Calculates the degrees of freedom (df) for various statistical tests, representing the number of independent pieces of information available to estimate a parameter.
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Enter Sample Size (n), Number of Groups (k), Number of Independent Variables, Number of Rows, and Number of Columns in the units shown.
Read Degrees of Freedom (Chi-Square), Degrees of Freedom (Regression), Degrees of Freedom (ANOVA): Within Groups, Degrees of Freedom (ANOVA): Between Groups, Degrees of Freedom (t-test): Independent Samples, and Degrees of Freedom (t-test): One Sample or Paired Samples in the results panel. Results update as you change the inputs.
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See the inputs and their calculated results.
Sample Size (n): 30; Number of Groups (k): 2; Number of Independent Variables: 1; Number of Rows: 2; Number of Columns: 2
Degrees of Freedom (Chi-Square): 1; Degrees of Freedom (Regression): 1; Degrees of Freedom (ANOVA): Within Groups: 28; Degrees of Freedom (ANOVA): Between Groups: 1; Degrees of Freedom (t-test): Independent Samples: 58; Degrees of Freedom (t-test): One Sample or Paired Samples: 29
Sample Size (n): 60; Number of Groups (k): 2; Number of Independent Variables: 1; Number of Rows: 2; Number of Columns: 2
Degrees of Freedom (Chi-Square): 1; Degrees of Freedom (Regression): 1; Degrees of Freedom (ANOVA): Within Groups: 58; Degrees of Freedom (ANOVA): Between Groups: 1; Degrees of Freedom (t-test): Independent Samples: 118; Degrees of Freedom (t-test): One Sample or Paired Samples: 59
Sample Size (n): 50; Number of Groups (k): 3; Number of Independent Variables: 1; Number of Rows: 2; Number of Columns: 2
Degrees of Freedom (Chi-Square): 1; Degrees of Freedom (Regression): 1; Degrees of Freedom (ANOVA): Within Groups: 47; Degrees of Freedom (ANOVA): Between Groups: 2; Degrees of Freedom (t-test): Independent Samples: 98; Degrees of Freedom (t-test): One Sample or Paired Samples: 49
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Performs a t-test (one-sample, two-sample, or paired) to compare means and determine statistical significance.
Finds the Z-score corresponding to a given cumulative probability (percentile) on a normal distribution.
Calculates the Chi-Square statistic and p-value for goodness-of-fit or test of independence. It requires observed and expected frequencies or values.
Identifies potential outliers in a dataset using the IQR * 1.5 rule (calculating fences).