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Logit & Probit Regression.  Read the data use clear  Describe the data Codebook Summarize Tabulate.

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Presentation on theme: "Logit & Probit Regression.  Read the data use clear  Describe the data Codebook Summarize Tabulate."— Presentation transcript:

1 Logit & Probit Regression

2  Read the data use clear  Describe the data Codebook Summarize Tabulate Make graphs

3 Description of the Data  Dependent variable admit (0/1)  Independent variables gre, student's Graduate Record Exam score; gpa, student's grade point average topnotch, a 0/1 variable where 1 indicates that the undergraduate institution was "top notch" and 0 indicates that it is not.

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7 Some Possible Estimation Strategies  OLS regression: this analysis is problematic because the assumptions of OLS are violated when using a binary response variable.  Logit or Probit model  Probit analysis will produce results similar logit regression. The choice of probit versus logit regression depends largely on individual preferences.

8 Using Logit Model  Before running logit, check to see if any cells (created by the crosstab of our categorical and response variables) are empty or particularly small.  If this occurs, there may be difficulty to run the logit model.

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12 None of the cells are too small or empty (has no cases), so we will run our logit model.

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16  The log likelihood ( ) can be used in comparisons of nested models.  Observations (400) fewer observations would have been used if any of our variables had missing values.  The likelihood ratio chi-square (21.85) with a p-value (0.0001) our model as a whole fits significantly better than an empty model.

17  The interpretation of the coefficients can be awkward. For example, for a one unit increase in gpa, the log odds of being admitted to graduate school (vs. not being admitted) increases by.667.  For this reason, many researchers prefer to exponentiate the coefficients and interpret them as odds-ratios.  Stata will do this computation for you if you use the or option, illustrated below.

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21  Now we can say that for a one unit increase in gpa, the odds of being admitted to graduate school (vs. not being admitted) increased by a factor of  Since GRE scores increase only in units of 10, we can take the odds ratio and raise it to the 10th power, e.g ^ 10 = , and say for a 10 unit increase in GRE score, the odds of admission to graduate school increased by a factor of

22  We will start with prtab. (downloaded from internet)  This command can be used with either a categorical variable or a continuous variable and shows the predicted probability of the outcome being 1 for all levels of the specified predictor.  The predicted probability of being accepted into the graduate program is 0.29 if the undergraduate institution was not "top notch" and.39 if it was, while gre and gpa are held constant at their mean value.

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24  Below we can see that the predicted probability of getting accepted is only.15 if one's GRE score is 220 and increases to.429 if one's GRE score is 800  Notice that gpa and topnotch are held constant at their mean, indicated at the end of the output.

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26  We can use the prvalue command to obtain the predicted probabilities when gpa is set to specific values: 2, 3 and 4.  When one's GPA is 2, the predicted probability of being accepted is only.149, and.85 of not being accepted.  When GPA is 3, the probability of being accepted increases to.255  When one's GPA is 4, the predicted probability of being accepted is.40.

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28 Self-Assessment  Probit regression ( regress admit1 on topnotch, gre, gpa)  Probit regression (reporting odd ratio)  Obtain the predicted probability for different topnotch values  Obtain the predicted probabilities when gpa is set to specific values: 2, 3 and 4.


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