請幫忙解釋MINITAB中Binary Logistic Regression的所有數據代表意思
Binary Logistic Regression: neweyes versus cputime, age
Link Function: Logit
Response Information
Variable Value Count
neweyes 1 161 (Event)
0 61
Total 222
Logistic Regression Table
Odds 95% CI
Predictor Coef SE Coef Z P Ratio Lower Upper
Constant -0.278532 1.88890 -0.15 0.883
cputime 1.34257 0.859443 1.56 0.118 3.83 0.71 20.64
age 0.0562209 0.893042 0.06 0.950 1.06 0.18 6.09
cputime*age -0.440088 0.411154 -1.07 0.284 0.64 0.29 1.44
Log-Likelihood = -123.459
Test that all slopes are zero: G = 14.132, DF = 3, P-Value = 0.003
Goodness-of-Fit Tests
Method Chi-Square DF P
Pearson 7.39092 5 0.193
Deviance 7.61868 5 0.179
Hosmer-Lemeshow 2.91617 2 0.233
Table of Observed and Expected Frequencies:
(See Hosmer-Lemeshow Test for the Pearson Chi-Square Statistic)
Group
Value 1 2 3 4 Total
1
Obs 8 48 80 25 161
Exp 10.6 44.9 78.8 26.7
0
Obs 14 20 22 5 61
Exp 11.4 23.1 23.2 3.3
Total 22 68 102 30 222
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