Predicting On-Time Graduation with Binary Logistic Regression and Backward Elimination, Exercises of Research Methodology

The use of binary logistic regression model to predict graduation on time based on a set of independent variables. The document also discusses the process of backward elimination, where insignificant variables are removed to improve the model's fit. Logistic regression is chosen due to the dichotomous nature of the dependent variable.

Typology: Exercises

2018/2019

Uploaded on 11/02/2019

sh_aliahusna
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Binary Logistic Regression Model
We choose to use logistic regression model because the
dependent variable (graduation on time) is
dichotomous(binary).
The goal of logistic regression is to find the best fitting
model.
To describe the relationship between the dichotomous
characteristic of interest (dependent variable = response or
outcome variable) and a set of independent (predictor or
explanatory) variable.
In this study, this regression is use to to predict the sample
is graduate on time or not.
The logistic regression be a selection method to include or
exclude the variables to be the best model for this study.
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Binary Logistic Regression Model

  • (^) We choose to use logistic regression model because the dependent variable (graduation on time) is dichotomous(binary).
  • The goal of logistic regression is to find the best fitting model.
  • (^) To describe the relationship between the dichotomous characteristic of interest (dependent variable = response or outcome variable) and a set of independent (predictor or explanatory) variable.
  • (^) In this study, this regression is use to to predict the sample is graduate on time or not.
  • (^) The logistic regression be a selection method to include or

Backward Elimination

  • (^) Backward elimination is the reverse process.
  • (^) All the independent variables are entered into the equation first and each one is deleted one at a time if they do not contribute to the regression equation.
  • (^) At the end,only the significance variables that left.
  • (^) This will make the model as the best model.