consider the following two equations: 1) Wage = 5.228 + 0.38EXP 2) Ln(WAGE) = 0.5446 +...
consider the following two equations:
1) Wage = 5.228 + 0.38EXP
2) Ln(WAGE) = 0.5446 + 0.15 EXP
a) Can you use adjusted R squared to compare the fir of the linear equation in 1) with the non- linear logarithmic equation in 2) ?
b) explain why you chose the answer you did in part a)
c) Interpret the coefficient of EXP in the equation Ln (WAGE) = 0.5446 + 0.15 EXP.
Homework Answers
a) No, we cannot use adjusted R-squared to compare the fit of the linear equation with the non linear logarithmic equation.
b) The underlying assumptions for R-squared or adjusted R-squared value aren't true for non linear regression because it assumes that you are fitting a linear model. Hence, it is not the ideal metric to use for non linear regression models but we should use other metrics like MSE etc.
c) Interpretation of coefficient of EXP: With one unit increase in the value of EXP, it multiples the expected value of wage by e0.15
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consider the following two equations:
1) Wage = 5.228 + 0.38EXP
2) Ln(WAGE) = 0.5446 +...
The information of data 1 Question Consider the following table that relates earning per hour (WAGE)...
The information of data 1
Question
Consider the following table that relates earning per hour (WAGE) to years of education (EDUC): Dependent Variable: WAGE Method Least Squares Date: 03/09/20 Time 1330 Sample: 11200 Included observations: 1200 Variable Coefficient Std. Error -Statistic tbl) 1770148 Prob. 0.0000 0.0000 1962400 se(b2) EDUC - 10 39996 2 396761 R-squared Adjusted R-squared SE of regression Sum squared resid Log likelihood F-statistic Prob(F statistic) 0 207327 Mean dependent var 0 206666 SD dependent var 13.55328 Akake...
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Consider the STATA output below, in which the outcome variable is wage (rate of pay in...
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plz show work, thank you 1. For the following problem, determine if the following equations are...
plz show work, thank you
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Useful Equations - AH, Ln P= ΔΗ P 2.- vap 1) + C vap 2) Ln...
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The information of data 1
Question
Consider the following table that relates earning per hour (WAGE) to years of education (EDUC): Dependent Variable: WAGE Method Least Squares Date: 03/09/20 Time 1330 Sample: 11200 Included observations: 1200 Variable Coefficient Std. Error -Statistic tbl) 1770148 Prob. 0.0000 0.0000 1962400 se(b2) EDUC - 10 39996 2 396761 R-squared Adjusted R-squared SE of regression Sum squared resid Log likelihood F-statistic Prob(F statistic) 0 207327 Mean dependent var 0 206666 SD dependent var 13.55328 Akake...
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8. This exercise is a continuation of the previous one. The Lucas numbers Ln are defined by the same relationship as the Fibonacci numbers. Ln+2 = Ln+1 + Ln. However, we begin with Lo = 2 and L-1, which leads to the sequence 2, 1,3,4,7,11,... 「Ln+1 Ln As before, form the vector as a linear combination of vi and v2, eigenvectors of A. Explain why so that a. Xn+1 = Axn. Express X0 b. -(부).. (뷔 Explain why Ln is...
plz show work, thank you
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Useful Equations - AH, Ln P= ΔΗ P 2.- vap 1) + C vap 2) Ln P, RT T, T R R 62.37 L.mm Hg/mol-K or 8.314 x 103 kJ/mol.K 3) PV nRT, 1. For the following questions, assur vapor pressure of water (a cylinder is submerged in water etc.). an experimental set up similar to what used to determine the we A) At low temperature (0 °C), the volume (corrected) in the cylinder was 3.6 mL. The gas pressure...
2. Consider the following system of linear equations: -*1 + 2x2 - 13 = 2 -2:21 +222 + x3 = 4 3x1 + 2.02 +2.03 = 5 -3.21 + 8.22 + 5.23 = 17 (a) Put the system of linear equations into a coefficient matrix. (b) Find the reduced row echelon form of the coefficient matrix. (C) What is the dimension of the row space the coefficient matrix?
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8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer). The regression results are listed as follows. Coefficients: Estimate Std. Error tvalue Pr(Itl) (Intercept) -1.56794 0.72455 -2.164 0.0309 female -1.81085 0.26483 -6.838 2.26e-11*** educ 0.57150 0.04934 11.584 <2e-16*** 0.02540 0.01157 2.195 0.0286 exper 0.14101 0.02116 6.663 6.83e-11*** tenure Signif. codes:0.0010.010.050.1'"1 Residual standard error: 2.958 on 521 degrees of...
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer). The regression results are listed as follows. Coefficients: Estimate Std. Error tvalue Pr(Itl) (Intercept) -1.56794 0.72455 -2.164 0.0309 female -1.81085 0.26483 -6.838 2.26e-11*** educ 0.57150 0.04934 11.584 <2e-16*** 0.02540 0.01157 2.195 0.0286 exper 0.14101 0.02116 6.663 6.83e-11*** tenure Signif. codes:0.0010.010.050.1'"1 Residual standard error: 2.958 on 521 degrees of...
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