Homework Answers
This implies that the number of traffic accidents increases by 0.064 * 100 = 6.4 % every year.
The largest coefficient is for Q3t. So, the highest number of traffic accidents takes place in third quarter of the year.
The smallest coefficient is for Q2t which is negative. So, the lowest number of traffic accidents takes place in second quarter of the year.
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Consider the following equation, estimated by OLS, explaining the quarterly number of traffic accidents, in Portugal,...
The quarterly sales data (number of book sold) for Christian book over the past three years...
The quarterly sales data (number of book sold) for Christian book over the past three years in California follow: (You can use Excel to compute the equation) Quarter Year 1 Year 2 Year 3 1 2 3 4 1230 1020 2534 2600 1470 990 2800 2590 1520 1020 2850 2700 1. Use the following dummy variables to develop an estimated regression equation to account for any seasonal effects in the data: Quarter1=1 if the sales data point is in Quarter...
The quarterly sales data (number of book sold) for Christian book over the past three years...
The quarterly sales data (number of book sold) for Christian book over the past three years in California follow: (You can use Excel to compute the equation) Quarter Year 1 Year 2 Year 3 1 2 3 4 1230 1020 2534 2600 1470 990 2800 2590 1520 1020 2850 2700 1. Use the following dummy variables to develop an estimated regression equation to account for any seasonal effects in the data: Quarter1=1 if the sales data point is in Quarter...
6. eBook The quarterly sales data (number of copies sold) for a college textbook over the past th...
6. eBook The quarterly sales data (number of copies sold) for a college textbook over the past three years follow Quarter Year 1 Year 2 Year 3 1,765 1,063 2,974 2,554 1,591 1,827 935 2,646 2,423 980 2,812 2,358 4 There appears to be a seasonal pattern in the data and perhaps amoderate upward linear trend b. Use the following dummy variables to develop an estimated regression equation to account for any seasonal effects in the data: Qtrl 1 if...
? ANSWER FROM LETTER "E" AND DOWNWARDS III- (15pts) You are given the following economic model...
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ANSWER FROM LETTER "E" AND DOWNWARDS
III- (15pts) You are given the following economic model 0.013 -$26 Rsquare- 0.4177 0.0012ten (0.00024 log(wage) 0.478 + 0.085edu + 0.059ten-0.058/emale-0.01 ledu.female-0.02 1/emale./en- Std errors (0.113) (0.008) (0.007) (0.174) (0.006 With all the variables described as follows: log(wage) -log of average hourly wage; female is a dummy variable equal to 1 if the observed person is a female, and O if make; edu female is an interaction variable equal to education'female; edu is...
4. Consider the following estimated semilog equation: = -8.10+ 0.100ED,+ 0.11 EXP where SAL = salary...
4. Consider the following estimated semilog equation: = -8.10+ 0.100ED,+ 0.11 EXP where SAL = salary of the worker, ED = worker's years of education, and EXP = worker's years of experience. The mean salary is $50,000 and the mean education is 12 years. a. Interpret the estimated coefficients on education and experience. b. Calculate and interpret the slope and elasticity of salary with respect to education. c. Draw the shapes of the relationships between salary vs. education and salary...
III-(15pts) You are given the following estimated equation: log(wage)- 0.18+0.093edu +0.044exp+0.043 female-0.016edu female-0.010exp female-0.00068 exp (0.0001) 0.014) 0.4160 0.003 Std errors (0....
III-(15pts) You are given the following estimated equation: log(wage)- 0.18+0.093edu +0.044exp+0.043 female-0.016edu female-0.010exp female-0.00068 exp (0.0001) 0.014) 0.4160 0.003 Std errors (0.132) (0.009) (0.005) (0.196) n-526 R-square With all the variables described as follows: logiwage)-log of average hourly wage: female is a dummy variable equal to 1 if the observed person is a female, and 0 if male; edu female is an interaction variable equal to education 'female; edu is the number of years of schooling exp is the number...
please be detailed in your response :) thank you! 0 pts) You are given the following estimated equation: In(wage) 0.1279+0.0904educ + 0.041 exper-0 (0.1059) (0.0075) (0.0052) (0.00012) R 0.3003 5...
please be detailed in your response :) thank you!
0 pts) You are given the following estimated equation: In(wage) 0.1279+0.0904educ + 0.041 exper-0 (0.1059) (0.0075) (0.0052) (0.00012) R 0.3003 526 in which: log(wage) log of average hourly wage - educ is the number of years of schooling: - exper is the number of years of experience -exper'=experience"experience The plot of the residuals against the fitted values from the regression above, is provided below: .5 2.5 1.5 Fitted values a. With...
QUESTION 1 Consider the following OLS regression line (or sample regression function): wage =-2.10+ 0.50 educ...
QUESTION 1 Consider the following OLS regression line (or sample regression function): wage =-2.10+ 0.50 educ (1), where wage is hourly wage, measured in dollars, and educ years of formal education. According to (1), a person with no education has a predicted hourly wage of [wagehat] dollars. (NOTE: Write your answer in number format, with 2 decimal places of precision level; do not write your answer as a fraction. Add a leading minus sign symbol, a leading zero and trailing...
Please help. I'm stuck. The quarterly sales data (number of copies sold) for a college textbook...
