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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.
Prevalence : The measure of the number of people in a population who have a certain disease at a given point in time. It c an be measured by two methods, as point prevalence and p
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A comprehensive regression analysis of the case study London has been carried out to test the 4 assumptions of regression: 1. Variables are normally distributed 2. Linear rel
Bonferroni correction : A procedure for guarding against the rise in the probability of a type I error when performing the multiple signi?cance tests. To maintain probability of a
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The diagnostic tools or devices used to approach the closeness to the linearity of the non-linear model. They calculate the deviation of so-called expectation surface from the plan
The Null Hypothesis - H0: There is no first order autocorrelation The Alternative Hypothesis - H1: There is first order autocorrelation Durbin-Watson statistic = 1.98307
Likelihood is the probability of a set of observations provided the value of some parameter or the set of parameters. For instance, the likelihood of the random sample of n observ
What is statistical inference? Statistical inference can be defined as the method of drawing conclusions from data which are subject to random variations. This is based o
Multiple imputation : The Monte Carlo technique in which missing values in the data set are replaced by m> 1 simulated versions, where m is usually small (say 3-10). Each of simula
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