New Test Statistics To Assess The Goodness-Of-Fit Of Logistic Regression Models
| dc.contributor.author | Hussain, Jassim Nassir | |
| dc.date.accessioned | 2018-12-28T07:43:08Z | |
| dc.date.available | 2018-12-28T07:43:08Z | |
| dc.date.issued | 2013-04 | |
| dc.description.abstract | The binary (or binomial) logistic regression model (LRM) is one of the generalised linear models (GLMs). It is used when the dependent variable is dichotomous and the independent variables are of any type. LRM are popular in many applications and in different disciplines including biomedical and social sciences. Assessing the goodness-of-fit (GOF) is considered to be the important step after fitting the model to show the adequacy of the LRM in fitting the observations. The GOF test is defined as an evaluation of how well the estimated outcomes agree with the observed data. Two techniques may be used to construct the GOF test statistics of chi-square type. The first technique is based on ungrouped observations. This technique is not preferred in the LRM for many reasons including that the obtained distribution and values are incorrect. The second technique is the preferred technique where it is based on grouping the observations. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/7422 | |
| dc.language.iso | en | en_US |
| dc.publisher | Universiti Sains Malaysia | en_US |
| dc.subject | Assess The Goodness-Of-Fit | en_US |
| dc.subject | Logistic Regression Models | en_US |
| dc.title | New Test Statistics To Assess The Goodness-Of-Fit Of Logistic Regression Models | en_US |
| dc.type | Thesis | en_US |
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