t Stat P-value Coefficients Standard Error -80.609 20.632 6.078 1.146 -3.907 0.000 0.000 Lower 95% Upper 95% Lower 95.0% Upper 95.0% -121.331 -39.886 -121.331 -39.886 3.816 8.339 3.816 8.339 5.305 1.085 1.421 0.764 0.446 3.889 -1.719 3.889 Intercept Years of Education Length of tenure in current job Age Work Experience Household Size -1.719 2.629 3.589 0.486 7.379 0.000 4.549 2.629 4.549 -2.223 1.165 -1.909 0.058 -4.521 0.076 -4.521 0.076 1.876 - 1.209 1.563 -0.773 0.440 -4.294 1.876 -4.294 Based on the results, how many thousand dollars change would a 4-year change in one's education make in their salary? (Please only input the rounded number with no decimal place and "$" sign. For example, if it is $34,567.89, the answer would be: 35) In order to investigate the factors affecting a business professional's annual salary (in thousand dollars), a recruiting firm collected data from a group of business professionals and developed a regression model to predict their salary based on their education (in years), tenure in the current job (in years), work experience in years), as well as the size of their household and age. The results of the regression model are shown below. SUMMARY OUTPUT 0.641 0.411 Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.394 47.889 179 ANOVA SS MS F df 5.000 Significance F 0.000 24.144 Regression Residual Total 276842.580 55368.516 396742.482 2293.309 173.000 178.000 673585.061