Statistics p value

Parameter Estimate t Statistic p Value Parameter Estimate t Statistic p Value... 

Coefficients Standard Error r-Statistic p- Value Lower 95% Upper 95%... 

P-values from the PPC method that are -0.5 indicate that the model adequately describes the data with approximately 50% of the predictions being more extreme or equal to the observed test statistic. P-values close to 0 or 1 indicate some bias in the model predictions and in some circumstances may be used as evidence to reject the candidate model. There is no fixed value of the P-value that indicates poor model performance, although values more extreme than 0.1 or 0.9 may confer reasonable evidence against a model. 

The one-way ANOVA is useful when we study only one factor that groups the measurements (e.g., tissue type). If we, for example, want to study how electrode configuration in addition to tissue type affects the bioimpedance, we have a factorial design with two factors and may use the two-way ANOVA. The output of this test gives us the statistics (F-statistic, p value) that tell us whether each of the two factors have a significant effect on the bioimpedance. In addition, the two-way ANOVA can test whether there is a significant interaction between the two factors the difference in bioimpedance... 

The first step in evaluating the results of the Ordered Logit model is to review the model performance / fitting criteria. The model fitting information indicates the parameters for which the model-fit is calculated. There are four variables that evaluate the goodness of fit Chi-square statistics, p-value , log-likelihood value , and R-square. The model fitting... 

If the performance of the candidate, based on the results of the chosen properties, is consistently greater than the performance of the conttol, the statistical p-value can be considered acceptable at values lower than 0.05, since this would mean that the candidate material can be expected to meet the requirements with the same confidence as the confiol material (Fig. 1.5). That is, the performance is different, but the candidate is outperforming the control. 

If the performance of the candidate is consistently lower than the performance of the control, the statistical p-value must be adhered to, since any value lower than 0.05 would suggest that the probability that the candidate and control materials are... 

Statistic P-Value Statistic P-Value Statistic P-Value... 

Use zero-order Markov statistics to evaluate the probability of isotactic, syndio-tactic, and heterotactic triads for the series of p values spaced at intervals of... 

If the fractions of triads could be measured, they either would or would not lie on a single vertical line in Fig. 7.9. If they did occur at a single value of p, this would not only give the value of p (which could be obtained from the fraction of one kind of triad), but would also prove the statistics assumed. If the fractions were not consistent with a single p value, higher-order Markov statistics are indicated. 

The sample labeled atactic in Fig. 7.10 was prepared by a free-radical mechanism and, hence, is expected to follow zero-order Markov statistics. As a test of this, we examine Fig. 7.9 to see whether the values of p, P, and Pj, which are given by the fractions in Table 7.9, agree with a single set of p values. When this is done, it is apparent that these proportions are consistent with this type... 

The F-test indicates the dependence of die dependent variables widi the independent variables, P level indicates the statistical significance of the correlafion(Table 4). The F-test results for the relation of the amount of ortho methylol phenols with F/P molar ratio and the reaction temperature were low, however, for the OH/P wt %, the F-test result was very significant, indicating a clear dependence of ortho methylol phenols on the OH/P wt %. It can also be seen that P level values for the relation between the amount of ortho methylol phenols and both F/P molar ratio and reaction temperature are above the set P value of 0.05, while for the OH/P wt%, the P value is under the set value. This data indicated that the relations of dependent variables ortho methylol phenols with independent variable OH/P wt% is statistically significant at the 0.05 significmitx level, while tiie relation of dependent variables ortho methylol phenols with F/P molar ratio and reaction temperature are not statistically significant. 

The significance level relates to the risk of designating a chance occurrence as statistically significant. Usually a 5% level is utilized for testing treatment effects. If a p-value of 0.04 is reported for a treatment effect, this means that there is only a 4% chance that the difference in response between the active and control treatments occurred due to chance. Keep in mind, however, that if many tests are run in a trial, it is entirely possible that one or two might be significant due to chance. As an extreme example, consider a study in which 100 statistical tests are run. We would expect five of those tests to show significance with a p-value of 0.05 or less due to chance. Therefore, it is essential to specify the main tests to be run in the protocol. Any tests that are conducted after the trial has been completed should be clearly labeled as post hoc exploratory analyses. 

There are several items about the body of the table to mention here. First, there is no p-value column, as PROC TABULATE generally produces only descriptive statistics. Second, the styles of the n (%) statistics are oriented with n and % in different rows when we wanted n (%) in the same row in the same cell. ... 

APPEND AGE DESCRIPTIVE STATISTICS TO AGE P VALUE ROW AND CREATE AGE DESCRIPTIVE STATISTIC ROW LABELS. data age ... 

APPEND gender descriptive statistics to gender p value row AND CREATE GENDER DESCRIPTIVE STATISTIC ROW LABELS. data gender ... 

Because age is not normally distributed here, the Wilcoxon signed rank test is used to calculate the p-value and is placed into a data set called pvalue. (Inferential statistics are discussed further in Chapter 7.)... 

The output data set pvalue contains numerous Wilcoxon test statistics. Assuming that you want the two-sided normal approximation test p-value, the variable in the pvalue data set that you want is called P2 WIL. ... 

The OUTSTAT= output data set pvalue contains the p-value in the PROB variable. If you have multiple predictor variables, you need to use the PROC GLM ODS data set OverallANOVA to get the overall model p-value from the ProbF variable. These output data sets contain other variables, such as the degrees of freedom, sum of squares, mean square, and F statistic, if you need them for an ANOVA table presentation. 

Note that with PROC PHREG all covariates need to be numeric, so treatment and gender need to be numeric. The p-values and hazard ratios that are useful for your statistical tables can be found in the ProbChiSq and HazardRatio variables, respectively, in the pvalue data set. 

Statistical analysis. Values are given as the mean SEM. Data are represented as averages of independent experiments, performed in duplicate or triplicate. Statistical analyses were done using the Student s t-test. P < 0.05 was considered statistically significant. 

In summary, for any stated value of the population correlation (p) the z statistic is denoted as Z(p), and the corresponding correlation confidence limits can be determined. For our example, the Z statistic of 0.6366 corresponding to the lower correlation coefficient confidence limit is shown in the graphic below (Graphic 60-6a) as having a p value of 0.562575 this represents the lower confidence limit for the correlation coefficient for this example. 

Draper and Smith [1] discuss the application of DW to the analysis of residuals from a calibration their discussion is based on the fundamental work of Durbin, et al in the references listed at the beginning of this chapter. While we cannot reproduce their entire discussion here, at the heart of it is the fact that there are many kinds of serial correlation, including linear, quadratic and higher order. As Draper and Smith show (on p. 64), the linear correlation between the residuals from the calibration data and the predicted values from that calibration model is zero. Therefore if the sample data is ordered according to the analyte values predicted from the calibration model, a statistically significant value of the Durbin-Watson statistic for the residuals in indicative of high-order serial correlation, that is nonlinearity. 

Statistical analysis For statistical analysis of the behavioral tests an analysis of variance (two-way ANOVA) was used. For the symptomatology a Fisher exact probability test or an unpaired t-test with Welch s correction was used. In all tests p values <0.05 were considered significant. 

---