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Provided by: Creascience Logistic RegressionStatistics |
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Logistic regression is a statistical tool used to assess the effect of several explanatory variables on a categorical response variable, that is a variable that can only take on a limited set of values.
This session covers <B>logistic regression </B>, a technique used whenever the response variable is binary or categorical, that is, when it can only take on a limited number of values. An example of a <B>categorical variable</B> is the severity of a disease: not severe, mildly severe, moderately severe, very severe. An example of a <B>binary variable</B> is the survival of patients who received particular treatments: yes or no.</P>
The training starts with a refresher on multiple linear regression a statistical modeling technique used to relate a continuous response variable to a set of explanatory variables. The concepts of modeling, parameter estimation, as well as interpretation of model coefficients, goodness-of-fit and validation measures will be reviewed.</P>
Why ordinary multiple linear regression is no longer the appropriate technique to use when the response variable is discussed. </P>
The most classical case of logistic regression is then covered, that is the relationship between a categorical reponse variable and a set of explanatory variables. The modeling principle underlying the method is defined (the probability of observing a given value of the response variable) and illustrated with the help of examples.
Time is also devoted to the interpretation of statistical software output so that attendees can learn how to extract the pertinent information for the analysis of data and the interpretation of results. Some indicators are specific to logistic regression: odds ratios, tests on coefficients. Goodness-of-fit and validation measures specific to logistic regression are also covered.
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The use of the regression models for prediction and the principle of variable selection are also discussed during the training session.
</P>
Ordinal logistic regression and polytomous regression are also reviewed for ordinal and nominal response variables, respectively.
</P>Finally, the different procedures available in statistical software will be listed and ways to implement the regression techniques will be discussed.
This session covers <B>logistic regression </B>, a technique used whenever the response variable is binary or categorical, that is, when it can only take on a limited number of values. An example of a <B>categorical variable</B> is the severity of a disease: not severe, mildly severe, moderately severe, very severe. An example of a <B>binary variable</B> is the survival of patients who received particular treatments: yes or no.</P>
The training starts with a refresher on multiple linear regression a statistical modeling technique used to relate a continuous response variable to a set of explanatory variables. The concepts of modeling, parameter estimation, as well as interpretation of model coefficients, goodness-of-fit and validation measures will be reviewed.</P>
Why ordinary multiple linear regression is no longer the appropriate technique to use when the response variable is discussed. </P>
The most classical case of logistic regression is then covered, that is the relationship between a categorical reponse variable and a set of explanatory variables. The modeling principle underlying the method is defined (the probability of observing a given value of the response variable) and illustrated with the help of examples.
Time is also devoted to the interpretation of statistical software output so that attendees can learn how to extract the pertinent information for the analysis of data and the interpretation of results. Some indicators are specific to logistic regression: odds ratios, tests on coefficients. Goodness-of-fit and validation measures specific to logistic regression are also covered.
</P>
The use of the regression models for prediction and the principle of variable selection are also discussed during the training session.
</P>
Ordinal logistic regression and polytomous regression are also reviewed for ordinal and nominal response variables, respectively.
</P>Finally, the different procedures available in statistical software will be listed and ways to implement the regression techniques will be discussed.
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Training
Provided by Creascience
Logistic Regression
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About The Training Provider: Creascience
Creascience - Creascience offers a wide array of training sessions in statistics aimed primarily at non-statisticians. We also provide specific training courses on statistical methods aimed at statisticians. People attend our training sessions to gain basic knowledge or to deepen some specific aspects. The training sessions are offered both in our offices and on-site, depending on the needs of the client. We...
