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
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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
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The basic assumptions for t-tests - one random sampling, independent measurements, normal distribution and equal variance (Jowncend 2002), the t-tests were further strengthened by the use of the Bonferroni alteration test which uses t-tests to perform pair-wise comparison between group means. It reins overall error rate by setting the error rate for each test, to the experiment-wise error rate divided by the total number of tests. Hence, the observed significance level is adjusted and the multiple comparisons are being made. In this association the t-test with hypothesized mean value three is applied and the results were obtained.

Analysis of Variance (ANOVA)

ANOVA allows for the study of a single factor or several factors, but will only measure one variable (Bray and Maxwell 1985& Towncend 2002). An ANOVA works by measuring the variance of the population in two different ways; the first is by noting the spread of values within the sample; the second is by the spread out of the sample means. If the samples are from identical populations, these methods will give identical results. The basic assumptions for the test of ANOVA are random sampling independent measurements, normal distribution and equal variance (Jowncend, 2002).

Age, Gender, Eligibility, Education qualification, Sources of income, Family size,