# Examples Of Descriptive Statistics

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It is found from table 3.3 that the calculated P value of all the Educational loan repayments factors i.e. Perceived quality, Parental influence, Intention to repay, and Students attitude, is 0.172 which is greater than 0.05 which indicates perfectly fit. Here GFI (Goodness of Fit Index) value and AGFI (Adjusted Goodness of Fit Index) value is greater than 0.9 which represent it is a good fit. The calculated CFI (Comparative Fit Index) value is 0.986 which is greater than 0.90 means that it is a perfectly fit and also it is found that RMR (Root Mean Square Residuals) 0.013 and RMSEA (Root Mean Square Error of Approximation) value is 0.040 which is less than 0.08 which indicated it is perfectly fit.

The table also reveals that the calculated
Confirmatory factor analysis. xi. SEM model

Mean and Standard Deviation
Descriptive statistics is used to describe the basic structures of the data in a study. They provide simple outlines about the sample and the measures. Together with humble graphics analysis, they form the basis of practically every quantitative analysis of data. After positioning data, we can determine frequencies, which are the base of such descriptive measures as mean and standard deviation. Standard deviation is reflected as the most useful index of variability. It is a single number that tells us the variability, or spread, of a distribution (group of scores).
Percentage Analysis
Percentage analysis comprises of reducing a series of linked amounts to a series of percentage of a assumed base or in other words percentage analysis is the technique to represent raw streams of data as a percentage for improved understanding of collected data. It is particularly useful method of stating the relative frequency of survey responses and other many times.

Simple Mean
The simple mean is the customarily used measure of central tendency used in the present day research on variables such as experiential value, satisfaction, purchase intention and