Andy wants to examine the impact of years of education (EDUC) and labour productivity(PROD) on monthly salary (SALARY) of a sample of 150 teachers at StellenboschMunicipality. However, it is difficult to measure productivity. Hence, it is approximated asfollows: PROD = Pupil/Teacher ratio of the school where the teacher works. In other words,the regression model is:).,( PRODEDUCfSALARY = Andy argues that various reasonslead to the presence of error terms in the above regression. Explain any five of these reasons.Always refer to the above regression in your explanationQUESTION 2Busisiwe would like to investigate the relationship between weekly party hours (PARTY) andmodule test marks (MARKS) of a sample of 12 second-year Microeconomics students. Theinformation is presented in the following table:Student PARTY (X) MARKS (Y)[A] 02 90[B] 03 85[C] 04 82[D] 04 76[E] 04 75[F] 05 74[G] 05 70[H] 06 62[I] 07 55[J] 08 52[K] 10 43[L] 14 402.1 Complete the following table with the aid of the data in the above table: XYXY XY 2X5562.2 By using the OLS method to derive the parameters of the bivariate regression:.^2^1^PARTYMARKS +=Show all calculations.Hints:22^2XnXYXnYXiii−−=XY ^2^1−=

In this exercise we examine which factors might influence the wages of employees. To do so, the variable wage is regressed on the variables educ, tenure, tensq, female, married, and m_female.Table 1: Descriptive StatisticsVariable Explanationwage Average hourly wage (in €)educ Years of educationtenure Employment duration (current employer)tensq = tenure × tenurefemale dummy (= 1 if female)married dummy (= 1 if married)m_female      Interaction term (= married × female)The corresponding Stata output is:Source | SS df MS N. of obs. = 526 + F(6, 519) = 57.23 Model | 2851.181 6 475.197 Prob > F = 0.000 Residual | 4309.233 519 8.303 R-squared = 0.398 + Adj. R-sq. = 0.391 Total | 7160.414 525 13.639 Root MSE = 2.882 wage | Coef. S. Err. t-stat. P > |t| [95% Conf. Int.] + educ |0.522 0.046 ? ? ? ? tenure | 0.263 0.046 5.69 0.000 0.172 0.354 tensq | -0.005 0.002 -2.77 0.006 -0.008 -0.001 female | -0.321 0.408 -0.79 0.431 -1.123 0.480 married | 1.807 0.389 4.65 0.000 1.043 2.571 m_female | -2.326 0.526 ? ? ? ? _cons | -2.012 0.662 -3.04 0.002 -3.313 -0.711 Reference: Wooldridge, J. (2013). Introductory Econometrics, 5E. © 2013 South-Western, a part of Cengage, Inc. Reproduced by permission. www.cengage.com/permissions.Does educ have a significant relationship with the outcome wage? State the associated null hypothesis and alternative hypothesis for the coefficient βeduc. Also, determine its statistical significance at the 5 percent significance level in a two-sided test. Which one is the correct answer?Answer 1H0:   vs.   H1: c(0.05, 526-6-1) = 1.645⇒ t > c⇒ H0 can be rejected at the 5 percent significance level.Answer 2H0:   vs.   H1: c(0.05, 526-6-1) = 1.645⇒ t > c⇒ H0 can be rejected at the 5 percent significance level.Answer 3H0:   vs.   H1: c(0.05, 526-6-1) = 1.960⇒ t > c⇒ H0 can be rejected at the 5 percent significance level.Answer 4H0:   vs.   H1: c(0.05, 526-6-1) = 1.960⇒ t > c⇒ H0 can be rejected at the 5 percent significance level.

In the model Wage = B0 + B1 Education + U, if U is correlated with Educatiom and 8, > 0 the oLS estimators areSelect one: a.biased and consistentb,unbiased and inconsistentc,positively biased and consistentd,biased and inconsistent e.negatively biased and inconsistentf,unbiased and consistent

You are interested in the influence of education on hourly wages.Table 1: Description of VariablesVariable Descriptionwage Hourly wage (in €)educ Education in yearsSelect the population model with which the interpretation below is possible.The hourly wage of a person increases by x € if the education of the person increases by one percent (ceteris paribus).Answer 1Answer 2Answer 3Answer 4

Human capital theory suggests that those who have invested in higher levels of education will be able to command higher wages. A labour economist collected data on annual wages (Y, in $'000) and years of study (X) from a random sample of 12 employees to test this proposition. Assuming a linear relationship between Y and X, the labour economist used a least-squares method and found that the Y intercept = -23.50 and the slope = 9.73. Also, the sum of squares total (SST) and the sum of squares regression (SSR) were equal to 11132.92 and 8726.91, respectively. What is the lower bound of a 95% prediction interval of annual wages (in $'000) for an employee with 10 years of study. Assumer the h Statistic is 0.10. a. greater than $20 but less than $36 b. less than $20 c. greater than $20 but less than $30 d. greater than $36

Human capital theory suggests that those who have invested in higher levels of education will be able to command higher wages. A labour economist collected data on annual wages (Y, in $'000) and years of study (X) from a random sample of 12 employees to test this proposition. Assuming a linear relationship between Y and X, the labour economist used a least-squares method and found that the Y intercept = -23.50 and the slope = 9.73. Also, the sum of squares total (SST) and the sum of squares regression (SSR) were equal to 11132.92 and 8726.91, respectively. Based on this information, the standard error of the estimate is equal to __________. Round your final answer to two decimal places.