Statistics data types

The NERC GADS ten-year review report for 1971-1980 on equipment availability presents statistical data sets on the performance of major types of electrical power generating units. Cumulative and unit.-year averages are calculated on such quantities as service hours, available hours, scheduled outage rate, mean time between full forced outages, shutdown because of economic reasons, and probability of outage. The number of start demands and successful starts are included. 

In the beginning, before there is analysis, there must be accurate description. How much cancer is there, and how do rates of occurrence vary geographically, and between sexes, and with age How do rates of different types of cancer vary over time, and what happens to the rates that occur in specific groups of people when they move from one geographic location to another Information describing these types of differences and trends - which can be compiled with accuracy only when cancer registry information is reliable - are enormously beneficial in providing clues to the causes of cancer. The statistical data presented in Chapter 5 arose from these types of studies. 

Information on death rates from automobile or other types of accidents or activities is generally much more solid than that pertaining to most chemical risks. Statistical data, compiled by actuaries, are used to derive such risk information. There is uncertainty associated with these actuarial figures, but most are fairly reliable. Most of the risk information about various cancers, presented in Chapter 5, is of this type. 

Note that some of the risk information is actuarial (based on statistical data, typically collected and organized by insurance companies), and some of it has been derived from the type of risk assessment discussed in this book (chloroform in chlorinated drinking water, afla-toxin in peanut products). While the uncertainties associated with the figures in Table 11.2 are much greater for some risks than for others (not a trivial problem in presentation of risk data), such a presentation, it would seem, is helpful to people who are trying to acquire some understanding of extremely low probability events, of the order of one-in-one million. 

As we shall see later the data type to a large extent determines the class of statistical tests that we undertake. Commonly for continuous data we use the t-tests and their extensions analysis of variance and analysis of covariance. For binary, categorical and ordinal data we use the class of chi-square tests (Pearson chi-square for categorical data and the Mantel-Haenszel chi-square for ordinal data) and their extension, logistic regression. 

Calculate the value of the test statistic (usually = signal/noise). The formula for the test statistic will be based on a standard approach determined by the data type, the design of the trial (between- or within-patient) and the hypotheses of interest. Mathematics has provided us with optimum procedures for all the common (and not so common) situations and we will see numerous examples in subsequent sections. 

Chapter 3 together with testing hypotheses and the (dreaded ) p-value. Common statistical tests for various data types are developed in Chapter 4 which also covers different ways of measuring treatment effect for binary data, such as the odds ratio and relative risk. 

Isophorone also has been detected in urban runoff from Washington, DC (Cole et al. 1984). It has been detected in water (unspecified type) at 13 of 357 hazardous waste sites as shown in the contract laboratory statistical data base (1-538 ppb). 

For our validations, a CVp is a pooled estimate calculated from the particular type of statistical data set (36 samples) described earlier in the Statistical Experimental Design section of this report. A statistical procedure is given in Hald JL for determining an upper confidence limit for the coefficient of variation. This general theory had o be adapted appropriately for application to a pooled CVp estimate. For this design, and under the stated assumptions, there is a one-to-one correspondence between values of CVp and upper confidence limits for CVp. Therefore, the confidence limit criterion given above is equivalent to another criterion based on the relationship of CVp and its critical value. The... 

When the EPA considered exposures to insecticide residues in the home they identified at least six possible sources and routes these are given in Table 2.6. Their original approach apportioned the acceptable daily intake (ADI) between the various routes but it soon became clear that this was unrealistic because an individual was unlikely to be exposed via all routes on any one day. The EPA s present strategy is to develop an approach called micro-exposure event modelling. Micro-exposure event modelling is based on statistical data on the frequencies and levels of contamination of food, water, etc. and on behavioural information about the frequency of use of lawn/pet/timber treatments, etc. The combined data are assembled in a probabilistic model called LIFELINE which is able to predict the frequency and level of exposure to a group of hypothetical individuals over their lifetime.12 The model is also able to take account of the relative proportions of different types of accommodation, the incidence of pet ownership or any other data that will affect real levels of exposure. The output from the LIFELINE model allows the exposures of individuals in a population to be modelled over any interval from a single occasion to a lifetime. 

Based on the sample data, we may reject the null hypothesis when in fact it is true, and consequently accept the alternative hypothesis. By failing to recognize a true state and rejecting it in favor of a false state, we will make a decision error called a false rejection decision error. It is also called a false positive error, or in statistical terms, Type I decision error. The measure of the size of this error or the probability is named alpha (a). The probability of making a correct decision (accepting the null hypothesis when it is true) is then equal to 1—a. For environmental projects, a is usually selected in the range of 0.05-0.20. 

Models use mathematical expressions to quantify the processes leading to exposure and dose. Models that predict dispersion, fate, transport, and transfer of chemicals are based on physical and chemical principles. Models that describe activities of individuals as they interact with the environment are based on statistical data from observational measurement studies. In Figure 13, the processes that must be accounted for from source to dose are described the text above the orange boxes shows the types of models that can be used to quantify these processes. These models can be applied to predict exposure and dose for an individual however, they are most effectively applied at the population level (IPCS, 2005). 

The authors and editor wish to thank the staff of the Wine Institute of San Francisco for their assistance in securing statistical data and illustrations, for preparing the figures and slides, and especially for typing of the manuscript. 

The weaving of the yarn was conducted on a Draper model X P loom operating at 160 picks per minute. A 115 g/ni (3.4 oz/ydZ) printdoth fabric was produced with approximately 68 ends by 70 picks. The statistical data accumulated during the weaving opera-ation of the three types of yarn treatments are presented In Table VI. The average number of breaks per hour In warp yarn for starch, fermented starch, and enzyme-degraded starch was 1.62, 1.37, and 1.45, respectively. 

Statistics refers to the scientific methods applied to the collection, organization, interpretation, and presentation of information—numerical data. For statistical process control (SPC), data types are divided into attributes or variables. 

For statistical samples of small volume, an increase in the order of the polynomial regression of variables can produce a serious increase in the residual variance. We can reduce the number of the coefficients from the model but then we must introduce a transcendental regression relationship for the variables of the process. From the general theory of statistical process modelling (relations (5.1)-(5.9)) we can claim that the use of these types of relationships between dependent and independent process variables is possible. However, when using these relationships between the variables of the process, it is important to obtain an excellent ensemble of statistical data (i.e. with small residual and relative variances). 

The definitions and statistical theory of PPK, advantages, and disadvantages of PPK have been discussed in this chapter. Models, data type, methods, and software programs for estimating population pharmacokinetic parameters, design, and analysis of population pharmacokinetic studies have been reviewed, as well as its application in biopharmaceutics. The use of population methods continues to increase while there is a shortage of those who can implement the approach. 

This type of data processing and display is commonly done using computer packages for statistical data evaluations (e.g. [76]). 

Top-down analysis is focused on the research of key relations in the whole national economy and on the calculation of CO2 emissions at the country level with the use of models. The starting point for this type of analysis is a set of statistical data regarding macroeconomic quantities, fuel consumption and CO2 emissions. The results focus on quantities describing changes in GDP, energy consumption and CO2 emissions as the country total and in sectoral division. 

In some cases, where it was necessary to obtain statistical data of a type not collected by old-line agencies, the NPA Chemical Division collected the data. Typical examples are information concerning plant capacity, proposed future production, raw material requirements, power consumption, and end-use patterns. During the war the capacity and end-use data were not made available pubhcly. 

---