C) What is the value of the deviance residual e 10 D) What is the value of the Pearson residual ef? 10

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C) What is the value of the deviance residual el?
10
D) What is the value of the Pearson residual e ?
10
Transcribed Image Text:C) What is the value of the deviance residual el? 10 D) What is the value of the Pearson residual e ? 10
An abrasion test was performed using n = 14 moulded ceramic pieces. The following are the test results,
where for the i-th piece, the result of the test was recorded (one of Survived, Broken). The data also gives x₁,
the porosity index of the piece, which is a numerical quantity:
(Survived,8), (Broken,16), (Broken,19), (Survived,26), (Survived,49), (Survived,54), (Survived,66),
(Survived,79), (Survived, 102), (Broken,111), (Survived,114), (Broken,151), (Survived,163), (Broken,199)
We want to study the resistance of pieces to abrasion as function of porosity. To this end, consider the
following logistic regression model,
log( · ) = B₁ + B₁x₁₂
πi
π i
where the observations follow the distribution Y; Bernoulli(;). A data value yi = 1 indicates that the
piece survived the test and y; O indicates that the piece broke. Fit this model in R using the function
=
Transcribed Image Text:An abrasion test was performed using n = 14 moulded ceramic pieces. The following are the test results, where for the i-th piece, the result of the test was recorded (one of Survived, Broken). The data also gives x₁, the porosity index of the piece, which is a numerical quantity: (Survived,8), (Broken,16), (Broken,19), (Survived,26), (Survived,49), (Survived,54), (Survived,66), (Survived,79), (Survived, 102), (Broken,111), (Survived,114), (Broken,151), (Survived,163), (Broken,199) We want to study the resistance of pieces to abrasion as function of porosity. To this end, consider the following logistic regression model, log( · ) = B₁ + B₁x₁₂ πi π i where the observations follow the distribution Y; Bernoulli(;). A data value yi = 1 indicates that the piece survived the test and y; O indicates that the piece broke. Fit this model in R using the function =
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