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    %% Diagnostic Plots for Generalized Linear Models
% This example shows how to analyze  a logistic model using  diagnostic
% plots.
%%
% Input the sample data.

% Copyright 2015 The MathWorks, Inc.

w = [2100 2300 2500 2700 2900 3100 ...
     3300 3500 3700 3900 4100 4300]';
total = [48 42 31 34 31 21 23 23 21 16 17 21]';
poor = [1 2 0 3 8 8 14 17 19 15 17 21]';
%%
% The data are derived from |carbig.mat| , which contains measurements of
% large cars of various weights. Each weight in |w| has a corresponding
% number of cars in total and a corresponding number of poor-mileage cars
% in |poor| . It is reasonable to assume that the values of |poor| follow
% binomial distributions, with the number of trials given by total and the
% percentage of successes depending on |w| . This distribution can be
% accounted for in the context of a logistic model by using a generalized
% linear model with link function log(μ/(1 - μ)) = Xb. This link
% function is called |'logit'| .
%%
% Fit a binomial regression with a logit link function.
mdl = fitglm(w,[poor total],...
    'linear','Distribution','binomial','link','logit')
%%
% See how well the model fits the data.
plotSlice(mdl)
%%
% The fit looks reasonably good, with fairly wide confidence bounds.
%%
% To examine further details, create a leverage plot.
plotDiagnostics(mdl)