1 / 30

Analysis of Variance 2-Way ANOVA

Analysis of Variance 2-Way ANOVA. MARE 250 Dr. Jason Turner. Two-Way – ANOVA. Two-way ANOVA - procedure to test the equality of population means when there are two factors 2-Sample T-Test (1R, 1F, 2 Levels) One-Way ANOVA (1R, 1F, >2 Levels) Two-Way ANOVA (1R, 2F, >1 Level).

samuel-strickland
Télécharger la présentation

Analysis of Variance 2-Way ANOVA

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Analysis of Variance 2-Way ANOVA MARE 250 Dr. Jason Turner

  2. Two-Way – ANOVA Two-way ANOVA - procedure to test the equality of population means when there are two factors 2-Sample T-Test(1R, 1F, 2 Levels) One-Way ANOVA(1R, 1F, >2 Levels) Two-Way ANOVA (1R, 2F, >1 Level)

  3. Two-Way – ANOVA For Example… One-Way ANOVA– means of urchin #’s from each distance (shallow, middle, deep) are equal Response – urchin #, Factor – distance Two-Way ANOVA– means of urchin’s from each distance collected with each quadrat (¼m, ½m) are equal Response – urchin #, Factors – distance, quadrat

  4. Two-Way – ANOVA Factor 1 Location (S, M, D) Factor 2 Quad Size (¼m, ½m) Deep Intermed. Shallow SeaWall

  5. Two-Way – ANOVA Factor 1 Location (S, M, D) Factor 2 Quad Size (¼m, ½m) Deep Intermed. Shallow SeaWall

  6. Two-Way – ANOVA Factor 1 Location (S, M, D) Factor 2 Quad Size (¼m, ½m) Deep Intermed. Shallow INTERACTION Factor 1 X Factor 2 Location X Quad Size SeaWall

  7. Two-Way – ANOVA Results If the effect of a fixed factor is significant, then the level means for that factor are significantly different from each other (just like a one-way ANOVA) If the effect of an interaction term is significant, then the effects of each factor are different at different levels of the other factor(s)

  8. Two-Way – ANOVA Results

  9. Two-Way – ANOVA Results Urchins Location Quad Size

  10. Two-Way – ANOVA Results Two-Way ANOVA : Analysis of Variance Table Source DF SS MS F P Location 1 228.17 228.167 8.99 0.008 Quadsize 2 308.33 154.167 6.07 0.010 Interaction 2 76.33 38.167 1.50 0.249 Error 18 457.00 25.389 Total 23 1069.83

  11. For the urchin analysis, the results indicate the following: The effect of Location (p = 0.008) is significant This indicates that urchin populations numbers were significantly different a different distances from shore The effect of Quad Size (p = 0.010) is significant This indicates quadrat type had a significant effect upon the number of urchins collected The interaction between Distance and Quadrat (p = 0.249) is not significant This means that the distance and quadrat size results were not influencing the other Thus, it is okay to interpret the individual effects of either factor alone

  12. Two-Way – ANOVA Results Two-Way ANOVA : Analysis of Variance Table Source DF SS MS F P Location 1 228.17 228.167 8.99 0.008 Quadsize 2 308.33 154.167 6.07 0.010 Interaction 2 76.33 38.167 1.50 0.009 Error 18 457.00 25.389 Total 23 1069.83

  13. For the urchin analysis, the results indicate the following: The effect of Location (p = 0.008) is significant This indicates that urchin populations numbers were significantly different a different distances from shore The effect of Quad Size (p = 0.010) is significant This indicates quadrat type had a significant effect upon the number of urchins collected The interaction between Distance and Quadrat (p = 0.009) is not significant This means that the distance and quadrat size results WERE INFLUENCING the other Thus, the individual Factors must be analyzed alone

  14. Interactions Use interactions plots to assess the two-factor interactions in a design Evaluate the lines to determine if there is an interaction: If the lines are parallel, there is no interaction If the lines cross, there IS Interaction The greater the lines depart from being parallel, the greater the degree of interaction

  15. Interactions Plots

  16. Interactions Plots

  17. Interactions Plots Why is there interaction? Because we get a different answer regarding #Urchins by Location (S,M,D) when using different Quadrats (¼m, ½m)

  18. Interactions Plots Why is there interaction? Because we get a different answer regarding #Urchins by Quad Size (¼m, ½m) at different Locations (S,M,D)

  19. Two-Way – ANOVA The two-way ANOVA procedure does not support multiple comparisons To compare means using multiple comparisons, or if your data are unbalanced – use a General Linear Model General Linear Model - means of urchin #’s and species #’s from each distance (shallow, middle, deep) are equal Responses – urchin #, Factor – distance, quadrat Unbalanced…No Problem! Or multiple factors… General Linear Model - means of urchin #’s and species #’s from each distance (shallow, middle, deep) are equal Responses – urchin #, Factor – distance, quadrat, transect

  20. Two-Way – ANOVA Two-Way ANOVA is a statistical test – there is a parametric (Two-Way ANOVA) and nonparametric version (Friedman’s) There are 3 ways to run a Two-Way ANOVA in minitab: 1) Two-Way ANOVA – for parametric (normal) balanced (equal n among levels) data 2) General Linear Model (GLM) – for all parametric (normal) data – balanced or not 3) Friedman – nonparametric (not normal) data

  21. Two-Way – ANOVA 1) Two-Way ANOVA – for parametric (normal) balanced (equal n among levels) data - See examples of Two-Way ANOVA above * Note – Two-Way ANOVA program cannot run Multiple Comparisons Tests (Tukey)

  22. Two-Way – ANOVA 2) General Linear Model (GLM) – for all parametric (normal) data – balanced or not

  23. Two-Way – ANOVA 2) General Linear Model (GLM) – for all parametric (normal) data – balanced or not

  24. Two-Way – ANOVA 2) General Linear Model (GLM) – for all parametric (normal) data – balanced or not

  25. Two-Way – ANOVA 2) General Linear Model (GLM) – for all parametric (normal) data – balanced or not

  26. Two-Way – ANOVA 2) General Linear Model (GLM) – for all parametric (normal) data – balanced or not

  27. Two-Way – ANOVA 2) General Linear Model (GLM) – for all parametric (normal) data – balanced or not Location Quad Size

  28. Two-Way – ANOVA 2) General Linear Model (GLM) – for all parametric (normal) data – balanced or not Location*Quad Size

  29. Two-Way – ANOVA 3) Friedman – nonparametric (not normal) data

  30. Two-Way – ANOVA 3) Friedman – nonparametric (not normal) data

More Related