Please help. I'm stuck.
The quarterly sales data (number of copies sold) for a college textbook over the past three years follow. Quarter Year 1 Year 2 Year 3 1 1,690 1,800 1,850 N 940 900 1,100 3 2,625 2,900 2,930 4 2,500 2,360 2,615 (a) Construct a time series plot. 3500 3000+ 2500+ 2000+ N 1 500+ 1000+ 500+ 0 4 1 4 1 + 2 3 Year 1 2 3 Year 2 2 3 Year 3 O Year/Quarter...
ONLY NEES A,B help please!!! 1II. (10 pts) You are given the following estimated equation In(wage)- 0.3688+0.0852educ + 0.05 teure-0.000994tenue 0.0908) (0.0069) (0.0068) (0.00025) R-0.3294 526...
ONLY NEES A,B help please!!!
1II. (10 pts) You are given the following estimated equation In(wage)- 0.3688+0.0852educ + 0.05 teure-0.000994tenue 0.0908) (0.0069) (0.0068) (0.00025) R-0.3294 526 in which: logtwage) log of average hourly wage; educ is the number of years of schooling tenure is the number of years of tenure fenure tenure remure The plot of the residuals against the fitted values from the regression above, is provided below: 2.5 1.5 Fitted values .5 a. With a 1% significance level,...
6. eBook The quarterly sales data (number of copies sold) for a college textbook over the past three years follow Quarter Year 1 Year 2 Year 3 1,765 1,063 2,974 2,554 1,591 1,827 935 2,646 2,423 980 2,812 2,358 4 There appears to be a seasonal pattern in the data and perhaps amoderate upward linear trend b. Use the following dummy variables to develop an estimated regression equation to account for any seasonal effects in the data: Qtrl 1 if...
?
ANSWER FROM LETTER "E" AND DOWNWARDS
III- (15pts) You are given the following economic model 0.013 -$26 Rsquare- 0.4177 0.0012ten (0.00024 log(wage) 0.478 + 0.085edu + 0.059ten-0.058/emale-0.01 ledu.female-0.02 1/emale./en- Std errors (0.113) (0.008) (0.007) (0.174) (0.006 With all the variables described as follows: log(wage) -log of average hourly wage; female is a dummy variable equal to 1 if the observed person is a female, and O if make; edu female is an interaction variable equal to education'female; edu is...
4. Consider the following estimated semilog equation: = -8.10+ 0.100ED,+ 0.11 EXP where SAL = salary of the worker, ED = worker's years of education, and EXP = worker's years of experience. The mean salary is $50,000 and the mean education is 12 years. a. Interpret the estimated coefficients on education and experience. b. Calculate and interpret the slope and elasticity of salary with respect to education. c. Draw the shapes of the relationships between salary vs. education and salary...
III-(15pts) You are given the following estimated equation: log(wage)- 0.18+0.093edu +0.044exp+0.043 female-0.016edu female-0.010exp female-0.00068 exp (0.0001) 0.014) 0.4160 0.003 Std errors (0.132) (0.009) (0.005) (0.196) n-526 R-square With all the variables described as follows: logiwage)-log of average hourly wage: female is a dummy variable equal to 1 if the observed person is a female, and 0 if male; edu female is an interaction variable equal to education 'female; edu is the number of years of schooling exp is the number...
please be detailed in your response :) thank you!
0 pts) You are given the following estimated equation: In(wage) 0.1279+0.0904educ + 0.041 exper-0 (0.1059) (0.0075) (0.0052) (0.00012) R 0.3003 526 in which: log(wage) log of average hourly wage - educ is the number of years of schooling: - exper is the number of years of experience -exper'=experience"experience The plot of the residuals against the fitted values from the regression above, is provided below: .5 2.5 1.5 Fitted values a. With...
QUESTION 1 Consider the following OLS regression line (or sample regression function): wage =-2.10+ 0.50 educ (1), where wage is hourly wage, measured in dollars, and educ years of formal education. According to (1), a person with no education has a predicted hourly wage of [wagehat] dollars. (NOTE: Write your answer in number format, with 2 decimal places of precision level; do not write your answer as a fraction. Add a leading minus sign symbol, a leading zero and trailing...
Please help. I'm stuck.
The quarterly sales data (number of copies sold) for a college textbook over the past three years follow. Quarter Year 1 Year 2 Year 3 1 1,690 1,800 1,850 N 940 900 1,100 3 2,625 2,900 2,930 4 2,500 2,360 2,615 (a) Construct a time series plot. 3500 3000+ 2500+ 2000+ N 1 500+ 1000+ 500+ 0 4 1 4 1 + 2 3 Year 1 2 3 Year 2 2 3 Year 3 O Year/Quarter...
ONLY NEES A,B help please!!!
1II. (10 pts) You are given the following estimated equation In(wage)- 0.3688+0.0852educ + 0.05 teure-0.000994tenue 0.0908) (0.0069) (0.0068) (0.00025) R-0.3294 526 in which: logtwage) log of average hourly wage; educ is the number of years of schooling tenure is the number of years of tenure fenure tenure remure The plot of the residuals against the fitted values from the regression above, is provided below: 2.5 1.5 Fitted values .5 a. With a 1% significance level,...
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