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to create void space. Recent technology advances are expected to improve the viability of oil shale technology, leading to commercialization. Of importance in the in situ processing option is the characteristic that oil shale is relatively hard impermeable rocks through which fluids will not flow. Also of interest is the specific gravity of the oil shale since much of the inorganic material must be disposed. The specific gravity of the Green River kerogen is approximately 1.05 and the mineral fraction has an approximate value of 2.7 (Baughman, 1978). In situ processes can be technically feasible where permeability of the rock exists or can be created through fracturing. The target deposit is fractured, air is injected, the deposit is ignited to heat the formation, and resulting shale oil is moved through the natural or man-made fractures to production wells that transport it to the surface (Fig. 6.2). However, difficulties in controlling the flame front and the flow of pyrolized oil can limit the ultimate oil recovery, leaving portions of the deposit unheated and portions of the pyrolized oil unrecovered.

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There are many different types of mobile computers and devices, each with different features, functions, and purposes. The majority of devices are broken into approximately five to seven categories. The categories and some major hardware vendors offering products are listed below. Laptop (Dell, IBM, Toshiba) Rugged laptop (Itronix, Panasonic) Clamshell Handheld (Hewlett Packard) Rugged handheld (Symbol, Intermec/Norand) Pocket PC or Personal Digital Assistant (Palm, Compaq, Casio, HP) Two-way Pagers (Research in Motion, Motorola) Smart Phones (Nokia, Ericsson, Motorola)

new is that now we re able to understand in more detail why these disciplines work. In my opinion, you re always better off understanding why something works, rather than just applying the process blindly. It improves both your confidence and your judgment within the framework of the discipline.

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As we proceed in statistics, our interest turns to estimating unknown population values We have previously described a statistic as a value that describes a sample and a parameter as a value that describes a population Now we want use a statistic as an estimate of a parameter We know that if we draw multiple samples and compute some statistic of interest, say x , that we will likely get different values each time even though the samples are all drawn from a population with a single mean, What we now do is to develop a process by which we will use our estimate to generate a range of likely population values for the parameter The statistic itself is called a point estimate, and the range of likely population values from which we might have obtained our estimate is called a confidence interval.

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example: We do a sample survey and find that 42% of the sample plans to vote for Normajean for student body treasurer That is, p = 042 Based on this, we generate an interval of likely values (the confidence interval) for the true proportion of students who will vote for Normajean and find that between 38% and 46% of the students are likely to vote for Normajean The interval (038, 046) is a confidence interval for the true proportion who will vote for Normajean Note that saying a confidence interval is likely to contain the true population value is not to say that is necessarily does It may or may not we will see ways to quantify just how confident we are in our interval.

In this chapter, we will construct confidence intervals for a single mean, the difference between two means, a single proportion, and the difference between two proportions Our ability to construct confidence intervals depends on our understanding of the sampling distributions for each of the parameters In 8, we discussed the concept of sampling distribution for sample means and sample proportions Similar arguments exist for the sampling distributions of the difference between two means or the difference between two proportions..

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