Check For Null Values In Sas
Check For Null Values In Sas
In order to match any missing values in the SAS data set, SAS includes a check for null values in the query that it passes to DB2. Often, this check results in DB2 performing a full-table scan, which occurs even if the DB2 column has been defined as NOT NULL.
If the DB2 column is defined as NOT NULL or you know that your SAS variable does not contain any missing values, you can work around this problem by using the LIBNAME or DBNULLKEYS= data set option and setting it to NO.
We can also look at the number of missing values in each observation. For example, we can use SAS function cmiss to store the number of missing values from both numeric and character variables in each observation.
We can also look at the patterns of missing values. By default the MI procedure will output missing data patterns for the variables in the specified datasets. If no var statementis specified Proc MI will output a table for the all the variables in a dataset. The ods select statement tells SAS to only output the "Missing Data Patterns" table.
According to the SAS manual, if the sample size is over 2000, the Kolmgorov test should be used. If the sample size is less than 2000, the Shapiro test is better. The null hypothesis of a normality test is that there is no significant departure from normality. When the p is more than .05, it fails to reject the null hypothesis and thus the assumption holds.
The calculated Χ2 value is then compared to the critical value from the Χ2 distribution table with degrees of freedom df = (R - 1)(C - 1) and chosen confidence level. If the calculated Χ2 value > critical Χ2 value, then we reject the null hypothesis.
Since the p-value is less than our chosen significance level α = 0.05, we can reject the null hypothesis, and conclude that there is an association between class rank and whether or not students live on-campus.
Responses to a questionnaire, for example, could be missing for one of several reasons(Refused, illness, Dead, not home). By using special missing values, each of these can betabulated separately, but the variables are still treated as missing by SAS in dataanalysis.
Formulate the null hypothesis H0 (commonly, that the observations are the result of pure chance) and the alternative hypothesis H1 (commonly, that the observations show a real effect combined with a component of chance variation).
Compute the P-value, which is the probability that a test statistic at least as significant as the one observed would be obtained assuming that the null hypothesis were true. The smaller the P-value, the stronger the evidence against the null hypothesis.
Equivalently, the null hypothesis can be stated as the \(k\) predictor terms associated with the omitted coefficients have no relationship with the response, given the remaining predictor terms are already in the model. If we fit both models, we can compute the likelihood-ratio test (LRT) statistic:
where \(L_0\) and \(L_1\) are the max likelihood values for the reduced and full models, respectively. The degrees of freedom would be \(k\), the number of coefficients in question. The p-value is the area under the \(\chi^2_k\) curve to the right of \( G^2)\).
Large chi-square statistics lead to small p-values and provide evidence against the intercept-only model in favor of the current model. The Wald test is based on asymptotic normality of ML estimates of \(\beta\)s. Rather than using the Wald, most statisticians would prefer the LR test. If these three tests agree, that is evidence that the large-sample approximations are working well and the results are trustworthy. If the results from the three tests disagree, most statisticians would tend to trust the likelihood-ratio test more than the other two.
In our example, the "intercept only" model or the null model says that student's smoking is unrelated to parents' smoking habits. Thus the test of the global null hypothesis \(\beta_1=0\) is equivalent to the usual test for indepen