| Summary: | 碩士 === 中原大學 === 應用數學研究所 === 94 === Linear Regression Model is a model that is meant to find out the statistic of testing hypothesis on the basis oformal distribution and then to calculate the confidence interval. Therefore, the importance of testing if the data match
normal distribution cannot be overemphasized.
Generally speaking, there are two methods of testing normal distribution: one is normal probability plot and the other is Shapiro-Wilks test. However, different people may make different conclusions when they are explaining the same draw due to he different methods they use. As a result, Shapiro-Wilks test, which is based on testing, reveals its importance.Once rejection null hypothesis happens, it means that the data do not match normal distribution. On the other hand,if rejection null hypothesis does not happen, it does not mean that the data come from normal distribution.
This research is meant to investigate the similarity between the present data and normal distribution, and provide the users of linear regression model with a more reliable method.The proposed method provided in this thesis can also apply to judging the degree of similarity between the present data and other distributions, from which the users can clearly know what distribution the data probably come from.
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