Hello, was just wondering if anyone could help out with these? All of the info required should be in the photo attached. Thanks.1. Write down the econometric model that this OLS regression estimates2. Interpret the belavg coefficient.3. At how many years of experience is the marginal effect of experience on predictedwage equal to zero?4. What is predicted effect of an increasing work experience from 5 to 6 years on wage? 5. What is the predicted hourly wage for an average looking worker with 10 years ofexperience? Consider the following OLS regression outputLinear regressionwagebe lavgabvavgexperexpersq_consRobuststd. err.Coefficientt P>|t|-1.269988.2589818-4.00 0.0000.84 0.40210.69 0.000.3314639-.00548690007058 -7.77 0.0002.95287 .2500308 11.81 0.000Number of obsF(4, 1255)Prob > FR-squaredRoot MSE.3172118.3090035. 0310071||||||||||===1,26046.490.00000.09084.4511[95% conf. interval]-1.892312-.6476643-.3472386.8652022.2706325.3922954-.0068716 -.00410213.4433942.462345where wage is hourly wage in US$, belavg is a dummy variable which is equal to 1 if theperson is below average looking (and 0 otherwise), abvavg is a dummy variable which isequal to 1 if the person is above average looking (and 0 otherwise), exper is years of workexperience, and expersq is experience squared (exper* exper). Assume that MLR 1-6 hold.

1. Write down the econometric model that this OLS regression estimates 2. Interpret the belavg coefficient. 3. At how many years of experience is the marginal effect of experience on predicted wage equal to zero? 4. What is predicted effect of an increasing work experience from 5 to 6 years on wage? 5. What is the predicted hourly wage for an average looking worker with 10 years of experience?

1. Write down the econometric model that this OLS regression estimates 2. Interpret the belavg coefficient. 3. At how many years of experience is the marginal effect of experience on predicted wage equal to zero? 4. What is predicted effect of an increasing work experience from 5 to 6 years on wage? 5. What is the predicted hourly wage for an average looking worker with 10 years of experience?

