bsebitdaratio degree deg knots minebitdaratio23

Bsebitdaratio degree deg knots minebitdaratio23

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bs(ebitdaratio, degree = deg, knots = quantile(ebitdaratio[ebitdaratio > min(ebitdaratio)])[2:3]) 3039 5 36.14 6.783e-37 bs(year, degree = deg, knots = quantile(year[year > min(year)])[2:3]) 611.6 5 7.273 8.193e-07 24
Sum Sq Df F value Pr(>F) bs(assets, degree = deg, knots = quantile(assets[assets > min(assets)])[2:3]) 5245 5 62.37 1.625e-64 bs(capex, degree = deg, knots = quantile(capex[capex > min(capex)])[2:3]) 259.8 5 3.089 0.008645 bs(ltd, degree = deg, knots = quantile(ltd[ltd > min(ltd)])[2:3]) 401.5 5 4.775 0.0002314 bs(ebitda, degree = deg, knots = quantile(ebitda[ebitda > min(ebitda)])[2:3]) 2650 5 31.51 5.083e-32 bs(ppe, degree = deg, knots = quantile(ppe[ppe > min(ppe)])[2:3]) 1723 5 20.5 1.909e-20 bs(sales, degree = deg, knots = quantile(sales[sales > min(sales)])[2:3]) 262.2 5 3.118 0.008137 bs(ads, degree = deg, knots = quantile(ads[ads > min(ads)])[2:3]) 232.7 5 2.768 0.01671 bs(rd, degree = deg, knots = quantile(rd[rd > min(rd)])[2:3]) 486.8 5 5.789 2.405e-05 bs(bookval, degree = deg, knots = quantile(bookval[bookval > min(bookval)])[2:3]) 44053 5 523.9 0 bs(mv, degree = deg, knots = quantile(mv[mv > min(mv)])[2:3]) 68811 5 818.3 0 as.factor(indclass) 1298 40 1.929 0.0003886 Residuals 225348 13399 NA NA pander ( Anova (stepfitbs4)) Table 16: Anova Table (Type II tests) Sum Sq Df F value Pr(>F) bs(ltdratio, degree = deg, knots = quantile(ltdratio[ltdratio > min(ltdratio)])[2:3]) 3374 5 40.02 5.567e-41 bs(ebitdaratio, degree = deg, knots = quantile(ebitdaratio[ebitdaratio > min(ebitdaratio)])[2:3]) 3158 5 37.45 2.822e-38 bs(year, degree = deg, knots = quantile(year[year > min(year)])[2:3]) 763.6 5 9.056 1.315e-08 bs(assets, degree = deg, knots = quantile(assets[assets > min(assets)])[2:3]) 6174 5 73.22 6.583e-76 bs(capex, degree = deg, knots = quantile(capex[capex > min(capex)])[2:3]) 225.7 5 2.677 0.02007 bs(ltd, degree = deg, knots = quantile(ltd[ltd > min(ltd)])[2:3]) 382.9 5 4.54 0.0003876 bs(ebitda, degree = deg, knots = quantile(ebitda[ebitda > min(ebitda)])[2:3]) 2895 5 34.34 5.375e-35 25
Sum Sq Df F value Pr(>F) bs(ppe, degree = deg, knots = quantile(ppe[ppe > min(ppe)])[2:3]) 1633 5 19.37 2.889e-19 bs(sales, degree = deg, knots = quantile(sales[sales > min(sales)])[2:3]) 448.2 5 5.316 6.963e-05 bs(ads, degree = deg, knots = quantile(ads[ads > min(ads)])[2:3]) 209.8 5 2.488 0.02928 bs(rd, degree = deg, knots = quantile(rd[rd > min(rd)])[2:3]) 786.8 5 9.331 6.914e-09 bs(bookval, degree = deg, knots = quantile(bookval[bookval > min(bookval)])[2:3]) 45926 5 544.7 0 bs(mv, degree = deg, knots = quantile(mv[mv > min(mv)])[2:3]) 76835 5 911.2 0 Residuals 226974 13459 NA NA pander ( AIC (bsfit4, stepfitbs4)) df AIC bsfit4 127 76684 stepfitbs4 67 76661 pander ( BIC (bsfit4, stepfitbs4)) df BIC bsfit4 127 77638 stepfitbs4 67 77164 par ( mfrow = c ( 1 , 2 )) 26
residualPlots (bsfit4, terms = ~ 1 ) Test stat Pr(>|t|) Tukey test 85.44 0 residualPlots (stepfitbs4, terms = ~ 1 ) 27
Test stat Pr(>|t|) Tukey test 83.616 0 par ( mfrow = c ( 2 , 2 )) plot (newdat$tobinsQ, fitted (stepfitbs4)) abline ( 0 , 1 ) plot ( fitted (stepfitbs4), residuals (stepfitbs4)) abline ( h = 0 ) hist ( residuals (stepfitbs4)) acf ( residuals (stepfitbs4)) 28
ncvTest (stepfitbs4) Non-constant Variance Score Test Variance formula: ~ fitted.values Chisquare = 110418.6 Df = 1 p = 0 runs.test ( residuals (stepfitbs4)) Runs Test - Two sided data: residuals(stepfitbs4) Standardized Runs Statistic = -50.278, p-value < 2.2e-16 29
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