This paper studied the spatial variability of soil water in 0～90 cm (divided into 9 layers) at the thick-covered dark loessial soil on the east of Gansu rainfed highland, by appiing the classical statistics method and geostatistics within m~2: 5×5 range m~2: 1×1 grid at 36 observation points.
Since the deficiency of classical statistics on analyzing the spatial variation randomness of parameters is made up by the used method the spatial structural information and the best spatial estimation can be obtained,which makes the analysis and assessment tend to be rational.
In order to reduce the total number of the testing cases but not to decrease the confidence level of the testing results for the reliability demonstration of safety-critical software, a new method which uses Bayesian inference with prior knowledge dynamic integration is presented on the basis of analyzing the classical statistical hypothesis testing and the ignorance prior Bayesian method.
【Method】Grid and line sampling methods were used to sample soils respectively, and classical statistical tools were applied to analyze the spatial variability characters of soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), total potassium (TK), available nitrogen (AN), available phosphorus (AP), available potassium (AK), pH, and C/N.
The third, presents the global positioning system (GPS) and the basic field soil nutrient data using the differential global positioning system (DGPS), then analyze the data with the method of classic statistics and the method of geostatistics to study the spatial variability of field soil nutrients.
Because there is not a complete set of the systematic theory about the precision analysis from the classic statistics theory to small sample experimentation analysis, so the studies of precision analysis theory in this thesis have some practical meaning and application values.
This paper introduces Bayesian theory, and uses it to analyse type A evaluation of uncertainty in measurement. By contrast with classic statistics methods, it can fully utilize the information of the history measurement data. Due to the much more and useful information, it makes the evaluation of uncertainty be more rational.
The classic statistics method can not make use of the history test information and the traditional Bayes method can not differentiate between the history test information and the current information. In order to overcome the limitation of the traditional method, a Bayes assessment model for reliability of success or failure product in the development phases is established.
Nowadays the classical statistic theory has been already quite perfect, the classical regression is always limited to accurate data-processing, however in real life the observations are fuzzy data, so the fuzzy data- processing becomes a new research field.
The research of the classical statistic theory has been already quite systematic and complete at present, however, the classical statistic theory also exits some limitation, and can not well solve all practical problems.
Many existing analysis methods prescribed in those databases and corresponding safety indictors are based on classical statistical analysis, and their applicability are considerably restricted by the requirement of normality.
Classical statistical analysis based on a certain hypercomplex multivariate normal distribution
The database has partly to be considered as rather unsharp or fuzzy and makes classical statistical analysis very difficult.
Classical statistical analysis revealed complex nonlinear changes in channel dwell times and unitary conductance of single Na+ channels as a function of conditioning membrane depolarization.
Classical statistical analysis results indicated that the variability of SOC was moderate (CV = 0.30).
The spatial variability of total soil nematodes and trophic groups in bare and fallow plots in Shenyang Experimental Station of Ecology, Chinese Academy of Sciences was examined using geostatistics combined with classic statistics.
An example of the solution of a geological problem shows that methods of classic statistics are not good and the reductive method is much better.
In the next section, we briefly describe the properties of the macroeconomic time series and classic statistics that will be used in this study.