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AS.440.606.80.SP25 M07: Assignment
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Read Example 7.9 on page 233 carefully. Consider the following sample regression function:ย log โก ( ๐ค ๐ ๐ ๐ ) ^ = ๐ฝ ^ 0 + .2 ๐ ๐ ๐ ๐ ๐ค ๐ ๐ ๐ + .05 ๐ ๐ ๐ ๐ โ ๐ ๐ ๐ + .03 ๐ ๐ ๐ ๐ ๐ค ๐ ๐ ๐ โ ๐ ๐ ๐ ๐ โ ๐ ๐ ๐ + ๐ ๐ก โ ๐ ๐ ๐ ๐ ๐ ๐ก ๐ ๐ ๐ ( .01 ) ( .02 ) ( .005 ) where the variables wage, compwork,ย and comphome are defined respectively as hourly wage, = 1 if an individual uses a computer at work, and = 1 if an individual uses a computer at home. Which of the following statements is correct? ย
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A.The estimates imply that the average wage of a person who uses a computer at work (but not at home) is about 20% above the average wage of a person who doesnโt use a computer at all, holding all the other factors constant.
B.The estimates imply that the average wage of a person who uses a computer at home (but not at work) is about 5% above the average wage of a person who doesnโt use a computer at all, holding all the other factors constant.
C.The estimates imply that the average wage of a person who uses a computer at home and at work is about 28% above the average wage of a person who doesnโt use a computer at all, holding all the other factors constant.
D.All of the above.
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The question presents a log-linear regression: log(wage) = ฮฒ0 + 0.20ยทcompwork + 0.05ยทcomphome + 0.03ยทcompworkยทcomphome + other terms. Here, compwork = 1 if the person uses a computer at work (0 otherwise) and comphome = 1 if the person uses a computer at home (0 otherwise).
Option 1: The estimates imply that the average wage of a person who uses a computer at work (but not at home) is about 20% above the average wage of a person who doesnโt use a computer at all, holding all the other factors constant.
- When compwork = 1 and comphome = 0, the predicted change in log(wage) is +0.20. To translate a log wage change into a percentage change in wage, we use exp(ฮlog wage) โ 1. Here, exp(0.20) โ 1 โ 0.221,......Login to view full explanation็ปๅฝๅณๅฏๆฅ็ๅฎๆด็ญๆก
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Consider the following estimated regression model [math: lnโก(yi)^=0.034โ0.023xi]\widehat {\ln (y_i)}=0.034-0.023x_i where [math: lnโก(.)]\ln (.) denotes the natural log. Which of the following is a correct interpretation?[Fill in the blank]
The following data were collected for two related variables x and y. A scatterplot indicates a non-linear association. The data is linearised using a logx transformation and a least squares line is then fitted. The equation of this line isclosest to:
An exponential model with a quarterly data forecasting equation isย log( ห Y i)=2.0886+0.0016Xiโ0.0528Q1โ0.0389Q2โ0.0449Q3 Where Xiย represents the coded quarter, Qi is a dummy variable for quarters, Qi=1 if it is ith quarter. What is the prediction when Xi=27, and it is the fourth quarter?
The following data were collected for two related variables x and y. A scatterplot indicates a non-linear association. The data is linearised using a logx transformation and a least squares line is then fitted. The equation of this line isclosest to:
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