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parsaloiparsaloi
committedAug 2, 2024
parsa composed ppmcc:v1 and ppmcc:v2
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‎.gitignore

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# the clean.py script
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clean.py
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## Julia stuff
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*.jl.cov
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*.jl.*.cov
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*.jl.mem
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deps/deps.jl
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# Byte-compiled / optimized / DLL files
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__pycache__/

‎correlation-analysis-techniques/PPMCC/Manifest.toml

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‎correlation-analysis-techniques/PPMCC/Project.toml

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‎correlation-analysis-techniques/PPMCC/src/PPMCC.jl

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‎correlation-analysis-techniques/ppmcc/v1/ppmcc/Project.toml

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name = "ppmcc"
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uuid = "64e3ba1c-fcbe-4a0c-824e-8c0982845989"
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authors = ["parsaizme "]
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version = "0.1.0"

‎correlation-analysis-techniques/ppmcc/v1/ppmcc/src/ppmcc.jl

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using Statistics
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function analyze_concrete_data()
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# Data
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temperature = [15, 16, 18, 22, 14, 25, 17, 28, 30, 26]
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days = [21, 20, 18, 16, 24, 15, 18, 14, 12, 14]
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# (i) Compute Pearson's product moment correlation coefficient
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mean_temp = mean(temperature)
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mean_days = mean(days)
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numerator = sum((temperature .- mean_temp) .* (days .- mean_days))
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denominator = sqrt(sum((temperature .- mean_temp).^2) * sum((days .- mean_days).^2))
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r = numerator / denominator
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println("(i) Pearson's correlation coefficient r = ", round(r, digits=4))
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if r < -0.8
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println("There is a strong negative correlation between temperature and drying time.")
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elseif r < -0.5
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println("There is a moderate negative correlation between temperature and drying time.")
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elseif r < 0
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println("There is a weak negative correlation between temperature and drying time.")
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elseif r == 0
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println("There is no linear correlation between temperature and drying time.")
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elseif r <= 0.5
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println("There is a weak positive correlation between temperature and drying time.")
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elseif r <= 0.8
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println("There is a moderate positive correlation between temperature and drying time.")
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else
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println("There is a strong positive correlation between temperature and drying time.")
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end
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# (ii) Calculate the regression equation of time on temperature
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b = r * (std(days) / std(temperature))
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a = mean_days - b * mean_temp
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println("\n(ii) Regression equation:")
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println("Time = ", round(a, digits=2), " + (", round(b, digits=2), " * Temperature)")
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# (iii) Estimate drying time for temperature of 23°C
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estimated_time = a + b * 23
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println("\n(iii) Estimated drying time for 23°C:")
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println(round(estimated_time, digits=2), " days")
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end
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# Run the analysis
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analyze_concrete_data()

‎correlation-analysis-techniques/ppmcc/v2/ppmcc/Manifest.toml

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# This file is machine-generated - editing it directly is not advised
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‎correlation-analysis-techniques/ppmcc/v2/ppmcc/Project.toml

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name = "ppmcc"
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uuid = "b0920459-b932-4b04-83ee-fb3fa13b1737"
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authors = ["parsaizme "]
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version = "0.1.0"
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[deps]
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Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"

‎correlation-analysis-techniques/ppmcc/v2/ppmcc/bin/run_app.jl

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using ppmcc
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function main()
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temperature = [15, 16, 18, 22, 14, 25, 17, 28, 30, 26]
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days = [21, 20, 18, 16, 24, 15, 18, 14, 12, 14]
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r, (a, b), estimated_time = analyze_concrete_data(temperature, days)
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println("Pearson's correlation coefficient r = ", round(r, digits=4))
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println("Regression equation: Time = $(round(a, digits=2)) + ($(round(b, digits=2)) * Temperature)")
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println("Estimated drying time for 23°C: $(round(estimated_time, digits=2)) days")
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end
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main()

‎correlation-analysis-techniques/ppmcc/v2/ppmcc/src/ppmcc.jl

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module ppmcc
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using Statistics
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export analyze_concrete_data
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function analyze_concrete_data(temperature, days)
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# Correlation coefficient calculation
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r = cor(temperature, days)
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# Regression equation
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b = r * (std(days) / std(temperature))
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a = mean(days) - b * mean(temperature)
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# Estimation for 23°C
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estimated_time = a + b * 23
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return (r, (a, b), estimated_time)
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end
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end # module ppmcc

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