/test-statistics

Examples of using the test statistics to test hypotheses

Primary LanguagePythonMIT LicenseMIT

Test Statistics

(The test numbering matches the numbering in the book "100 Statistical Tests 3rd Edition" by Gopal K Kanji)

Z-test for a population mean (variance known)

  • Sample Size n=5
  • Sample Size n=20
  • Sample Size n=100

Z-test for two population means (variances known and unequal)

  • μ1 = μ2 = 10, σ1 = σ2 = 4
    • n1=10, n2=5
    • n1=20, n2=40
    • n1=200, n2=100
  • μ1 = 15, μ2 = 10, σ1 = 2, σ2 = 4
    • n1=10, n2=5
    • n1=20, n2=40
    • n1=200, n2=100

Z-test for a proportion (binomial distribution)

  • Sample Size n=40
  • Sample Size n=200
  • Sample Size n=1000

Z-test for the equality of two proportions (binomial distribution)

  • n1=40, n2=100
  • n1=200, n2=400
  • n1=1000, n2=500

t-test for a population mean (variance unknown)

  • Sample Size n=5
  • Sample Size n=20
  • Sample Size n=100

t-test for two population means (variances unknown but equal)

  • μ1 = μ2 = 10, σ1 = σ2 = 3
    • n1=10, n2=5
    • n1=20, n2=40
    • n1=200, n2=100
  • μ1 = 12, μ2 = 10, σ1 = σ2 = 3
    • n1=10, n2=5
    • n1=20, n2=40
    • n1=200, n2=100

t-test for two population means (variances unknown and unequal)

  • μ1 = μ2 = 10, σ1 = 2, σ2 = 4
    • n1=10, n2=5
    • n1=20, n2=40
    • n1=200, n2=100
  • μ1 = 20, μ2 = 15, σ1 = 2, σ2 = 4
    • n1=10, n2=5
    • n1=20, n2=40
    • n1=200, n2=100

χ2-test for a population variance

  • Sample Size n=5
  • Sample Size n=20
  • Sample Size n=100

F-test for two population variances (variance ratio test)

  • μ1 = 10, μ2 = 15, σ1 = σ2 = 2
    • n1=10, n2=5
    • n1=20, n2=40
    • n1=200, n2=100

F-test for K population means (analysis of variance)

  • μ1 = μ2 = μ3 = μ4 = 10

    • n1=5, n2=10, n3=15, n4=20
    • n1=50, n2=100, n3=150, n4=200

  • μ1 = 25, μ2 = 20, μ3 = 15, μ4 = 10

    • n1=5, n2=10, n3=15, n4=20

License

This project is available under the MIT license © Nail Sharipov