Suppose you have the following wage and education data for a sample with 3 observations: obs. no. 1 2 3 wage educ ($/hr) (years) 10.73 11 13.44 12 18.05 16 Calculate the coefficient for the regression of wage on education through the origin (i.e. 1 in the model wage = Bieduc). Express your answer out to two (2) decimal points.
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- How do you interpret the coefficient of the education variable in the following: wage -0.90 + 0.54 educationConsider the following regression estimates (FN2) Linear regression belavg abvavg female married _cons b. 1 wage O C. 5 O d. 4 Robust Coef. Std. Err. 3047845 3150202 2820787 -1.063254 .0693348 -2.751963 .9686236 .2612646 6.699098 .2889831 Number of obs F(4, 1255) Prob > F R-squared Root MSE t P>|t| -3.49 0.001 0.22 0.826 -9.76 0.000 3.71 0.000 23.18 0.000 |||||||||| = = .45606 6.132155 = -1.661197 -.5486894 -3.305361 = = [95% Conf. Interval] 1,260 56.35 0.0000 0.1121 4.3987 assume that MLR 1-6 hold. In the regression above, how many coefficients (including the constant) are statistically significant at the 1% level? a. 3 -.4653108 .687359 -2.198565 1.481187 7.266042How should be the following given be treated while estimating national income (1) bonus paid to employees
- Consider the following regression estimates (ID6) Source Model Residual Total wage educ exper exper2 _cons inverse u-shaped SS C. linear d. exponential 1927.87673 5232.53756 7160.41429 df -.0046123 -3.96489 642.625576 3 522 10.0240183 525 Coef. Std. Err. .5953429 .0530251 .268287 .0368969 .000822 .7521526 MS 13.6388844 Number of obs F(3, 522) Prob > F R-squared Adj R-squared Root MSE t P>|t| 0.000 11.23 7.27 0.000 -5.61 0.000 -5.27 0.000 = .4911741 .1958023 -.006227 -5.442508 = = = 526 64.11 0.0000 0.2692 0.2650 3.1661 [95% Conf. Interval] .6995118 .3407717 where wage is hourly wage in US$, educ is years of education, exper is years of work experience and exper2 is experience squared (exper* exper). According to these estimates, what is the functional form of the relationship between experience and predicted wage when we hold education constant? a. u-shaped b.i -.0029975 -2.487272wages = B1 + B2educ + ßzexper + e where wages denotes hourly wages. We estimate the regression in R and obtain the output ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 2.0 1.0 2 0.0455 * ## educ 0.5 0.5 1 0.3173 ## exper 2.0 0.5 4 6.33e-05 *** ## --- ## Signif. codes: ****' 0. 001 '**' 0.01 **' 0.05 ' 0.1 ' ' 1 Build a 90% confidence interval for B3 using a normal approximation. (Use that if Z ~ N(0, 1) and z1-a satisfies P(Z > z1-a) = a, then zo9 = 1.28, zo95 = 1.64, Z0.975 = 1.96, zo.99 = 2.33, and z0.995 = 2.58). Oa. [2 – 1.64 x (0.5), 2 + 1.64 x (0.5)] O b. [2 – 1.28 × (0.5), 2 + 1.28 x (0.5)] c. [2 – 1.28 x (0.5)², 2 + 1.28 × (0.5)²] O d. [2 – 1.64 × (0.5)², 2 + 1.64 × (0.5)²] O e. [2 – 1.96 × (0.5)², 2 + 1.96 × (0.5)²] O f. [2 – 1.96 × (0.5), 2 + 1.96 × (0.5)]Question 19 An economist wants to estimate the relationship between earnings (in thousands of dollars) and years of education. She estimates the model Earnings BO+B1 *Education + E. The following is a portion of her results. Coefficient Standard Error Intercept 15.77 Education 4.21 Which of the following is true? 3.8 1.4 p-value 0.003 0.032 O If education is increased by one year, we predict that earnings will increase by $4,210. O If earnings are increased by $1000, we predict that education will increase by 21 O If education is increased by one year, we predict that earnings will increase by $3200. O If earnings are increased by $1000, we predict that education will increase by .032 years,
- Compute the R² using the following information. What does this number mean? Wage = 4.43 +0.61Edu + e n = 1089, SST = 37211.6, SSE 37211.6, SSE 19848.2 R² = = = 0.4666. 46.66% of the variation in wage can be explained by education. R² = 0.5334. 53.34% of the variation in wage can be explained by education. R² = 0.61.61% of the variation in wage can be - explained by education. R² = 0.3043. 30.43% of the variation in wage can be explained by education.QUESTION 7 Which is NOT true about the coefficient of determination? As you add more variables, the R-square generally rises. As you add more variables, the adjusted R-square can fall. If the R-square is above 50%, the regression is considered significant. The R-square gives the percent of the variation in the dependent variable that is explained by the independent variables. The higher is the R-square, the better is the fit.ii. Interpret both slope coefficients of the estimated OLS regression of wage (in rupees) on education (in years) and experience (in years). What is the overall predicted wage change if education and experience change by one year each? = Wage 30000+1500 education + 500 experience What can you infer about the shape (U-shape/inverse U-shape) of the relationship between job performance (the number of tasks completed per day) and hours of sleep a person gets from the following estimated OLS regression? At what level of sleep does the job performance peak? Job Performance 20+5 hours of sleep-0.25 (hours of sleep)2
- A commercial airline is analyzing the price scale for round trip flights between New York and Pennsylvania. The sooner the c buys the ticket, the cheaper the ticket price is. The sales manager collected information for the last year on how many cust bought their tickets at each price: Ticket Price (in dollars) # of Customers (in thousands) 5 15 23 27 20 89 125 135 150 178 198 10 What is the coefficient of correlation between the ticket price and the number of customers purchasing at that price? Answer:In this exercise, you will investigate the relationship between earnings and height. These data are taken from the US National Health Interview Survey for 1994. Download the data from the table by clicking the download table icon . A detailed description of the variables used in the dataset is available here LOADING... . Use a statistical package of your choice to answer the following questions. Earnings and…Johnny works as a laborer and he earns the following income for the hours he works. He receives an overtime bonus of 150% of his hourly rate for all hours he works after 40 hours. Hours Worked Per Week Income Earned 10 140 20 280 30 420 40 560 50 770 The relationship displayed between the number of hours Johnny works and the income he eams is positive negative