C++ code implementing the Central Limit Theorem for calculating mean, standard deviation, and sample mean, generating random samples, and calculating Z-values.
This C++ program demonstrates the Central Limit Theorem by calculating mean, standard deviation, sample mean, and Z-values. It also generates random samples from a dataset and saves the calculated values in output files.
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Data File: Make sure you have a data file named "EmployeeData.csv" in the same directory as the program. The file should contain the dataset from which random samples will be extracted.
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Libraries: The program requires the following libraries:
<iostream>,<fstream>,<vector>,<string>,<cmath>,<cstdlib>, and<time.h>. Ensure that these libraries are available in your C++ environment. -
Compile: Compile the program using a C++ compiler. For example, using g++ in the command line:
g++ central_limit_theorem.cpp -o central_limit_theorem -
Run: Execute the compiled program:
./central_limit_theorem -
Output Files: The program will generate two output files:
- "Output10.txt": Contains the Z-values calculated for sample size 10. Each value is listed on a separate line.
- "test.txt": Contains the Z-values calculated for sample size 300. Each value is listed on a separate line.
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Review Results: Open the output files to review the calculated Z-values.
Note: The program assumes that the data file is in the correct format and the dataset contains at least 1000 entries.
- The program uses random number generation to extract samples from the dataset.
- The Central Limit Theorem states that as the sample size increases, the distribution of sample means approaches a normal distribution, regardless of the shape of the original population.
- The calculated Z-values represent the standardized deviation of the sample mean from the population mean, taking into account the sample size and standard deviation.
Important: Ensure that you have a proper understanding of the Central Limit Theorem and the code before using it or making any modifications.