By David J. Lilja
Measuring desktop functionality units out the elemental thoughts utilized in examining and knowing the functionality of computers. The emphasis is on sensible equipment of dimension, simulation, and analytical modeling. David Lilja discusses functionality metrics and offers unique insurance of the ideas utilized in benchmark courses. as well as intuitive motives of key statistical instruments, he describes the final "design of experiments" strategy and exhibits how the utmost volume of knowledge may be got with minimal attempt. gains comprise appendices directory universal likelihood distributions and statistical tables and a thesaurus of significant technical phrases.
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As a result, they have unpredictable effects on the outcomes of any measurements. While it is impossible to predict the precise effect of specific sources of random errors, it is possible to develop a statistical model to describe their overall effect on the experimental results. This model can then help us to determine how to use appropriate statistical tools to quantify the precision of our measurements. Chapter 4: Errors in Experimental Measurements 35 36 Chapter 4: Errors in Experimental Measurements Experimental errors are typically assumed to be Gaussian.
95. 5%. 035. 5% interval. 21) with the corresponding value from the t table since only n=8 measurements were used to compute the sample mean and the sample standard deviation. 3 Confidence intervals for proportions Oftentimes in analyzing the performance of computer systems we are interested in discrete values, such as a 40 Chapter 4: Errors in Experimental Measurements Chapter 4: Errors in Experimental Measurements 41 count of the number of times several different events occur. We then may want to find the fraction of the time each event occurs.
If the measuring process itself cannot ensure that the underlying error distribution is Gaussian, then the data must be normalized. This normalization can be accomplished by finding the arithmetic mean of four or more randomly selected measurements. The central-limit theorem then assures us that these averaged values follow a Gaussian distribution, so that the confidence intervals apply to the overall mean of these average values. Similarly, if it is impossible to measure the event of interest directly, then you can measure the mean of the event several times and calculate a confidence interval for the overall mean.
Measuring Computer Performance by David J. Lilja