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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.
The Null Hypothesis - H0: There is no heteroscedasticity i.e. β 1 = 0 The Alternative Hypothesis - H1: There is heteroscedasticity i.e. β 1 0 Reject H0 if Q = ESS/2 >
Historical controls : The group of patients treated in the past with the standard therapy, taken in use as the control group for evaluating the new treatment on the present patient
The model for data containing continuous and categorical variables both.The categorical data are summarized by the contingency table and their marginal distribution, 182by the mult
Window variables are the variables measured during the constrained interval of an observation period which is accepted as the proxies for the information over the whole period. Fo
Behrens Fisher problem : The difficulty of testing for the equality of the means of the two normal distributions which do not have the equal variance. Various test statistics have
The method or technique for displaying the relationships between categorical variables in a type of the scatter plot diagram. For two this type of variables displayed in the form o
Homoscedasticity - Reasons for Screening Data Homoscedasticity is the assumption that the variability in scores for a continuous variable is roughly the same at all values of
Matching distribution is a probability distribution which arises in the following manner. Suppose that the set of n subjects, numbered 1; . . . ; n respectively, are arranged in
The Null Hypothesis - H0: γ 1 = γ 2 = ... = 0 i.e. there is no heteroscedasticity in the model The Alternative Hypothesis - H1: at least one of the γ i 's are not equal
PRINCIPLES OF MODELLING IN OR.
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