Logistic Regression, how make predictions?

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Logistic Regression, how make predictions?

nicklan
i have this dataset :
https://www.dropbox.com/s/fzeqotlpc6jlik4/Coronary%20Heart%20Disease.csv?dl=0

i run logistic regression and the results were

=== Run information ===

Scheme:       weka.classifiers.functions.Logistic -R 1.0E-8 -M -1
-num-decimal-places 4
Relation:     Coronary Heart
Disease-weka.filters.unsupervised.attribute.NumericToNominal-Rfirst-last
Instances:    100
Attributes:   2
              Age
              Coronary-Heart-Disease
Test mode:    10-fold cross-validation

=== Classifier model (full training set) ===

Logistic Regression with ridge parameter of 1.0E-8
Coefficients...
               Class
Variable           0
====================
Age=25        1.8177
Age=32        1.4923
Age=37        0.7191
Age=42        0.3136
Age=47       -0.2254
Age=52       -0.8903
Age=57       -1.5581
Age=62       -1.7658
Intercept     0.3795


Odds Ratios...
               Class
Variable           0
====================
Age=25        6.1578
Age=32        4.4473
Age=37        2.0526
Age=42        1.3684
Age=47        0.7982
Age=52        0.4105
Age=57        0.2105
Age=62         0.171


Time taken to build model: 0.01 seconds

=== Stratified cross-validation ===
=== Summary ===

Correctly Classified Instances          71               71      %
Incorrectly Classified Instances        29               29      %
Kappa statistic                          0.3998
Mean absolute error                      0.3808
Root mean squared error                  0.4512
Relative absolute error                 77.5828 %
Root relative squared error             91.0537 %
Total Number of Instances              100    

=== Detailed Accuracy By Class ===

                 TP Rate  FP Rate  Precision  Recall   F-Measure  MCC    
ROC Area  PRC Area  Class
                 0,789    0,395    0,726      0,789    0,756      0,402  
0,737     0,737     0
                 0,605    0,211    0,684      0,605    0,642      0,402  
0,737     0,620     1
Weighted Avg.    0,710    0,316    0,708      0,710    0,707      0,402  
0,737     0,687    

=== Confusion Matrix ===

  a  b   <-- classified as
 45 12 |  a = 0
 17 26 |  b = 1
---------------------------------------------------------------
*a) how can i predict, for example, the probability of someone of 41 years
old belonging to class0 or class1?
b) how can i find the slope of the model?
c) can i graph the nonlinear model we produced?
d) can i find the Sum of Squared Error & Total Sum of Squares?*



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