【導讀】研究所，正式推出了SAS軟件。SAS是用于決策支持的大型集成信息。SAS現(xiàn)在的版本為版，大。經過多年的發(fā)展，SAS已被全世界120多個國家和地區(qū)的。生、生產、運輸、通訊、政府和教育科研等領域。在英美等國，能熟。練使用SAS進行統(tǒng)計分析是許多公司和科研機構選材的條件之一。數據處理和統(tǒng)計分析領域，SAS系統(tǒng)被譽為國際上的標準軟件系統(tǒng)，并在96～97年度被評選為建立數據庫的首選產品。在此僅舉一例如下：在以苛刻嚴格著稱于世的美國FDA新。此可見SAS的權威地位。基本部分是BASESAS模塊。調用其他SAS模塊和產品。也就是說，SAS系統(tǒng)的運行，首先必須啟。計計算功能以外，還是SAS系統(tǒng)的中央調度室。了各類概率分析函數、分位數函數、樣本統(tǒng)計函數和隨機數生成函數，使用戶能方便地實現(xiàn)特殊統(tǒng)計要求。SAS是由大型機系統(tǒng)發(fā)展而來，其核心操作方式就是程序驅動，關鍵字、名字、特殊字符和運算符等按照語法規(guī)則排列組成SAS語句，可成為一段SAS程序，只要能完成一個完整的功能。組成員只有兩個群體。

　　

【正文】 search but it is not remended for theory testing (Menard 1995). Theory testing is the testing of apriori theories or hypotheses of the relationships between variables. Exploratory testing makes no apriori assumptions regarding the relationships between the variables, thus the goal is to discover relationships. Backward stepwise regression appears to be the preferred method of exploratory analyses, where the analysis begins with a full or saturated model and variables are eliminated from the model in an iterative process. The fit of the model is tested after the elimination of each variable to ensure that the model still adequately fits the no more variables can be eliminated from the model, the analysis has been pleted. There are two main uses of logistic regression. The first is the prediction of group membership. Since logistic regression calculates the probability or success over the probability of failure, the results of the analysis are in the form of an odds ratio. For example, logistic regression is often used in epidemiological studies where the result of the analysis is the probability of developing cancer after controlling for other associated risks. Logistic regression also provides knowledge of the relationships and strengths among the variables (., smoking 10 packs a day puts you at a higher risk for developing cancer than working in an asbestos mine). The process by which coefficients are tested for significance for inclusion or elimination from the model involves several different techniques. Each of these will be discussed below. Wald Test: A Wald test is used to test the statistical significance of each coefficient (?) in the model. A Wald test calculates a Z statistic, which is: .BSEZ ? This z value is then squared, yielding a Wald statistic with a chisquare distribution. However, several authors have identified problems with the use of the Wald statistic. Menard (1995) warns that for large coefficients, standard error is inflated, lowering the Wald statistic (chisquare) value. Agresti (1996) states that the likelihoodratio test is more reliable for small sample sizes than the Wald test. LikelihoodRatio Test: The likelihoodratio test uses the ratio of the maximized value of the likelihood function for the full model (L1) over the maximized value of the likelihood function for the simpler model (L0). The likelihoodratio test statistic equals: ? ? ? ? ? ? ? ?01 0 1 0 12 l o g 2 l o g l o g 2LL L L L L? ? ? ? ? ? ????? This log transformation of the likelihood functions yields a chisquared statistic. This is the remended test statistic to use when building a model through backward stepwise elimination. HosmerLemshow Goodness of Fit Test: The HosmerLemshow statistic evaluates the goodnessoffit by creating 10 ordered groups of subjects and then pares the number actually in the each group (observed) to the number predicted by the logistic regression model (predicted). Thus, the test statistic is a chisquare statistic with a desirable oute of nonsignificance, indicating that the model prediction does not significantly differ from the observed. The 10 ordered groups are created based on their estimated probability。 those with estimated probability below form one group, and so on, up to those with probability to . Each of these categories is further divided into two groups based on the actual observed oute variable (success, failure). The expected frequencies for each of the cells are obtained from the the model is good, then most of the subjects with success are classified in the higher deciles of risk and those with failure in the lower deciles of risk.