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T-Test with Two Samples of Different Variances

T-Test with Two Samples of Different Variances
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2013-12-29 04:52
FunctionProbability & Statistics
This example demonstrates a T-test for two samples with different variances. Key mathematical concepts include hypothesis testing, T-distribution, and degrees of freedom. Using the GeoGebra commands from the construction summary, one can observe how to calculate sample means, standard errors, T-values, and P-values. Suitable for statistics and probability courses.

GeoGebra Commands & Parameters

  1. TDistribution(df, x) → T
  2. InverseTDistribution(df, If[tailn ≠ 1, alfa / abs(tailn), 1.0E-5]) → tcL
  3. InverseTDistribution(df, If[tailn > 0, 1 - alfa / tailn, 0.99999]) → tcR
  4. Integral(T, tcL, tcR) → c
  5. TDistribution(df, x, true) → p

AI Drawing Prompt Reference

Create a T-test graph for two samples with different variances. First, calculate the means of the two samples, then compute the standard errors, followed by the T-values and P-values, and finally plot the T-distribution curve, marking the area corresponding to the P-value for the T-values.
The prompt above was compiled from the original drawing commands and is for reference only.