"do is to make a table with all possible scores along the bottom (x axis) and the number of times it isfound that the record score vertical (Y axis) as a bar. This goal is simply difficult to make statisticalanalyses which were more forms of digital data abstraction.A graphical representation similar to a bar graph in the structure that organizes a group of data pointsin intervals specified by the user. The histogram data series was condensed into an easy to interpretvisual Taking many data elements grouped in ranges or logical containers. Histograms are commonlyused in statistics to show how a certain variable appears in a specific range.A scatter plot is a graph that helps you visualize the relationship between two variables. It can be usedto check if a variable is linked to another year critical variables and effective way to find thecommuniqué relationship. (Mann, Jones, Joiner and Snee) A scatter diagram is used when there is avariable that is under the control of the experimenter. If a parameter that increases and decreasessystematically by other reviews is the independent variable and is generally called Expired plottedalong the horizontal axis. The dependent variable is usually plotted along the vertical axis. Ascattergram can offer various types of correlations between variables with a confidence interval ofsomeThe degree of correlation is the combination of two or more variables. These variables explainwhether or not they usually move together. The correlation is a term that refers to the strength of arelationship between the two variables. A strong correlation of higher gold means that two clusters ofvariables have a strong relationship with the other, while a low, low correlation of gold means thegrouping of variables that are not linked . Correlation coefficients between -1.00 and +1.00 can lines.The value of -1.00 represents a perfect negative correlation, while a value of 1.00 represents a perfectpositive correlation. And the value of 0.00 means that there is no grouping of the relationship betweenvariablesReviews correlation coefficient of the most commonly used type is the R Who is Pearson. Calledlinear or product-moment correlation. This analysis of the two variables analyzed white suppose thereMeasured at scales less apart. The coefficient is calculated by taking the covariance of two variablesdivided by the product of the standard deviations there. Regression analysis is to identify the relationship between a dependent variable and one or moreindependent variables. A model of the relationship is the assumptions and parameter values used todevelop estimates are the year of the estimated regression equation. Various tests are then used todetermine if the model is satisfactory. If the model is considered satisfactory, the estimated regressionequation can be used to predict the value of the variable dependent data values for the independentvariable26 CHAPTER NO 4Data AnalysisTable 1Descriptive Statistics Number Min Max Mean Std. Dev.ACP30 8 126 42.76 29.617APP 30 11 93 29.50 20.055TAT 30 0 3 2.37 .691NPT 30 0 30 5.73 8.212Valid Number304.1 Descriptive StatisticsInterpretationThis table shows the descriptive statistics show the big picture of all four variables. Number ofobservations for each variable is 30. In the above table the average values and standard deviationvalues of all four variables were demonstrated. Average value provides the concept of the main trendof the values of a variable. We note the previous result to assess the average response rate ordefendant Then we come to know the average of different variables such as debtor days (mean:42.76), average payment (average: 29.50) Net total assets Affairs (average: 2.37) and margin(average: 5.73) earnings. The standard deviation gives the idea of the spread of STIs variable thevalues of the mean value. DSO (SD: 29617), average payment (SD: 20.055) Total assets (SD: 691)and net profit margin (DE: 8212) .The variable is the total value of the asset rotation standarddeviation (SD: 691), which is the lowest compared to another value of variable values. DSO of thevalue of the standard deviation (SD: 29617) what is quite high compared to other variables .The valuebetween the minimum value of 0 and a table esta 11.in the maximum value data between 3-126 The27 average value of data values between 1.27 to 41.67.The standard deviation value of the data betweenthe values of A 691 29 6174.2Unit pool test:In this case the result of 10 textile industries of 03 years for the presented in the table.4.2.1 Unit pool test of textile industries panel dataCross Levin & Chu ADF - Fisher & Section ObsChi-Square Variables Statistics Level Statistics LevelNPM -6.47435* 0.00 54.84143* 0.00 5 145APP -7.31976* 0.00 73.2597* 0.00 5 116ACP -9.00145* 0.00 84.3827* 0.00 5 138TAT -6.00816* 0.00 54.4498* 0.00 5 1464.2.2 Interpretation: In this case the unit pool test is presented in the table. The unit pool test is use to find out is the data islevel and suitable for Panel data analysis or not. The net profit margin NPM has 145 observations and5 cross sections in the panel. The NPM is level at 0.00 with the statistics of -6.47435% in Levin, Lin& Chu test and also in ADF-Fisher Chi-square test at 0.00 with the statistics of 54.8143%. TheAverage payment period APP has 116 observations and 5 cross sections in the panel. The APP is levelat 0.00 with the statistics of -7.31976% in Levin, Lin & Chu test and also in ADF-Fisher Chi-squaretest at 0.00 with the statistics of 73.2597%. The Average collection period ACP has 138 observationsand 5 cross sections in the panel. The ACP is level at 0.00 with the statistics of -9.00145% in Levin,Lin & Chu test and also in ADF-Fisher Chi-square test at 0.00 with the statistics of 84.4198%.TheTotal assets turnover TAT has 146 observations and 5 cross sections in the panel. The TAT is level at0.00 with the statistics of -6.00816% in Levin, Lin & Chu test and also in ADF-Fisher Chi-square test28 at 0.00 with the statistics of 54.4498%. As the above results mentioned in the table the panel data islevel and suitable for Panel data analysis. 4.3 Panel data analysisIn this case the result of 10 textile industries of 03 years for the presented in the table 4.3.1 Panel data analysis for south Asian countries Model NPMVariables Fixed RandomAPP 0.653645* 0.588155*(3.05) (3.20)ACP 0.019619 0.011051(0.62) (0.42)TAT -0.065291 -0.068918(-1.63) (-1.82)Constant 4.922567* 5.105930*(8.02) (7.94)Observation 145 145Adjusted R-square 0.12816 0.0480434.3.2 Interpretation:In this case the panel data analysis results are presented in the table. In the model the NPM isdependent variable and three APP, ACP and TAT are independent variables. In first technique (fixed)the first variable Average payment period APP is significant as the t-value shown in the table isgreater than 3 (3.05) with the co-efficient value of 0.653645. It means 2% change in NPM brings the0.653645% change in NPM dependent variable and there is a positive relationship between APP andNPM. The next independent variable Average collection period ACP is not significant as the t-valueshown in the table is less than 3(0.62) with the co-efficient value of 0.019619. It means there is norelationship between the ACP and NPM. The next variable Total assets turnover TAT is also not29 significant as the t-value is less than 2(-1.63) with the co-efficient value of -0.065291. It means thereis no relationship between TAT and NPM.In second technique (random) the first variable Average payment period APP is significant as the t- value shown in the table is greater than 3 (3.20) with the co-efficient value of 0.588155. It means 1%change in NPM brings the 0.588155% change in NPM dependent variable and there is a positiverelationship between APP and NPM. The next independent variable Average collection period ACP isnot significant as the t-value shown in the table is less than 2(0.42) with the co-efficient value of0.011051. It means there is no relationship between the ACP and NPM. The next variable Total assetsturnover TAT is also not significant as the t-value is less than 2(-1.82) with the co-efficient value of - 0.068918. It means there is no relationship between TAT and NPM.According to the above criteria in this case, the researchers applied two fixed effects and randomeffects model and then choose the most suitable of them by applying the Hausman test. Researchereffects in the model set a Hausman test is significant.30 "