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Input to the compress is a text le with arbitrary size, but for this assignment we will assume that the data structure of the file fits in the main memory of a computer. Output of the program is a compressed representation of the original file. You will have to save the codetable in the header of the compressed file, so that you can use the codetable for decompressing the compressed file. Input to the decompress is a compressed file, from which the program recovers the original file. For sanity check, you should have a specific magic word at some position in the header of the compressed file, so that decompress can identify whether the given file is a valid Huffman compressed file. You should pay attention to the following issues:
The file that we will use for testing can be very large, having size in Gigabytes, so make sure that your program is bug-free and it works for large input le.
Write efficient algorithm, we will take off as much as 20 points if we feel that the program is taking unusually long time.
You must make sure that your program runs on a Linux Machine, and identically follows the formatting instructions. For formatting error, as much as 15 points can be taken off .
You must provide a Make file to compile your programs. Also, a README.txt le should be provided that will have the instruction to compile and run the programs.
Multivariate data is the data for which each observation consists of the values for more than one random variable. For instance, measurements on the blood pressure, temperature an
Missing Data - Reasons for screening data In case of any missing data, the researcher needs to conduct tests to ascertain that the pattern of these missing cases is random.
Formal graphical representation of the "causal diagrams" or the "path diagrams" where the relationships are directed but acyclic (that is no feedback relations allowed). Plays an
calculate the mean yearly value using the average unemployment rate by month
Mention the characteristics of Statistics. Explain any two applications of Statistics.
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
#explanation of methods of collection of data..
The problematic and enigmatic theory of an inference introduced by the Fisher, which extracts a probability distribution for the parameter on the basis of the data without having f
Uncertainty analysis is the process for assessing the variability in the outcome variable that is due to the uncertainty in estimating the values of input parameters. A sensitivit
Regression dilution is the term which is applied when a covariate in the model cannot be measured directly and instead of that a related observed value must be used in analysis. I
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