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Command-Line options
Compression:
C++: ./compress -f myfile.txt [-o myfile.hzip -s
Java: sh compress.sh -f myfile.txt [-o myfile.hzip -s]
Decompression:
C++: ./decompress -f myfile.hzip[-r -v]
Java: sh decompress.sh -f myfile.hzip [-o myfile.txt -s]
The command-line options that are within the square bracket are optional. The option \-f" precedes the input le name, which always has a .txt extension. The \-s" option prints statistics, such as for compression it prints, how many distinct characters are there, what is the compression ratio, and the wall clock time that it took for performing the compression task. For decompression, it prints how many character were written, and the wall clock time it took for performing the decompression task. The \-o" option precedes the name of an output le. If the output file name is not given, then we will append .hzip at the end of the input filename to create the output filename.
Morbidity is the term used in the epidemiological studies to describe sickness in the human populations. The WHO Expert Committee on the Health Statistics noted in its sixth repor
An approach to decrease the size of very large data sets in which the data are first 'binned' and then statistics such as the mean and variance/covariance are calculated on each bi
Common cause failures (CCF): Simultaneous failures of the number of components due to a same reason. A reason can be external to the components, or it can be the single failure wh
VIF is the abbreviation of variance inflation factor which is a measure of the amount of multicollinearity that exists in a set of multiple regression variables. *The VIF value
The function of a variable t which, when extended formally as a power series in t, yields factorial moments as the coefficients of the respective powers. If the P(t) is probability
Gllamm is a program which estimates the generalized linear latent and mixed models by the maximum likelihood. The models which can be fitted include structural equation models mul
Bayes factor : A summary of evidence for the modelM1 against the another modelM0 provided by the set of data D, which can be used in the model selection. Given by the ratio of post
(a) You are trying to develop a strategy for investing in two different stocks, Stock A and Stock B. The anticipated annual return for a $1000 investment in each stock under four
A vague concept which occurs all through statistics. Essentially the term means the number of independent units of the information in an easy relevant to the estimation of the para
hello I have a dataset including both categorical & numerical variable for market segmentation.how can i cluster them via k-means in matlab? thank you
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