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Origin: Two-Way ANOVA

Analysis of Variance, or ANOVA, is the standard technique for quantifying and partitioning sample variance in experimental data. Origin's ANOVA tools, both One-Way and Two-way, are both powerful and user-friendly.

Two-way analysis of variance experiments have two independent treatment factors each of which has two or more levels. Two-way ANOVA tests for significant differences between the factor level means within a factor and for interactions between the factors.

This tool supports seven means comparison tests:

  • Tukey
  • Bonferroni
  • Dunn-Sidak
  • Fisher LSD
  • Sheffé
  • Holm-Bonferroni
  • Holm-Sidak

You may also use it to test the interaction between two factors. In addition, you can calculate actual power for a specified alpha level and hypothetical power for varying sample sizes.

Note: OriginPro also provides a Two-Way Repeated Measures ANOVA tool.

Origin's Two-Way ANOVA dialog

While performing Two-Way ANOVA in Origin, you can select non-contiguous data sets from any worksheet in the Origin project. When interaction effects are computed, unequal N between cells is supported, but empty cells are not.

Origin's Two-Way ANOVA results

To test new drugs, total chola concentration are measured for 3 different dose levels(100mg, 200mg and 300mg) and 2 different exercise strength (Light and Moderate). Tests are performed for these 3 levels of dose and 2 levels of exercise strength.

From the Two-Way ANOVA results we see that the P-value of Exercise is 0.00769, which is less than 0.05. It indicates that, at the level of 0.05, there is significant difference among 2 levels of exercise strength. Similarly, since the P-value of Dose is less than 0.05, we can see that the means of Dose's 3 different levels are significantly different.

If you want to know which levels have different means, please use means comparison test in Origin.

Origin includes the ability to automatically recalculate the analysis results of the Two Way ANOVA operation any time you change the parameters or update your source data. In addition, the settings for this analysis routine can be saved to an analysis theme for use later with similar data.


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