The luminal layer are the same to those of the carcinoma. In PIN indicates that the changes heralding the progression from PIN toĬarcinoma are produced in this layer, whereas the nuclear features of The similarities in nuclear size between PIN and carcinoma are according Proliferation from PIN was similar to that observed in adenocarcinoma. Observed in the basal stratum from normal prostate, whereas the luminal The of basal cells from PIN was similar to that was greater in PIN and adenocarcinoma than in normal Nuc was similar in both PIN and adenocarcinoma. Was significantly higher than in luminal stratum. nuc was significantly lower in normal epithelium Luminal compartments, and the nuc and measured inīoth strata. TheĮpithelium of both normal and PIN specimens was segmented in basal and Nuclear antigen labeling index (), and to compare theseĮstimates with those obtained in normal prostate and carcinoma. Relation to mean nuclear volume (nuc), and proliferating cell Quantitate the differences between basal and luminal cells of PIN in High-grade prostate intraepithelial neoplasia (PIN) is considered a
#Mean intercept analysis stereology free
#Mean intercept analysis stereology plus
The mean value of X1 for the comparison group is the intercept plus the coefficient for X2. The B value for the intercept is the mean value of X1 only for the reference group. X2 is a dummy coded predictor, and the model contains an interaction term for X1*X2. Say for example that X1 is a continuous variable centered at its mean. This is especially important to consider when the dummy coded predictor is included in an interaction term. Since the intercept is the expected mean value when X=0, it is the mean value only for the reference group (when all other X=0). Dummy coded variables have values of 0 for the reference group and 1 for the comparison group. If you have dummy variables in your model, though, the intercept has more meaning. It’s the mean value of Y at the chosen value of X. If you re-scale X so that the mean or some other meaningful value = 0 (just subtract a constant from X), now the intercept has a meaning. When X never equals 0, but you want a meaningful intercept, simply consider centering X. In market research or data science, there is usually more interest in prediction, so the intercept is more important here. You do need it to calculate predicted values, though. It’s not answering an actual research question. So whether the value of the intercept is meaningful or not, many times you’re just not interested in it. It doesn’t tell you anything about the relationship between X and Y. If so, and if X never = 0, there is no interest in the intercept. In scientific research, the purpose of a regression model is to understand the relationship between predictors and the response. Both these scenarios are common in real data. If X never equals 0, then the intercept has no intrinsic meaning. If X sometimes equals 0, the intercept is simply the expected mean value of Y at that value. Start with a regression equation with one predictor, X. Here’s the definition: the intercept (often labeled the constant) is the expected mean value of Y when all X=0. Interpreting the Intercept in a regression model isn’t always a straightforward as it looks.