Dispersal diagram with correlation between hemoglobin measurements from two data methods in Table 3 and Figure 1. The dotted line is a trend line (the line of the smallest squares) by the observed values, and the correlation coefficient is 0.98. The different points, however, are very far from the perfect line of concordance (solid black line) A positive correlation between mitotic and positivity pcna (p < 0.05) and between low levels of PCNA and the absence of p53 overexpression (p < 0.05) in both subgroups of tumors. An inverse correlation was found between overexpression of ER and the onset of metastasis or death from disease (p < 0.05). High levels of PCNA and p53 overexpression were directly related to metastasis or death (p < 0.05) and lack of ER expression (p < 0.01). In particular, OGCTJ had significant p53 overexpression and high PCNA rates. In all LTCOs, there was an inverse correlation between high expression of ER and overexpression of p53 and PCNA (p < 0.05). It is important to note that of the four sections studied for each tumor, we found minimal and insignificant differences between censuses. This finding indicates the absence of significant intratumoral heterogeneity in the OCGT of our series. There was no significant difference in the clinical behavior of tumors for elderly patients, endocrine status or LTD.
Overall coincidence coincidence is probably the probability that they agreed on either yes or no, i.e.: As another example, as chi-square and Kappa compares, consider the distribution of the agreements shown in Table IV. Here, at 2 – 6.25 (p < 0.02), while n – 0.20. Although the chi-square is significant, Kappa`s value indicates little match. In the absence of rating guidelines, ratings are increasingly influenced by the experimenter, i.e. by a trend in credit ratings that drift towards what he expects from the advisor. In processes with repeated actions, the correction of board drift can be addressed by regularly retraining to ensure that advisors understand the guidelines and measurement objectives. if fO is the number of agreements observed between advisors, fE is the number of agreements expected at random and N is the total number of observations. Essentially, kappa answers the question: how many of the values that are not expected to be (fortuitous) are actually agreements? Kappa statistics showed a fair agreement between self-report and positive findings of tetrahydrocannabinol (THC) in oral fluid and blood, and a moderate match with 11-nor-9-carboxy-delta-9-tetrahydrocannabinol (THCCOOH) in the blood.