman's economics course the correlation between the students' total scores before the final examination and their final examination scores is r-0.56. The pre-exam totals for all students in the course have mean 286 and standard deviation 28, The final exam scores have mean 90 and standard deviation 9. Professor Friedman has lost Julie's final exam but knows that her total before the exam was 320, He decides to predict Julie's final exam score from her pre exam total. Question...
Consider the following regression results based on 40 observations. [You may find it useful to reference...
Consider the following regression results based on 40 observations. [You may find it useful to reference the t table.] Standard Error 12.6824 0.9614 Coefficients t Stat 3.257 p-value 0.002 Intercept 1.3096 0.9535 #1 0.992 0.328 a. Specify the hypotheses to determine if the slope differs from minus one. b-1. Calculate the value of the test statistic. (Round intermediate calculations to at least 4 decimal places and final answer to 3 decimal places.) Test statistic
In Professor Friedman's economics course the correlation between the students' total scores before the final examination...
In Professor Friedman's economics course the correlation between
the students' total scores before the final examination and their
final examination scores is r = 0.52. The pre-exam totals
for all students in the course have mean 276 and standard deviation
21. The final exam scores have mean 50 and standard deviation 9.
Professor Friedman has lost Julie's final exam but knows that her
total before the exam was 318. He decides to predict Julie's final
exam score from her pre-exam...
The accompanying data file contains 20 observations fort and yt: Click here for the Excel Data...
The accompanying data file contains 20 observations fort and yt: Click here for the Excel Data File Actual series are plotted along with the superimposed linear and exponential trends. 25 20 15 Y 10 5 Linearly Expon.lv 0 10 15 20 25 b-1. Estimate a linear trend model and an exponential trend model for the sample. (Round your answers to 2 decimal places.) Linear Trend Exponential Trend Variable Intercept t Standard Error b-1. Estimate a linear trend model and an...
Thus Question: 6 pts 2 of 30 (1 complete) This Qu The data below represent the...
Thus Question: 6 pts 2 of 30 (1 complete) This Qu The data below represent the number of days absent, x, and the final grade, y, for a sample of college students at a large university Complete pe below. No. of absences, * 0 1 2 3 4 5 6 7 8 9 Final grade, y 88.1 852 822 796 76.6 72.1 62.5 66.9 63.9 60.9 (a) Find the least squares regression line treating the number of absences, x, as...
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...
Consider the sample regressions for the linear, the logarithmic,
the exponential, and the log-log models. For each of the estimated
models, predict y when x equals 50. (Do
not round intermediate calculations. Round final answers to 2
decimal places.)
Response Variable: y
Response Variable: ln(y)
Model 1
Model 2
Model 3
Model 4
Intercept
18.52
−6.74
1.48
1.02
x
1.68
NA
0.06
NA
ln(x)
NA
29.96
NA
0.96
se
23.92
19.71
0.12
0.10
Model 1 Model 2 Model 3 Model...
Consider the sample regressions for the linear (Model 1), the logarithmic (Model 2), the exponential (Model 3), and the log-log (Model 4) models. For each of the estimated models, predict y when x equals 50. (Do not round intermediate calculations. Round your answers to 2 decimal places.) Response Variable: y Response Variable: In(y) Model 18.52 1.68 NA 23.92 Model 2 -6.74 NA 29.96 19.71 Model 3 1.48 0.06 NA 0.12 Model 4 1.02 NA 0.96 0.10 Intercept In(x) 102.52 Model...
Consider the following sample regressions for the linear, the quadratic, and the cubic models along with their respective R2 and adjusted R2. Intercept х x2 Linear 28.53 0.12 NA NA Quadratic 28.80 0.01 0.01 Cubic 28.62 0.15 -0.02 -0.01 x3 NA R2 Adjusted R2 0.005 -0.021 0.006 -0.048 0.006 -0.077 a. Predict y for x = 2 and 4 with each of the estimated models. (Round intermediate calculations to at least 4 decimal places and final answers to 2 decimal...
In Professor Friedman's economics course the correlation between the students' total scores before the final examination and their final examination scores is r-0.56. The pre-exam totals for all students in the course have mean 286 and standard deviation 28, The final exam scores have mean 90 and standard deviation 9. Professor Friedman has lost Julie's final exam but knows that her total before the exam was 320, He decides to predict Julie's final exam score from her pre exam total. Question...
Consider the following regression results based on 40 observations. [You may find it useful to reference the t table.] Standard Error 12.6824 0.9614 Coefficients t Stat 3.257 p-value 0.002 Intercept 1.3096 0.9535 #1 0.992 0.328 a. Specify the hypotheses to determine if the slope differs from minus one. b-1. Calculate the value of the test statistic. (Round intermediate calculations to at least 4 decimal places and final answer to 3 decimal places.) Test statistic
In Professor Friedman's economics course the correlation between
the students' total scores before the final examination and their
final examination scores is r = 0.52. The pre-exam totals
for all students in the course have mean 276 and standard deviation
21. The final exam scores have mean 50 and standard deviation 9.
Professor Friedman has lost Julie's final exam but knows that her
total before the exam was 318. He decides to predict Julie's final
exam score from her pre-exam...
The accompanying data file contains 20 observations fort and yt: Click here for the Excel Data File Actual series are plotted along with the superimposed linear and exponential trends. 25 20 15 Y 10 5 Linearly Expon.lv 0 10 15 20 25 b-1. Estimate a linear trend model and an exponential trend model for the sample. (Round your answers to 2 decimal places.) Linear Trend Exponential Trend Variable Intercept t Standard Error b-1. Estimate a linear trend model and an...
Thus Question: 6 pts 2 of 30 (1 complete) This Qu The data below represent the number of days absent, x, and the final grade, y, for a sample of college students at a large university Complete pe below. No. of absences, * 0 1 2 3 4 5 6 7 8 9 Final grade, y 88.1 852 822 796 76.6 72.1 62.5 66.9 63.9 60.9 (a) Find the least squares regression line treating the number of absences, x, as...
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