Descriptive statistics distribution

A microscopic description characterizes the structure of the pores. The objective of a pore-structure analysis is to provide a description that relates to the macroscopic or bulk flow properties. The major bulk properties that need to be correlated with pore description or characterization are the four basic parameters porosity, permeability, tortuosity and connectivity. In studying different samples of the same medium, it becomes apparent that the number of pore sizes, shapes, orientations and interconnections are enormous. Due to this complexity, pore-structure description is most often a statistical distribution of apparent pore sizes. This distribution is apparent because to convert measurements to pore sizes one must resort to models that provide average or model pore sizes. A common approach to defining a characteristic pore size distribution is to model the porous medium as a bundle of straight cylindrical or rectangular capillaries (refer to Figure 2). The diameters of the model capillaries are defined on the basis of a convenient distribution function. 

A simulation model has entities (e.g. machines, materials, people, etc.) and activities (e.g. processing, transporting, etc.). It also has a description of the logic governing each activity. For example, a processing activity can only start when a certain quantity of working material is available, a person to run the machine and an empty conveyor to take away the product. Once an activity has started, a time to completion is calculated, often using a sample from a statistical distribution. 

Descriptive statistics quantify central tendency and variance of data sets. The probability of occurrence of a value in a given population can be described in terms of the Gaussian distribution. 

The quantities AUMC and AUSC can be regarded as the first and second statistical moments of the plasma concentration curve. These two moments have an equivalent in descriptive statistics, where they define the mean and variance, respectively, in the case of a stochastic distribution of frequencies (Section 3.2). From the above considerations it appears that the statistical moment method strongly depends on numerical integration of the plasma concentration curve Cp(r) and its product with t and (r-MRT). Multiplication by t and (r-MRT) tends to amplify the errors in the plasma concentration Cp(r) at larger values of t. As a consequence, the estimation of the statistical moments critically depends on the precision of the measurement process that is used in the determination of the plasma concentration values. This contrasts with compartmental analysis, where the parameters of the model are estimated by means of least squares regression. 

For continuous variables you may be required to provide inferential statistics along with the descriptive statistics that you generate from PROC UNIVARIATE. The inferential statistics discussed here are all focused on two-sided tests of mean values and whether they differ significantly in either direction from a specified value or another population mean. Many of these tests of the mean are parametric tests that assume the variable being tested is normally distributed. Because this is often not the case with clinical trial data, we discuss substitute nonparametric tests of the population means as well. Here are some common continuous variable inferential tests and how to get the inferential statistics you need out of SAS. 

In order to determine the optimal number of compartments, literature information on small intestinal transit times was utilized. A total of over 400 human small intestinal transit time data were collected and compiled from various publications, since the small intestinal transit time is independent of dosage form, gender, age, body weight, and the presence of food [70]. Descriptive statistics showed that the mean small intestinal transit time was 199 min with a standard deviation of 78 min and a 95% confidence interval of 7 min. The data set was then analyzed by arranging the data into 14 classes, each with a width of 40 min. Figure 9 shows the distribution of this data set. 

Descriptive statistics are used to summarize the general nature of a data set. As such, the parameters describing any single group of data have two components. One of these describes the location of the data, while the other gives a measure of the dispersion of the data in and about this location. Often overlooked is the fact that the choice of which parameters are used to give these pieces of information implies a particular type of distribution for the data. 

All these models involve a description of the amorphous state in terms of statistical distributions. These models have been discussed widely in the recent literature. 

With this bold stroke, Boltzmann escaped the futile attempt to describe microscopic molecular phenomena in terms of then-known Newtonian mechanical laws. Instead, he injected an essential probabilistic element that reduces the description of the microscopic domain to a statistical distribution of microstates, i.e., alternative microscopic ways of partitioning the total macroscopic energy U and volume V among the unknown degrees of freedom of the molecular domain, all such partitionings having equal a priori probability in the absence of definite information to the contrary. 

The biomonitoring data presented in each of the national exposure reports include descriptive statistics on the distribution of blood or urine concentrations of each chemical, including geometric means and percentiles with confidence intervals (CDC 2003). Each report also includes brief toxicity profiles and information relating the findings to biological exposure indices and European reference values or ranges, if available. Additionally, the raw data from the reports are publicly available and serve as a valuable resource. 

Oja, H., Descriptive statistics for multivariate distributions, Stat. Probab. Lett., 1, 327-332, 1983. 

Table 3 Descriptive statistics of the two-dimensional microscale distributions of chlorophyll a concentration (pg l1) and seawater excess viscosity (%) for each sampling date...

