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> summary(fm1 <- glm(lot1 ~ log(u), data = clotting, family = Gamma))
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> summary(fm1 <- glm(lot1 ~ log(u), data = clotting, family = Gamma))
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Call:
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Call:
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glm(formula = lot1 ~ log(u), family = Gamma, data = clotting)
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glm(formula = lot1 ~ log(u), family = Gamma, data = clotting)
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Deviance Residuals:
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     Min        1Q    Median        3Q       Max
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-0.04008  -0.03756  -0.02637   0.02905   0.08641
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Coefficients:
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Coefficients:
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              Estimate Std. Error t value Pr(>|t|)
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              Estimate Std. Error t value Pr(>|t|)
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(Intercept) -0.0165544  0.0009275  -17.85 4.28e-07 ***
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(Intercept) -0.0165544  0.0009275  -17.85 4.28e-07 ***
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log(u)       0.0153431  0.0004150   36.98 2.75e-09 ***
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log(u)       0.0153431  0.0004150   36.98 2.75e-09 ***
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---
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> summary(fm2 <- glm(lot2 ~ log(u), data = clotting, family = Gamma))
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> summary(fm2 <- glm(lot2 ~ log(u), data = clotting, family = Gamma))
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Call:
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Call:
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glm(formula = lot2 ~ log(u), family = Gamma, data = clotting)
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glm(formula = lot2 ~ log(u), family = Gamma, data = clotting)
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Deviance Residuals:
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     Min        1Q    Median        3Q       Max
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-0.05574  -0.02925   0.01030   0.01714   0.06371
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Coefficients:
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Coefficients:
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              Estimate Std. Error t value Pr(>|t|)
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              Estimate Std. Error t value Pr(>|t|)
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(Intercept) -0.0239085  0.0013265  -18.02 4.00e-07 ***
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(Intercept) -0.0239085  0.0013265  -18.02 4.00e-07 ***
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log(u)       0.0235992  0.0005768   40.91 1.36e-09 ***
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log(u)       0.0235992  0.0005768   40.91 1.36e-09 ***
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---
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