Linear Algebra: A Modern Introduction

4th Edition

ISBN:9781285463247

Author:David Poole

Publisher:David Poole

Chapter7: Distance And Approximation

Section7.3: Least Squares Approximation

Problem 31EQ

See similar textbooks

Similar questions

Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
The following is a partial computer output of a multiple regression analysis of a data set containing 20 sets of observations on the dependent variableThe regression equation isSALEPRIC = 1470 + 0.814 LANDVAL + 0.820 IMPROVAL + 13.5 AREA Predictor Coef SE Coef T P Constant 1470 5746 0.26 0.801 LANDVAL 0.8145 0.5122 1.59 0.131 IMPROVAL 0.8204 0.2112 3.88 0.0001 AREA 13.529 6.586 2.05 0.057 S = 79190.48 R-Sq = 89.7% R-Sq(adj) = 87.8% Analysis of Variance Source DF SS MS Regression 3 8779676741 2926558914 Residual Error 16 1003491259 62718204 Total 19 9783168000 For the problem above, we want to carry out the significance test about the coefficient of LANDVAL, what is the t-value for this test, and is it significant? 46.66, significant 2.05, significant 1.59, not significant 0.26, not significant
A researcher notes that, in a certain region, a disproportionate number of software millionaires were born around the year 1955. Is this a coincidence, or does birth year matter when gauging whether a software founder will besuccessful? The researcher investigated this question by analyzing the data shown in the accompanying table. Complete parts a through c below. a. Find the coefficient of determination for the simple linear regression model relating number (y) of software millionaire birthdays in a decade to total number (x) of births in the region. Interpret the result. The coefficient of determination is 1.___? (Round to three decimal places as needed.) This value indicates that 2.____ of the sample variation in the number of software millionaire birthdays is explained by the linear relationship with the total number of births in the region. (Round to one decimal place as needed.) b. Find the coefficient of determination for the simple linear regression model…
Consider the following regression model: Dependent Variable: MU Sample: 134 Variable b BETA t-Statistic P-value Constant 7.292206 6.666348 0.0000 COLLEGE -0.115133 -0.23 -4.437697 0.0000 GROWTH 0.018898 0.01 1.718810 0.0880 SENIORS -0.022380 -0.24 -0.731017 0.4661 WAGE -0.0000294 -0.001 -0.874836 0.3833 R-squared 0.192456 Mean dependent var 3.852239 Adjusted R-squared 0.167416 S.D. dependent var 1.928368 Where MU = metropolitan area unemployment rate, COLLEGE = percentage of the metropolitan area population with a college degree, GROWTH = growth rate of the metropolitan area population, SENIORS = percentage of the metropolitan area population that is elderly, and WAGE = average metropolitan area wage Which two variables have the least impact on MU?
The following information pertains to a simple least squares regression for DEF Corporation: Mean value of the dependent variable 30Mean value of the independent variable 8Coefficient of the independent variable 3Number of observations 12 What is the "a" value for the leasts-quares regression model? a. 60b. 30c. 6d. 0
An econometrician suspects that the residuals of her model might be autocorrelated. Explain the steps involved in testing this theory using the Durbin–Watson (DW) test
Which of the following does not need to be computed to determine a simple regression line? SSx SP "Y-hat" SSy
Suppose that a multiple linear regression model was fit to data and that the following output resulted: Coefficients: (Intercept)ageheightforearm Estimate10.14882 0.06045-0.02108-0.06479 Std. Error4.492450.068380.063500.06847 t value 2.2590.884-0.332-0.946 Pr(>|t|)0.02640.37920.74080.3467 True or False? If we were to perform a backwards selection process on this data set, the first to be removed is forearm since it has the smallest p-value. True False
In Australia, 16% of the adult population is nearsighted.17 If three Australians are chosen at random, what is the probability that two are nearsighted and one is not? 2.state each of the ﬁve assumptions of the classical regression model (OLS) and give an intuitive explanation of the meaning and need for each of them.
A manufacturer of car batteries claims that the mean lifetime of their battery is 67 months. Thinking that this claim is inflated, graduate students buy a random sample of 72 car batteries from this manufacturer. How should they proceed? a) Perform a hypothesis test of H0:μ=67 versus Ha:μ>67 b) Perform a hypothesis test of H0:μ=67 versus Ha:μ<67 c) Use Simple linear regression
Interpret the estimated regression coefficient corresponding to the Z variable. Data Salary Education Experience Sex 29.7985 15 3 1 21.8219 4 0 0 22.8978 4 0 0 22.0917 1 1 0 21.8993 5 0 0 22.4829 3 1 1 28.0772 15 0 0 y=salary 23.6292 6 1 1 x1=education level in schooling years 32.3595 0 15 1 x2=experience level in employment level 21.794 1 0 0 d=sex (1 for male,0 for female) 19.8762 3 0 0 Ln(Y) = alpha +beta1X1 +Beta2X2+ Beta3D +Beta4Z +e 21.0253 3 0 0 where z =X2D 24.6323 0 5 1 19.0247 0 0 0 18.8857 0 0 0 21.8552 1 0 0 24.2675 6 1 0 18.7931 0 0 0 18.9276 0 0 0 23.4441 5 1 1 20.8047 2 0 0 18.26 0 0 0 20.6726 0 2 1 21.7815 3 0 0…
In a typical multiple linear regression model where x1 and x2 are non-random regressors, the expected value of the response variable y given x1 and x2 is denoted by E(y | 2,, X2). Build a multiple linear regression model for E (y | *,, *2) such that the value of E(y | x1, X2) may change as the value of x2 changes but the change in the value of E(y | X1, X2) may differ in the value of x1 . How can such a potential difference be tested and estimated statistically?