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Table 4. Logistic regression analysis

Table 4 Logistic regression analysis

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Operational definition of variables wmenanneypenees’ Ss * UPeevv The data used in this research is obtained from the company's financial statements issuec by the company which can be accessed on the Indonesian stock exchange website. Dat reliability is obtained by relying on data sources. In this case the data comes from financia reports issued by the regulator, namely the Indonesian stock exchange. Another source 0! reliability is that the financial statements that are used as sources have been audited by ar audit firm. It is assumed that the opinion of the public accountant reflects the quality of the financial statements. This assumption can be made because public accountants are bound by < code of ethics and laws and regulations. The amount of data obtained is shown in table |] below.
Operational definition of variables wmenanneypenees’ Ss * UPeevv The data used in this research is obtained from the company's financial statements issuec by the company which can be accessed on the Indonesian stock exchange website. Dat reliability is obtained by relying on data sources. In this case the data comes from financia reports issued by the regulator, namely the Indonesian stock exchange. Another source 0! reliability is that the financial statements that are used as sources have been audited by ar audit firm. It is assumed that the opinion of the public accountant reflects the quality of the financial statements. This assumption can be made because public accountants are bound by < code of ethics and laws and regulations. The amount of data obtained is shown in table |] below.
Multicollinearity test result Table 2. Descriptive statistics In addition to normally distributed data, so that the independent variables can be related to the dependent variable, the independent variables must not have a too close relationship. When the independent variables have a relationship that is too tight, the error value will tend to be too low, making the model appears to have a small error value outside the expected value. Thus, as if the model has a high degree of suitability. This problem of multicollinearity can be detected by analyzing the correlation between these variables. Furthermore, the collection of error correlations between these variables is combined in a matrix called the correlation matrix.
Multicollinearity test result Table 2. Descriptive statistics In addition to normally distributed data, so that the independent variables can be related to the dependent variable, the independent variables must not have a too close relationship. When the independent variables have a relationship that is too tight, the error value will tend to be too low, making the model appears to have a small error value outside the expected value. Thus, as if the model has a high degree of suitability. This problem of multicollinearity can be detected by analyzing the correlation between these variables. Furthermore, the collection of error correlations between these variables is combined in a matrix called the correlation matrix.
Table 3. Multicollinearity table
Table 3. Multicollinearity table
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