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Table 16. Variables in the Equation The table above is a table of hypothesis testing results processed using binary logistic regression. The results of this test are used to see whether the variables of inventory intensity (I.P.), current ratio (R.L.), financial leverage (F.L.), and gross profit margin (MLK) affect the selection of inventory valuation methods with a significance level of <0.05. After testing the feasibility of the logistic regression model and obtaining a fit model, so the next step is to test the hypothesis to answer the objectives of this study.

Table 16 Variables in the Equation The table above is a table of hypothesis testing results processed using binary logistic regression. The results of this test are used to see whether the variables of inventory intensity (I.P.), current ratio (R.L.), financial leverage (F.L.), and gross profit margin (MLK) affect the selection of inventory valuation methods with a significance level of <0.05. After testing the feasibility of the logistic regression model and obtaining a fit model, so the next step is to test the hypothesis to answer the objectives of this study.

Related Figures (17)

Figure |. Percent (%) of Inventory by Industry Sector (Source: Processed Data)
Figure |. Percent (%) of Inventory by Industry Sector (Source: Processed Data)
Analysis Method
Analysis Method
Table 2. Analysis The data used in this study is the financial statement data of manufacturing companies in the form of balance sheets and income statements published annually on December 31. There are two purposes of this research. The first purpose will be tested using a case question. The second purpose will be tested with statistical tools using SPSS version 24. Below is the table displaying the sample that has been successfully selected for analysis:
Table 2. Analysis The data used in this study is the financial statement data of manufacturing companies in the form of balance sheets and income statements published annually on December 31. There are two purposes of this research. The first purpose will be tested using a case question. The second purpose will be tested with statistical tools using SPSS version 24. Below is the table displaying the sample that has been successfully selected for analysis:
Inventory records of manufacturing companies engaged in trading during December 2020 are as follows:
Inventory records of manufacturing companies engaged in trading during December 2020 are as follows:
Table 4. Inventory Additional information
Table 4. Inventory Additional information
Table 5. Inventory Valuation Method FIFO
Table 5. Inventory Valuation Method FIFO
Inventory Valuation Method Using Weighted Averaged
Inventory Valuation Method Using Weighted Averaged
Table 7. Calculates
Table 7. Calculates
Table 9. Descriptive Statistical
Table 9. Descriptive Statistical
Table 10. Dependent Variable Encoding
Table 10. Dependent Variable Encoding
Table 13. /teration History?®°?P The independent variable (see: Block 1) is entered into the model, and the -2 Log-Likelihood of 256,836 is obtained. Then, by calculating (df) = N — number of independent variables — 1 = 730 — 4 — 1 = 725 the degree of freedom is obtained. The value of the Chi-square table on df 725 with a probability value of 0.05 is 788,750. So the value of -2 Log-Likelihood (256.836) < Chi-Square table (788.750). Thus, the model with the independent variable is considered fit with the data.
Table 13. /teration History?®°?P The independent variable (see: Block 1) is entered into the model, and the -2 Log-Likelihood of 256,836 is obtained. Then, by calculating (df) = N — number of independent variables — 1 = 730 — 4 — 1 = 725 the degree of freedom is obtained. The value of the Chi-square table on df 725 with a probability value of 0.05 is 788,750. So the value of -2 Log-Likelihood (256.836) < Chi-Square table (788.750). Thus, the model with the independent variable is considered fit with the data.
Regression Model Feasibility Test (Hosmer and Lemeshow’s Goodness of Fit) Nagelkerke R Square is a modification of the Cox and Snell coefficient, which is a measure that tries to imitate R Square in the multiple regression method. The Nagelkerke R Square value is 0.203, which means the dependent variable can be explained by the independent variable’s variability of 20.3%, and the rest or 79.7% (100% - 20.3%) is explained by other variables.
Regression Model Feasibility Test (Hosmer and Lemeshow’s Goodness of Fit) Nagelkerke R Square is a modification of the Cox and Snell coefficient, which is a measure that tries to imitate R Square in the multiple regression method. The Nagelkerke R Square value is 0.203, which means the dependent variable can be explained by the independent variable’s variability of 20.3%, and the rest or 79.7% (100% - 20.3%) is explained by other variables.
Based on the regression model’s feasibility test results, the chi-square value is 7.530, the degree of freedom (df) value is 8, and the significance probability value is 0.481. The chi-square table for df 8 at a significance of 0.05 is 15.51. Because the calculated chi-square value < chi-square table and a significance of 0.48 (0.05) indicate that this logistic regression model is accepted and feasible for analysis.
Based on the regression model’s feasibility test results, the chi-square value is 7.530, the degree of freedom (df) value is 8, and the significance probability value is 0.481. The chi-square table for df 8 at a significance of 0.05 is 15.51. Because the calculated chi-square value < chi-square table and a significance of 0.48 (0.05) indicate that this logistic regression model is accepted and feasible for analysis.
Table 17. WALD Values with Chi-Square The magnitude of the effect can be indicated by EXP (B) and the direction of the regression coefficient, indicated by the B value. A small S.E. value explains a more accurate or representative sample. The following is a comparison table for WALD values with a chi-square table.
Table 17. WALD Values with Chi-Square The magnitude of the effect can be indicated by EXP (B) and the direction of the regression coefficient, indicated by the B value. A small S.E. value explains a more accurate or representative sample. The following is a comparison table for WALD values with a chi-square table.
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Inventory Turnover
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