The analytical plan of epidemiological studies should use descrip tive and analytical techniques in describing the sample and results. Descriptive statistics, such as frequency distributions, cross-tabulations, measures of central tendency, and variation, can help explain underlying distributions of variables and direct the assessment of appropriateness of more advanced statistical techniques. Careful weighing of study findings with respect to the design and methods helps to ensure the validity of results. 

THIS CHAPTER contains a brief description of the methods used by toxicologists at Oak Ridge National Laboratory (ORNL) to derive the U.S. Army s interim reference doses (RfDs) for GA, GB, GD, VX, sulfur mustard, and lewisite. Those methods were based on the procedures outlined by the U.S. Enviromnental Protection Agency for Superfund risk assessment guidelines (EPA 1989) and for reference concentrations (EPA 1994). An alternative method, the benchmark-dose (BD) approach (Crump 1984) is also described. Because uncertainty factors are integral to both approaches, further consideration is also given to the statistical distribution and confidence associated with them. 

In the Poisson and binomial distributions, the mean and variance are not independent quantities, and in the Poisson distribution they are equal. This is not an appropriate description of most measurements or observations, where the variance depends on the type of experiment. For example, a series of repeated weighings of an object will give an average value, but the spread of the observed values will depend on the quality and precision of the balance used. In other words, the mean and variance are independent quantities, and different two parameter statistical distribution functions are needed to describe these situations. The most celebrated such function is the Gaussian, or normal, distribution ... 

The purpose of most practical work is to observe and measure a particular characteristic of a chemical system. However, it would be extremely rare if the same value was obtained every time the characteristic was measured, or with every experimental subject. More commonly, such measurements will show variability, due to measurement error and sampling variation. Such variability can be displayed as a frequency distribution (e.g. Fig. 37.3), where the y axis shows the number of times (frequency,/) each particular value of the measured variable (T) has been obtained. Descriptive (or summary) statistics quantify aspects of the frequency distribution of a sample (Box 40.1). You can use them to condense a large data set, for presentation in figures or tables. An additional application of descriptive statistics is to provide estimates of the true values of the underlying frequency distribution of the population being sampled, allowing the significance and precision of the experimental observations to be assessed (p. 272). 

The appropriate descriptive statistics to choose will depend on both the type of data, i.e. whether quantitative, ranked or qualitative (see p. 65) and the nature of the underlying frequency distribution. 

Three important features of a frequency distribution that can be summarized by descriptive statistics are ... 

A random sample of product containers may be used for this testing. Alternately, the product may originate from various defined areas within the lyophilizer. The data gathered should be interpreted in terms of descriptive statistics. For each analytical attribute, the mean, the standard deviation, the percentiles, the extreme values, and the normality of the distribution can be determined. 

Each measure of an analysed variable, or variate, may be considered independent. By summing elements of each column vector the mean and standard deviation for each variate can be calculated (Table 7). Although these operations reduce the size of the data set to a smaller set of descriptive statistics, much relevant information can be lost. When performing any multivariate data analysis it is important that the variates are not considered in isolation but are combined to provide as complete a description of the total system as possible. Interaction between variables can be as important as the individual mean values and the distributions of the individual variates. Variables which exhibit no interaction are said to be statistically independent, as a change in the value in one variable cannot be predicted by a change in another measured variable. In many cases in analytical science the variates are not statistically independent, and some measure of their interaction is required in order to interpret the data and characterize the samples. The degree or extent of this interaction between variables can be estimated by calculating their covariances, the subject of the next section. 

The heights of the bars or columns usually represent the mean values for the various groups, and the T-shaped extension denotes the standard deviation (SD), or more commonly, the standard error of the mean (discussed in more detail in Section 7.3.2.3). Especially if the standard error of the mean is presented, this type of graph tells us very litde about the data - the only descriptive statistic is the mean. In contrast, consider the box and whisker plot (Figure 7.2) which was first presented in Tukey s book Exploratory Data Analysis. The ends of the whiskers are the maximum and minimum values. The horizontal line within the central box is the median, fhe value above and below which 50% of the individual values lie. The upper limit of the box is the upper or third quartile, the value above which 25% and below which 75% of fhe individual values lie. Finally, the lower limit of the box is the lower or first quartile, the values above which 75% and below which 25% of individual values lie. For descriptive purposes this graphical presentation is very informative in giving information about the distribution of the data. 

An important prerequisite for the use of descriptive statistics is the shape of the distribution of variables, that is, the frequency of values from different ranges of the variable. It is assumed in multiple regression analysis that the residuals — predicted minus observed values — are normally distributed. 

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