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)....
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...
9. A regression of log(wage) is run on a set of following variables: educ (years of education), exper (years of experience) and numdep (number of dependents). The regression results are listed as follows. > a-1m(1wage-educ+exper+numdep,data-wage1) > summary(a) Call: LmCformula lwage educ exper numdep, data wage1) Residuals: -2.04105-0.30678-0.05124 0.30711 1.41812 Coefficients: Min 10 Median Max Estimate Std. Error t value Preltl (Intercept) 0.180983 0.117485 1.540 0.1 educ exper numdep 0.099472 0.007862 12.652 < 2e-16 0.010510 0.001569 0.013218 0.016486 0.802 0.423 Signif....
Interpreting regression results 2. This is the result of a regression where goals is the dependent variable and minutes played is the explanatory variable. a. Write out the simple linear regression equation that predicts goals based on time played using the output displayed here. If the average soccer player played one additional game (90 minutes), how many additional goals would you predict them to have scored? b. Call: 1m(formula goalstimeplayed, data -data) Residuals: Min 1Q Median 3Q Max 5.0572-1.6294 -0.3651...
A company manager is interested in analyzing the relationship between years of working experience and the salary of their employees. He has collected the data from 30 employees of their years of experience and the salary. Below provided is a partial regression output from R. Use the provided information to answer below questions Coefficients: (Intercept) YearsExperience Estimate Std. Error t value Pr(>ltl) 25792.2 2273.1 9450.0 --- Signif. codes: O '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1'' 1 Analysis of...
Using CrunchIt, print out and attach the regression output using the Chapter 10 Exercise 01 data set. The independent variable is years of education attained (EDUC); the dependent variable is income (INC), which is measured in dollars. (This is the regression output I got from the data) a. Based on your regression output, is the coefficient on EDUC statistically different from 0 at the 1% level? Why? b. Calculate the predicted value for INC when EDUC=10. Results Multiple Linear Regression...
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...
1.-Interpret the following regression model Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -7.819e+05 7.468e+04 -10.470 < 2e-16 *** Lot.Size -5.359e-01 1.163e-01 -4.610 4.67e-06 *** Square.Feet 1.108e+02 1.109e+01 9.986 < 2e-16 *** Num.Baths 2.985e+04 9.650e+03 3.094 0.00204 ** API.2011 1.226e+03 9.034e+01 13.568 < 2e-16 *** dis_coast -7.706e+00 2.550e+00 -3.022 0.00259 ** dis_fwy 1.617e+01 1.232e+01 1.312 0.18995 dis_down 5.364e+00 3.299e+00 1.626 0.10429 I(dis_fwy * dis_down) -4.414e-04 5.143e-04 -0.858 0.39098 Pool 1.044e+05 2.010e+04 5.194 2.59e-07 *** --- Signif. codes: 0 ‘***’ 0.001...
Question on interpreting linear regression 1. The data file airfares.txt on the book web site gives the one-way airfare (in US dol on modeling airfare as a function of distance. The first model fit to the data was Fare B+BDistance+e (3.7) (a) Based on the output for model (3.7) a business analyst concluded the following The regression coefficient of the predictor variable, Distance is highly statistically signifi- cant and the model explains 99.4% of the variability in the Y-variable. Fare....
2.-Interpret the following regression model Call: lm(formula = Sale.Price ~ Lot.Size + Square.Feet + Num.Baths + API.2011 + dis_coast + I(dis_fwy * dis_down * dis_coast) + Pool, data = Training) Residuals: Min 1Q Median 3Q Max -920838 -84637 -19943 68311 745239 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -7.375e+05 7.138e+04 -10.332 < 2e-16 *** Lot.Size -5.217e-01 1.139e-01 -4.581 5.34e-06 *** Square.Feet 1.124e+02 1.086e+01 10.349 < 2e-16 *** Num.Baths 3.063e+04 9.635e+03 3.179 0.00153 ** API.2011 1.246e+03 8.650e+01 14.405 < 2e-16...